diff --git a/MindSPONGE/README.md b/MindSPONGE/README.md
index 3baba22227a222af088ac8b67bef843da0d1df45..d52b2f8676a94920c7dd00b5847bfaa596f4a2eb 100644
--- a/MindSPONGE/README.md
+++ b/MindSPONGE/README.md
@@ -33,7 +33,7 @@ MindSPONGE(Simulation Package tOwards Next GEneration molecular modelling)是基
 
 ## **最新消息**
 
-- 2022.07.18 论文"SPONGE: A GPU-Accelerated Molecular Dynamics Package with Enhanced Sampling and AI-Driven Algorithms"发表于期刊Chinese Journal of Chemistry。详情参见[论文](https://onlinelibrary.wiley.com/doi/epdf/10.1002/cjoc.202100456)和[代码](https://gitee.com/mindspore/mindscience/tree/dev-md/MindSPONGE/mindsponge/ccsrc/molecular_dynamics)
+- 2022.07.18 论文"SPONGE: A GPU-Accelerated Molecular Dynamics Package with Enhanced Sampling and AI-Driven Algorithms"发表于期刊Chinese Journal of Chemistry。详情参见[论文](https://onlinelibrary.wiley.com/doi/epdf/10.1002/cjoc.202100456)和[代码](https://gitee.com/mindspore/mindscience/tree/master/MindSPONGE/mindsponge/ccsrc/molecular_dynamics)
 - 2022.07.09 MEGA-Assessment在CAMEO-QE月榜取得第一名
 - 2022.06.27 [发布首个百万级开源蛋白质结构训练数据集PSP](https://arxiv.org/pdf/2206.12240v1.pdf)
 - 2022.04.21 [CAMEO竞赛月榜第一](https://www.huawei.com/cn/news/2022/4/mindspore-cameo-protein-ascend)
@@ -120,10 +120,10 @@ md.run(1000, callbacks=[run_info, cb_h5md])
 
 **更多应用案例请见**：
 
-- [蛋白质结构弛豫](https://gitee.com/mindspore/mindscience/tree/dev-md/MindSPONGE/applications/molecular_dynamics/protein_relax/)
-- [蛋白质结构预测 MEGA-Fold](https://gitee.com/mindspore/mindscience/tree/dev-md/MindSPONGE/applications/MEGAProtein/)
-- [蛋白质结构评估 MEGA-Assessment](https://gitee.com/mindspore/mindscience/tree/dev-md/MindSPONGE/applications/MEGAProtein/)
-- [共进化数据引擎 MEGA-EvoGen](https://gitee.com/mindspore/mindscience/tree/dev-md/MindSPONGE/applications/MEGAProtein/)
+- [蛋白质结构松弛](https://gitee.com/mindspore/mindscience/tree/master/MindSPONGE/applications/molecular_dynamics/protein_relax/)
+- [蛋白质结构预测 MEGA-Fold](https://gitee.com/mindspore/mindscience/tree/master/MindSPONGE/applications/MEGAProtein/)
+- [蛋白质结构评估 MEGA-Assessment](https://gitee.com/mindspore/mindscience/tree/master/MindSPONGE/applications/MEGAProtein/)
+- [共进化数据引擎 MEGA-EvoGen](https://gitee.com/mindspore/mindscience/tree/master/MindSPONGE/applications/MEGAProtein/)
 - 基于功能的蛋白设计(TO BE DONE)
 - 基于结构的蛋白设计(TO BE DONE)
 - 蛋白质功能预测(TO BE DONE)
diff --git a/MindSPONGE/build.sh b/MindSPONGE/build.sh
index 8023939456fd93ab6bb68b57539562875c672f16..59f4e27f8e4905be797a5e90080d3d07ea1c1a3a 100644
--- a/MindSPONGE/build.sh
+++ b/MindSPONGE/build.sh
@@ -120,6 +120,7 @@ build_mindsponge()
   fi
   mk_new_dir "${OUTPUT_PATH}"
   cp -r "${BASEPATH}/mindsponge/python/" "${BASEPATH}/build/mindsponge/"
+  cp -r "${BASEPATH}/mindsponge/toolkits/" "${BASEPATH}/build/mindsponge/"
   cp "${BASEPATH}/setup.py" "${BASEPATH}/build/"
   cd "${BASEPATH}/build/"
   echo ${CMAKE_FLAG}
diff --git a/MindSPONGE/mindsponge/python/common/constants.py b/MindSPONGE/mindsponge/python/common/constants.py
deleted file mode 100644
index 3ea9ea0e4e3688474dbc1579a8b6f0dbc5887df5..0000000000000000000000000000000000000000
--- a/MindSPONGE/mindsponge/python/common/constants.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# Copyright 2021 The AIMM Group at Shenzhen Bay Laboratory & Peking University & Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""constant"""
-PI = 3.1415926535
-KB = 0.00198716
-TIME_UNIT = 20.455
-CHARGE_UNIT = 18.2223
-SUPPORT_TYPE = ["AMBER"]
diff --git a/MindSPONGE/mindsponge/python/common/ff14SB_constants.py b/MindSPONGE/mindsponge/python/common/ff14SB_constants.py
deleted file mode 100644
index b0f11fa737870633112abf83fc94668c6f57d4dd..0000000000000000000000000000000000000000
--- a/MindSPONGE/mindsponge/python/common/ff14SB_constants.py
+++ /dev/null
@@ -1,4449 +0,0 @@
-# Copyright 2021 The AIMM Group at Shenzhen Bay Laboratory & Peking University & Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""This file is used to store constant parameters while modeling."""
-import numpy as np
-
-restype_name_to_atom14_names = {
-    'ALA': ['N', 'CA', 'C', 'O', 'CB', '', '', '', '', '', '', '', '', ''],
-    'ARG': ['N', 'CA', 'C', 'O', 'CB', 'CG', 'CD', 'NE', 'CZ', 'NH1', 'NH2', '', '', ''],
-    'ASN': ['N', 'CA', 'C', 'O', 'CB', 'CG', 'OD1', 'ND2', '', '', '', '', '', ''],
-    'ASP': ['N', 'CA', 'C', 'O', 'CB', 'CG', 'OD1', 'OD2', '', '', '', '', '', ''],
-    'CYS': ['N', 'CA', 'C', 'O', 'CB', 'SG', '', '', '', '', '', '', '', ''],
-    'GLN': ['N', 'CA', 'C', 'O', 'CB', 'CG', 'CD', 'OE1', 'NE2', '', '', '', '', ''],
-    'GLU': ['N', 'CA', 'C', 'O', 'CB', 'CG', 'CD', 'OE1', 'OE2', '', '', '', '', ''],
-    'GLY': ['N', 'CA', 'C', 'O', '', '', '', '', '', '', '', '', '', ''],
-    'HIS': ['N', 'CA', 'C', 'O', 'CB', 'CG', 'ND1', 'CD2', 'CE1', 'NE2', '', '', '', ''],
-    'ILE': ['N', 'CA', 'C', 'O', 'CB', 'CG1', 'CG2', 'CD1', '', '', '', '', '', ''],
-    'LEU': ['N', 'CA', 'C', 'O', 'CB', 'CG', 'CD1', 'CD2', '', '', '', '', '', ''],
-    'LYS': ['N', 'CA', 'C', 'O', 'CB', 'CG', 'CD', 'CE', 'NZ', '', '', '', '', ''],
-    'MET': ['N', 'CA', 'C', 'O', 'CB', 'CG', 'SD', 'CE', '', '', '', '', '', ''],
-    'PHE': ['N', 'CA', 'C', 'O', 'CB', 'CG', 'CD1', 'CD2', 'CE1', 'CE2', 'CZ', '', '', ''],
-    'PRO': ['N', 'CA', 'C', 'O', 'CB', 'CG', 'CD', '', '', '', '', '', '', ''],
-    'SER': ['N', 'CA', 'C', 'O', 'CB', 'OG', '', '', '', '', '', '', '', ''],
-    'THR': ['N', 'CA', 'C', 'O', 'CB', 'OG1', 'CG2', '', '', '', '', '', '', ''],
-    'TRP': ['N', 'CA', 'C', 'O', 'CB', 'CG', 'CD1', 'CD2', 'NE1', 'CE2', 'CE3', 'CZ2', 'CZ3', 'CH2'],
-    'TYR': ['N', 'CA', 'C', 'O', 'CB', 'CG', 'CD1', 'CD2', 'CE1', 'CE2', 'CZ', 'OH', '', ''],
-    'VAL': ['N', 'CA', 'C', 'O', 'CB', 'CG1', 'CG2', '', '', '', '', '', '', ''],
-    'UNK': ['', '', '', '', '', '', '', '', '', '', '', '', '', ''],
-
-}
-
-amino_dict = {
-    'VAL': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': '3C',
-        'HB': 'HC',
-        'CG1': 'CT',
-        'HG11': 'HC',
-        'HG12': 'HC',
-        'HG13': 'HC',
-        'CG2': 'CT',
-        'HG21': 'HC',
-        'HG22': 'HC',
-        'HG23': 'HC',
-        'C': 'C',
-        'O': 'O',
-    },
-    'CVAL': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': '3C',
-        'HB': 'HC',
-        'CG1': 'CT',
-        'HG11': 'HC',
-        'HG12': 'HC',
-        'HG13': 'HC',
-        'CG2': 'CT',
-        'HG21': 'HC',
-        'HG22': 'HC',
-        'HG23': 'HC',
-        'C': 'C',
-        'O': 'O2',
-        'OXT': 'O2',
-    },
-    'NVAL': {
-        'N': 'N3',
-        'H1': 'H',
-        'H2': 'H',
-        'H3': 'H',
-        'CA': 'CX',
-        'HA': 'HP',
-        'CB': '3C',
-        'HB': 'HC',
-        'CG1': 'CT',
-        'HG11': 'HC',
-        'HG12': 'HC',
-        'HG13': 'HC',
-        'CG2': 'CT',
-        'HG21': 'HC',
-        'HG22': 'HC',
-        'HG23': 'HC',
-        'C': 'C',
-        'O': 'O',
-    },
-    'GLN': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': '2C',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': '2C',
-        'HG2': 'HC',
-        'HG3': 'HC',
-        'CD': 'C',
-        'OE1': 'O',
-        'NE2': 'N',
-        'HE21': 'H',
-        'HE22': 'H',
-        'C': 'C',
-        'O': 'O',
-    },
-    'CGLN': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': '2C',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': '2C',
-        'HG2': 'HC',
-        'HG3': 'HC',
-        'CD': 'C',
-        'OE1': 'O',
-        'NE2': 'N',
-        'HE21': 'H',
-        'HE22': 'H',
-        'C': 'C',
-        'O': 'O2',
-        'OXT': 'O2',
-    },
-    'NGLN': {
-        'N': 'N3',
-        'H1': 'H',
-        'H2': 'H',
-        'H3': 'H',
-        'CA': 'CX',
-        'HA': 'HP',
-        'CB': '2C',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': '2C',
-        'HG2': 'HC',
-        'HG3': 'HC',
-        'CD': 'C',
-        'OE1': 'O',
-        'NE2': 'N',
-        'HE21': 'H',
-        'HE22': 'H',
-        'C': 'C',
-        'O': 'O',
-    },
-    'PHE': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': 'CT',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'CA',
-        'CD1': 'CA',
-        'HD1': 'HA',
-        'CE1': 'CA',
-        'HE1': 'HA',
-        'CZ': 'CA',
-        'HZ': 'HA',
-        'CE2': 'CA',
-        'HE2': 'HA',
-        'CD2': 'CA',
-        'HD2': 'HA',
-        'C': 'C',
-        'O': 'O',
-    },
-    'CPHE': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': 'CT',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'CA',
-        'CD1': 'CA',
-        'HD1': 'HA',
-        'CE1': 'CA',
-        'HE1': 'HA',
-        'CZ': 'CA',
-        'HZ': 'HA',
-        'CE2': 'CA',
-        'HE2': 'HA',
-        'CD2': 'CA',
-        'HD2': 'HA',
-        'C': 'C',
-        'O': 'O2',
-        'OXT': 'O2',
-    },
-    'NPHE': {
-        'N': 'N3',
-        'H1': 'H',
-        'H2': 'H',
-        'H3': 'H',
-        'CA': 'CX',
-        'HA': 'HP',
-        'CB': 'CT',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'CA',
-        'CD1': 'CA',
-        'HD1': 'HA',
-        'CE1': 'CA',
-        'HE1': 'HA',
-        'CZ': 'CA',
-        'HZ': 'HA',
-        'CE2': 'CA',
-        'HE2': 'HA',
-        'CD2': 'CA',
-        'HD2': 'HA',
-        'C': 'C',
-        'O': 'O',
-    },
-    'ASN': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': '2C',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'C',
-        'OD1': 'O',
-        'ND2': 'N',
-        'HD21': 'H',
-        'HD22': 'H',
-        'C': 'C',
-        'O': 'O',
-    },
-    'CASN': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': '2C',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'C',
-        'OD1': 'O',
-        'ND2': 'N',
-        'HD21': 'H',
-        'HD22': 'H',
-        'C': 'C',
-        'O': 'O2',
-        'OXT': 'O2',
-    },
-    'NASN': {
-        'N': 'N3',
-        'H1': 'H',
-        'H2': 'H',
-        'H3': 'H',
-        'CA': 'CX',
-        'HA': 'HP',
-        'CB': '2C',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'C',
-        'OD1': 'O',
-        'ND2': 'N',
-        'HD21': 'H',
-        'HD22': 'H',
-        'C': 'C',
-        'O': 'O',
-    },
-    'PRO': {
-        'N': 'N',
-        'CD': 'CT',
-        'HD2': 'H1',
-        'HD3': 'H1',
-        'CG': 'CT',
-        'HG2': 'HC',
-        'HG3': 'HC',
-        'CB': 'CT',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CA': 'CX',
-        'HA': 'H1',
-        'C': 'C',
-        'O': 'O',
-    },
-    'CPRO': {
-        'N': 'N',
-        'CD': 'CT',
-        'HD2': 'H1',
-        'HD3': 'H1',
-        'CG': 'CT',
-        'HG2': 'HC',
-        'HG3': 'HC',
-        'CB': 'CT',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CA': 'CX',
-        'HA': 'H1',
-        'C': 'C',
-        'O': 'O2',
-        'OXT': 'O2',
-    },
-    'NPRO': {
-        'N': 'N3',
-        'H2': 'H',
-        'H3': 'H',
-        'CD': 'CT',
-        'HD2': 'HP',
-        'HD3': 'HP',
-        'CG': 'CT',
-        'HG2': 'HC',
-        'HG3': 'HC',
-        'CB': 'CT',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CA': 'CX',
-        'HA': 'HP',
-        'C': 'C',
-        'O': 'O',
-    },
-    'THR': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': '3C',
-        'HB': 'H1',
-        'CG2': 'CT',
-        'HG21': 'HC',
-        'HG22': 'HC',
-        'HG23': 'HC',
-        'OG1': 'OH',
-        'HG1': 'HO',
-        'C': 'C',
-        'O': 'O',
-    },
-    'CTHR': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': '3C',
-        'HB': 'H1',
-        'CG2': 'CT',
-        'HG21': 'HC',
-        'HG22': 'HC',
-        'HG23': 'HC',
-        'OG1': 'OH',
-        'HG1': 'HO',
-        'C': 'C',
-        'O': 'O2',
-        'OXT': 'O2',
-    },
-    'NTHR': {
-        'N': 'N3',
-        'H1': 'H',
-        'H2': 'H',
-        'H3': 'H',
-        'CA': 'CX',
-        'HA': 'HP',
-        'CB': '3C',
-        'HB': 'H1',
-        'CG2': 'CT',
-        'HG21': 'HC',
-        'HG22': 'HC',
-        'HG23': 'HC',
-        'OG1': 'OH',
-        'HG1': 'HO',
-        'C': 'C',
-        'O': 'O',
-    },
-    'ALA': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': 'CT',
-        'HB1': 'HC',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'C': 'C',
-        'O': 'O',
-    },
-    'CALA': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': 'CT',
-        'HB1': 'HC',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'C': 'C',
-        'O': 'O2',
-        'OXT': 'O2',
-    },
-    'NALA': {
-        'N': 'N3',
-        'H1': 'H',
-        'H2': 'H',
-        'H3': 'H',
-        'CA': 'CX',
-        'HA': 'HP',
-        'CB': 'CT',
-        'HB1': 'HC',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'C': 'C',
-        'O': 'O',
-    },
-    'ILE': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': '3C',
-        'HB': 'HC',
-        'CG2': 'CT',
-        'HG21': 'HC',
-        'HG22': 'HC',
-        'HG23': 'HC',
-        'CG1': '2C',
-        'HG12': 'HC',
-        'HG13': 'HC',
-        'CD1': 'CT',
-        'HD11': 'HC',
-        'HD12': 'HC',
-        'HD13': 'HC',
-        'C': 'C',
-        'O': 'O',
-    },
-    'CILE': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': '3C',
-        'HB': 'HC',
-        'CG2': 'CT',
-        'HG21': 'HC',
-        'HG22': 'HC',
-        'HG23': 'HC',
-        'CG1': '2C',
-        'HG12': 'HC',
-        'HG13': 'HC',
-        'CD1': 'CT',
-        'HD11': 'HC',
-        'HD12': 'HC',
-        'HD13': 'HC',
-        'C': 'C',
-        'O': 'O2',
-        'OXT': 'O2',
-    },
-    'NILE': {
-        'N': 'N3',
-        'H1': 'H',
-        'H2': 'H',
-        'H3': 'H',
-        'CA': 'CX',
-        'HA': 'HP',
-        'CB': '3C',
-        'HB': 'HC',
-        'CG2': 'CT',
-        'HG21': 'HC',
-        'HG22': 'HC',
-        'HG23': 'HC',
-        'CG1': '2C',
-        'HG12': 'HC',
-        'HG13': 'HC',
-        'CD1': 'CT',
-        'HD11': 'HC',
-        'HD12': 'HC',
-        'HD13': 'HC',
-        'C': 'C',
-        'O': 'O',
-    },
-    'SER': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': '2C',
-        'HB2': 'H1',
-        'HB3': 'H1',
-        'OG': 'OH',
-        'HG': 'HO',
-        'C': 'C',
-        'O': 'O',
-    },
-    'CSER': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': '2C',
-        'HB2': 'H1',
-        'HB3': 'H1',
-        'OG': 'OH',
-        'HG': 'HO',
-        'C': 'C',
-        'O': 'O2',
-        'OXT': 'O2',
-    },
-    'NSER': {
-        'N': 'N3',
-        'H1': 'H',
-        'H2': 'H',
-        'H3': 'H',
-        'CA': 'CX',
-        'HA': 'HP',
-        'CB': '2C',
-        'HB2': 'H1',
-        'HB3': 'H1',
-        'OG': 'OH',
-        'HG': 'HO',
-        'C': 'C',
-        'O': 'O',
-    },
-    'LEU': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': '2C',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': '3C',
-        'HG': 'HC',
-        'CD1': 'CT',
-        'HD11': 'HC',
-        'HD12': 'HC',
-        'HD13': 'HC',
-        'CD2': 'CT',
-        'HD21': 'HC',
-        'HD22': 'HC',
-        'HD23': 'HC',
-        'C': 'C',
-        'O': 'O',
-    },
-    'CLEU': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': '2C',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': '3C',
-        'HG': 'HC',
-        'CD1': 'CT',
-        'HD11': 'HC',
-        'HD12': 'HC',
-        'HD13': 'HC',
-        'CD2': 'CT',
-        'HD21': 'HC',
-        'HD22': 'HC',
-        'HD23': 'HC',
-        'C': 'C',
-        'O': 'O2',
-        'OXT': 'O2',
-    },
-    'NLEU': {
-        'N': 'N3',
-        'H1': 'H',
-        'H2': 'H',
-        'H3': 'H',
-        'CA': 'CX',
-        'HA': 'HP',
-        'CB': '2C',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': '3C',
-        'HG': 'HC',
-        'CD1': 'CT',
-        'HD11': 'HC',
-        'HD12': 'HC',
-        'HD13': 'HC',
-        'CD2': 'CT',
-        'HD21': 'HC',
-        'HD22': 'HC',
-        'HD23': 'HC',
-        'C': 'C',
-        'O': 'O',
-    },
-    'TRP': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': 'CT',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'C*',
-        'CD1': 'CW',
-        'HD1': 'H4',
-        'NE1': 'NA',
-        'HE1': 'H',
-        'CE2': 'CN',
-        'CZ2': 'CA',
-        'HZ2': 'HA',
-        'CH2': 'CA',
-        'HH2': 'HA',
-        'CZ3': 'CA',
-        'HZ3': 'HA',
-        'CE3': 'CA',
-        'HE3': 'HA',
-        'CD2': 'CB',
-        'C': 'C',
-        'O': 'O',
-    },
-    'CTRP': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': 'CT',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'C*',
-        'CD1': 'CW',
-        'HD1': 'H4',
-        'NE1': 'NA',
-        'HE1': 'H',
-        'CE2': 'CN',
-        'CZ2': 'CA',
-        'HZ2': 'HA',
-        'CH2': 'CA',
-        'HH2': 'HA',
-        'CZ3': 'CA',
-        'HZ3': 'HA',
-        'CE3': 'CA',
-        'HE3': 'HA',
-        'CD2': 'CB',
-        'C': 'C',
-        'O': 'O2',
-        'OXT': 'O2',
-    },
-    'NTRP': {
-        'N': 'N3',
-        'H1': 'H',
-        'H2': 'H',
-        'H3': 'H',
-        'CA': 'CX',
-        'HA': 'HP',
-        'CB': 'CT',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'C*',
-        'CD1': 'CW',
-        'HD1': 'H4',
-        'NE1': 'NA',
-        'HE1': 'H',
-        'CE2': 'CN',
-        'CZ2': 'CA',
-        'HZ2': 'HA',
-        'CH2': 'CA',
-        'HH2': 'HA',
-        'CZ3': 'CA',
-        'HZ3': 'HA',
-        'CE3': 'CA',
-        'HE3': 'HA',
-        'CD2': 'CB',
-        'C': 'C',
-        'O': 'O',
-    },
-    'GLU': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': '2C',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': '2C',
-        'HG2': 'HC',
-        'HG3': 'HC',
-        'CD': 'CO',
-        'OE1': 'O2',
-        'OE2': 'O2',
-        'C': 'C',
-        'O': 'O',
-    },
-    'CGLU': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': '2C',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': '2C',
-        'HG2': 'HC',
-        'HG3': 'HC',
-        'CD': 'CO',
-        'OE1': 'O2',
-        'OE2': 'O2',
-        'C': 'C',
-        'O': 'O2',
-        'OXT': 'O2',
-    },
-    'NGLU': {
-        'N': 'N3',
-        'H1': 'H',
-        'H2': 'H',
-        'H3': 'H',
-        'CA': 'CX',
-        'HA': 'HP',
-        'CB': '2C',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': '2C',
-        'HG2': 'HC',
-        'HG3': 'HC',
-        'CD': 'CO',
-        'OE1': 'O2',
-        'OE2': 'O2',
-        'C': 'C',
-        'O': 'O',
-    },
-    'LYS': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': 'C8',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'C8',
-        'HG2': 'HC',
-        'HG3': 'HC',
-        'CD': 'C8',
-        'HD2': 'HC',
-        'HD3': 'HC',
-        'CE': 'C8',
-        'HE2': 'HP',
-        'HE3': 'HP',
-        'NZ': 'N3',
-        'HZ1': 'H',
-        'HZ2': 'H',
-        'HZ3': 'H',
-        'C': 'C',
-        'O': 'O',
-    },
-    'CLYS': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': 'C8',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'C8',
-        'HG2': 'HC',
-        'HG3': 'HC',
-        'CD': 'C8',
-        'HD2': 'HC',
-        'HD3': 'HC',
-        'CE': 'C8',
-        'HE2': 'HP',
-        'HE3': 'HP',
-        'NZ': 'N3',
-        'HZ1': 'H',
-        'HZ2': 'H',
-        'HZ3': 'H',
-        'C': 'C',
-        'O': 'O2',
-        'OXT': 'O2',
-    },
-    'NLYS': {
-        'N': 'N3',
-        'H1': 'H',
-        'H2': 'H',
-        'H3': 'H',
-        'CA': 'CX',
-        'HA': 'HP',
-        'CB': 'C8',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'C8',
-        'HG2': 'HC',
-        'HG3': 'HC',
-        'CD': 'C8',
-        'HD2': 'HC',
-        'HD3': 'HC',
-        'CE': 'C8',
-        'HE2': 'HP',
-        'HE3': 'HP',
-        'NZ': 'N3',
-        'HZ1': 'H',
-        'HZ2': 'H',
-        'HZ3': 'H',
-        'C': 'C',
-        'O': 'O',
-    },
-    'CYS': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': '2C',
-        'HB2': 'H1',
-        'HB3': 'H1',
-        'SG': 'SH',
-        'HG': 'HS',
-        'C': 'C',
-        'O': 'O',
-    },
-    'CCYS': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': '2C',
-        'HB2': 'H1',
-        'HB3': 'H1',
-        'SG': 'SH',
-        'HG': 'HS',
-        'C': 'C',
-        'O': 'O2',
-        'OXT': 'O2',
-    },
-    'NCYS': {
-        'N': 'N3',
-        'H1': 'H',
-        'H2': 'H',
-        'H3': 'H',
-        'CA': 'CX',
-        'HA': 'HP',
-        'CB': '2C',
-        'HB2': 'H1',
-        'HB3': 'H1',
-        'SG': 'SH',
-        'HG': 'HS',
-        'C': 'C',
-        'O': 'O',
-    },
-    'TYR': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': 'CT',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'CA',
-        'CD1': 'CA',
-        'HD1': 'HA',
-        'CE1': 'CA',
-        'HE1': 'HA',
-        'CZ': 'C',
-        'OH': 'OH',
-        'HH': 'HO',
-        'CE2': 'CA',
-        'HE2': 'HA',
-        'CD2': 'CA',
-        'HD2': 'HA',
-        'C': 'C',
-        'O': 'O',
-    },
-    'CTYR': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': 'CT',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'CA',
-        'CD1': 'CA',
-        'HD1': 'HA',
-        'CE1': 'CA',
-        'HE1': 'HA',
-        'CZ': 'C',
-        'OH': 'OH',
-        'HH': 'HO',
-        'CE2': 'CA',
-        'HE2': 'HA',
-        'CD2': 'CA',
-        'HD2': 'HA',
-        'C': 'C',
-        'O': 'O2',
-        'OXT': 'O2',
-    },
-    'NTYR': {
-        'N': 'N3',
-        'H1': 'H',
-        'H2': 'H',
-        'H3': 'H',
-        'CA': 'CX',
-        'HA': 'HP',
-        'CB': 'CT',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'CA',
-        'CD1': 'CA',
-        'HD1': 'HA',
-        'CE1': 'CA',
-        'HE1': 'HA',
-        'CZ': 'C',
-        'OH': 'OH',
-        'HH': 'HO',
-        'CE2': 'CA',
-        'HE2': 'HA',
-        'CD2': 'CA',
-        'HD2': 'HA',
-        'C': 'C',
-        'O': 'O',
-    },
-    'HIS': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': 'CT',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'CC',
-        'ND1': 'NB',
-        'CE1': 'CR',
-        'HE1': 'H5',
-        'NE2': 'NA',
-        'HE2': 'H',
-        'CD2': 'CW',
-        'HD2': 'H4',
-        'C': 'C',
-        'O': 'O',
-    },
-    'HID': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': 'CT',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'CC',
-        'ND1': 'NA',
-        'HD1': 'H',
-        'CE1': 'CR',
-        'HE1': 'H5',
-        'NE2': 'NB',
-        'CD2': 'CV',
-        'HD2': 'H4',
-        'C': 'C',
-        'O': 'O'
-    },
-    'NHID': {
-        'N': 'N3',
-        'H1': 'H',
-        'H2': 'H',
-        'H3': 'H',
-        'CA': 'CX',
-        'HA': 'HP',
-        'CB': 'CT',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'CC',
-        'ND1': 'NA',
-        'HD1': 'H',
-        'CE1': 'CR',
-        'HE1': 'H5',
-        'NE2': 'NB',
-        'CD2': 'CV',
-        'HD2': 'H4',
-        'C': 'C',
-        'O': 'O'
-    },
-    'CHID': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': 'CT',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'CC',
-        'ND1': 'NA',
-        'HD1': 'H',
-        'CE1': 'CR',
-        'HE1': 'H5',
-        'NE2': 'NB',
-        'CD2': 'CV',
-        'HD2': 'H4',
-        'C': 'C',
-        'O': 'O2',
-        'OXT': 'O2'
-    },
-    'CHIS': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': 'CT',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'CC',
-        'ND1': 'NB',
-        'CE1': 'CR',
-        'HE1': 'H5',
-        'NE2': 'NA',
-        'HE2': 'H',
-        'CD2': 'CW',
-        'HD2': 'H4',
-        'C': 'C',
-        'O': 'O2',
-        'OXT': 'O2',
-    },
-    'NHIS': {
-        'N': 'N3',
-        'H1': 'H',
-        'H2': 'H',
-        'H3': 'H',
-        'CA': 'CX',
-        'HA': 'HP',
-        'CB': 'CT',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'CC',
-        'ND1': 'NB',
-        'CE1': 'CR',
-        'HE1': 'H5',
-        'NE2': 'NA',
-        'HE2': 'H',
-        'CD2': 'CW',
-        'HD2': 'H4',
-        'C': 'C',
-        'O': 'O',
-    },
-    'GLY': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA2': 'H1',
-        'HA3': 'H1',
-        'C': 'C',
-        'O': 'O',
-    },
-    'CGLY': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA2': 'H1',
-        'HA3': 'H1',
-        'C': 'C',
-        'O': 'O2',
-        'OXT': 'O2',
-    },
-    'NGLY': {
-        'N': 'N3',
-        'H1': 'H',
-        'H2': 'H',
-        'H3': 'H',
-        'CA': 'CX',
-        'HA2': 'HP',
-        'HA3': 'HP',
-        'C': 'C',
-        'O': 'O',
-    },
-    'ACE': {
-        'H1': 'HC',
-        'CH3': 'CT',
-        'H2': 'HC',
-        'H3': 'HC',
-        'C': 'C',
-        'O': 'O',
-    },
-    'ASP': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': '2C',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'CO',
-        'OD1': 'O2',
-        'OD2': 'O2',
-        'C': 'C',
-        'O': 'O',
-    },
-    'CASP': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': '2C',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'CO',
-        'OD1': 'O2',
-        'OD2': 'O2',
-        'C': 'C',
-        'O': 'O2',
-        'OXT': 'O2',
-    },
-    'NASP': {
-        'N': 'N3',
-        'H1': 'H',
-        'H2': 'H',
-        'H3': 'H',
-        'CA': 'CX',
-        'HA': 'HP',
-        'CB': '2C',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'CO',
-        'OD1': 'O2',
-        'OD2': 'O2',
-        'C': 'C',
-        'O': 'O',
-    },
-    'ARG': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': 'C8',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'C8',
-        'HG2': 'HC',
-        'HG3': 'HC',
-        'CD': 'C8',
-        'HD2': 'H1',
-        'HD3': 'H1',
-        'NE': 'N2',
-        'HE': 'H',
-        'CZ': 'CA',
-        'NH1': 'N2',
-        'HH11': 'H',
-        'HH12': 'H',
-        'NH2': 'N2',
-        'HH21': 'H',
-        'HH22': 'H',
-        'C': 'C',
-        'O': 'O',
-    },
-    'CARG': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': 'C8',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'C8',
-        'HG2': 'HC',
-        'HG3': 'HC',
-        'CD': 'C8',
-        'HD2': 'H1',
-        'HD3': 'H1',
-        'NE': 'N2',
-        'HE': 'H',
-        'CZ': 'CA',
-        'NH1': 'N2',
-        'HH11': 'H',
-        'HH12': 'H',
-        'NH2': 'N2',
-        'HH21': 'H',
-        'HH22': 'H',
-        'C': 'C',
-        'O': 'O2',
-        'OXT': 'O2',
-    },
-    'NARG': {
-        'N': 'N3',
-        'H1': 'H',
-        'H2': 'H',
-        'H3': 'H',
-        'CA': 'CX',
-        'HA': 'HP',
-        'CB': 'C8',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': 'C8',
-        'HG2': 'HC',
-        'HG3': 'HC',
-        'CD': 'C8',
-        'HD2': 'H1',
-        'HD3': 'H1',
-        'NE': 'N2',
-        'HE': 'H',
-        'CZ': 'CA',
-        'NH1': 'N2',
-        'HH11': 'H',
-        'HH12': 'H',
-        'NH2': 'N2',
-        'HH21': 'H',
-        'HH22': 'H',
-        'C': 'C',
-        'O': 'O',
-    },
-    'MET': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': '2C',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': '2C',
-        'HG2': 'H1',
-        'HG3': 'H1',
-        'SD': 'S',
-        'CE': 'CT',
-        'HE1': 'H1',
-        'HE2': 'H1',
-        'HE3': 'H1',
-        'C': 'C',
-        'O': 'O',
-    },
-    'CMET': {
-        'N': 'N',
-        'H': 'H',
-        'CA': 'CX',
-        'HA': 'H1',
-        'CB': '2C',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': '2C',
-        'HG2': 'H1',
-        'HG3': 'H1',
-        'SD': 'S',
-        'CE': 'CT',
-        'HE1': 'H1',
-        'HE2': 'H1',
-        'HE3': 'H1',
-        'C': 'C',
-        'O': 'O2',
-        'OXT': 'O2',
-    },
-    'NMET': {
-        'N': 'N3',
-        'H1': 'H',
-        'H2': 'H',
-        'H3': 'H',
-        'CA': 'CX',
-        'HA': 'HP',
-        'CB': '2C',
-        'HB2': 'HC',
-        'HB3': 'HC',
-        'CG': '2C',
-        'HG2': 'H1',
-        'HG3': 'H1',
-        'SD': 'S',
-        'CE': 'CT',
-        'HE1': 'H1',
-        'HE2': 'H1',
-        'HE3': 'H1',
-        'C': 'C',
-        'O': 'O',
-    },
-    'NME': {
-        'N': 'N',
-        'H': 'H',
-        'CH3': 'CT',
-        'HH31': 'H1',
-        'HH32': 'H1',
-        'HH33': 'H1',
-    },
-}
-
-bond_pairs = {
-    'VAL': [
-        [0, 1],
-        [0, 2],
-        [2, 3],
-        [2, 4],
-        [2, 14],
-        [4, 5],
-        [4, 6],
-        [4, 10],
-        [6, 7],
-        [6, 8],
-        [6, 9],
-        [10, 11],
-        [10, 12],
-        [10, 13],
-        [14, 15],
-    ],
-    'CVAL': [
-        [0, 1],
-        [0, 2],
-        [2, 3],
-        [2, 4],
-        [2, 14],
-        [4, 5],
-        [4, 6],
-        [4, 10],
-        [6, 7],
-        [6, 8],
-        [6, 9],
-        [10, 11],
-        [10, 12],
-        [10, 13],
-        [14, 15],
-        [14, 16],
-    ],
-    'NVAL': [
-        [0, 1],
-        [0, 2],
-        [0, 3],
-        [0, 4],
-        [4, 5],
-        [4, 6],
-        [4, 16],
-        [6, 7],
-        [6, 8],
-        [6, 12],
-        [8, 9],
-        [8, 10],
-        [8, 11],
-        [12, 13],
-        [12, 14],
-        [12, 15],
-        [16, 17],
-    ],
-    'GLN': [
-        [0, 1],
-        [0, 2],
-        [2, 3],
-        [2, 4],
-        [2, 15],
-        [4, 5],
-        [4, 6],
-        [4, 7],
-        [7, 8],
-        [7, 9],
-        [7, 10],
-        [10, 11],
-        [10, 12],
-        [12, 13],
-        [12, 14],
-        [15, 16],
-    ],
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-        [0, 2],
-        [2, 3],
-        [2, 4],
-        [2, 10],
-        [4, 5],
-        [4, 6],
-        [4, 7],
-        [7, 8],
-        [7, 9],
-        [10, 11],
-    ],
-    'CASP': [
-        [0, 1],
-        [0, 2],
-        [2, 3],
-        [2, 4],
-        [2, 10],
-        [4, 5],
-        [4, 6],
-        [4, 7],
-        [7, 8],
-        [7, 9],
-        [10, 11],
-        [10, 12],
-    ],
-    'NASP': [
-        [0, 1],
-        [0, 2],
-        [0, 3],
-        [0, 4],
-        [4, 5],
-        [4, 6],
-        [4, 12],
-        [6, 7],
-        [6, 8],
-        [6, 9],
-        [9, 10],
-        [9, 11],
-        [12, 13],
-    ],
-    'ARG': [
-        [0, 1],
-        [0, 2],
-        [2, 3],
-        [2, 4],
-        [2, 22],
-        [4, 5],
-        [4, 6],
-        [4, 7],
-        [7, 8],
-        [7, 9],
-        [7, 10],
-        [10, 11],
-        [10, 12],
-        [10, 13],
-        [13, 14],
-        [13, 15],
-        [15, 16],
-        [15, 19],
-        [16, 17],
-        [16, 18],
-        [19, 20],
-        [19, 21],
-        [22, 23],
-    ],
-    'CARG': [
-        [0, 1],
-        [0, 2],
-        [2, 3],
-        [2, 4],
-        [2, 22],
-        [4, 5],
-        [4, 6],
-        [4, 7],
-        [7, 8],
-        [7, 9],
-        [7, 10],
-        [10, 11],
-        [10, 12],
-        [10, 13],
-        [13, 14],
-        [13, 15],
-        [15, 16],
-        [15, 19],
-        [16, 17],
-        [16, 18],
-        [19, 20],
-        [19, 21],
-        [22, 23],
-        [22, 24],
-    ],
-    'NARG': [
-        [0, 1],
-        [0, 2],
-        [0, 3],
-        [0, 4],
-        [4, 5],
-        [4, 6],
-        [4, 24],
-        [6, 7],
-        [6, 8],
-        [6, 9],
-        [9, 10],
-        [9, 11],
-        [9, 12],
-        [12, 13],
-        [12, 14],
-        [12, 15],
-        [15, 16],
-        [15, 17],
-        [17, 18],
-        [17, 21],
-        [18, 19],
-        [18, 20],
-        [21, 22],
-        [21, 23],
-        [24, 25],
-    ],
-    'MET': [
-        [0, 1],
-        [0, 2],
-        [2, 3],
-        [2, 4],
-        [2, 15],
-        [4, 5],
-        [4, 6],
-        [4, 7],
-        [7, 8],
-        [7, 9],
-        [7, 10],
-        [10, 11],
-        [11, 12],
-        [11, 13],
-        [11, 14],
-        [15, 16],
-    ],
-    'CMET': [
-        [0, 1],
-        [0, 2],
-        [2, 3],
-        [2, 4],
-        [2, 15],
-        [4, 5],
-        [4, 6],
-        [4, 7],
-        [7, 8],
-        [7, 9],
-        [7, 10],
-        [10, 11],
-        [11, 12],
-        [11, 13],
-        [11, 14],
-        [15, 16],
-        [15, 17],
-    ],
-    'NMET': [
-        [0, 1],
-        [0, 2],
-        [0, 3],
-        [0, 4],
-        [4, 5],
-        [4, 6],
-        [4, 17],
-        [6, 7],
-        [6, 8],
-        [6, 9],
-        [9, 10],
-        [9, 11],
-        [9, 12],
-        [12, 13],
-        [13, 14],
-        [13, 15],
-        [13, 16],
-        [17, 18],
-    ],
-    'NME': [
-        [0, 1],
-        [0, 2],
-        [2, 3],
-        [2, 4],
-        [2, 5],
-    ],
-}
-
-charge_dict = {
-    'NGLN': [2.720589, 3.637171, 3.637171, 3.637171, 0.976715,
-             1.849563, 1.186272, 0.091111, 0.091111, -1.645474,
-             0.603158, 0.603158, 13.400679, -11.175737, -18.278789,
-             8.070657, 8.070657, 11.157514, -10.4104],
-    'PRO': [-4.643042, 0.349868, 0.712492, 0.712492, 0.344401,
-            0.388135, 0.388135, -0.127556, 0.461024, 0.461024,
-            -0.484713, 1.168049, 10.743868, -10.474178],
-    'ACE': [2.046364, -6.673006, 2.046364, 2.046364, 10.882358,
-            -10.348444],
-    'CLYS': [-6.343183, 5.036644, -5.289934, 2.620367, -0.98036,
-             0.878315, 0.878315, 0.413646, 0.244179, 0.244179,
-             -0.714314, 1.113383, 1.113383, -0.320712, 2.04272,
-             2.04272, -6.816962, 6.148204, 6.148204, 6.148204,
-             15.467088, -15.037042, -15.037042],
-    'NLEU': [1.840452, 3.91415, 3.91415, 3.91415, 0.189512,
-             1.918808, -0.444624, 0.466491, 0.466491, 6.233849,
-             -0.692447, -7.482076, 1.785785, 1.785785, 1.785785,
-             -7.478432, 1.785785, 1.785785, 1.785785, 11.157514,
-             -10.4104],
-    'PHE': [-7.57501, 4.954643, -0.043734, 1.782141, -0.625025,
-            0.537558, 0.537558, 0.215023, -2.288721, 2.423566,
-            -3.10508, 2.605789, -1.953431, 2.363432, -3.10508,
-            2.605789, -2.288721, 2.423566, 10.88418, -10.348444],
-    'NSER': [3.369303, 3.458593, 3.458593, 3.458593, 1.033204,
-             1.424984, 4.730509, 0.497469, 0.497469, -12.234452,
-             7.724433, 11.230403, -10.4268],
-    'NCYS': [2.414455, 3.686371, 3.686371, 3.686371, 1.689207,
-             2.571167, -2.177565, 2.164809, 2.164809, -6.009715,
-             3.598904, 11.157514, -10.4104],
-    'NTRP': [3.485926, 3.44037, 3.44037, 3.44037, 0.767159,
-             2.117431, 0.989471, 0.404535, 0.404535, -3.013968,
-             -3.258147, 3.999795, -6.27576, 6.217449, 2.870012,
-             -4.938243, 2.895523, -1.968008, 2.571167, -3.706416,
-             2.656811, -4.127351, 2.999391, 2.062764, 11.157514,
-             -10.4104],
-    'NALA': [2.576633, 3.638993, 3.638993, 3.638993, 1.752985,
-             1.619962, -1.087871, 0.546669, 0.546669, 0.546669,
-             11.230403, -10.4268],
-    'CVAL': [-6.962741, 4.885399, -6.264827, 2.620367, 3.535126,
-             0.561247, -5.583313, 1.523384, 1.523384, 1.523384,
-             -5.583313, 1.523384, 1.523384, 1.523384, 15.21562,
-             -14.893086, -14.893086],
-    'CGLU': [-9.461018, 5.566913, -3.751972, 2.5493, 0.129378,
-             -0.142134, -0.142134, 1.230005, -0.998582, -0.998582,
-             14.911308, -14.978731, -14.978731, 13.520947, -14.450284,
-             -14.450284],
-    'CASN': [-6.962741, 4.885399, -3.790238, 2.474588, -4.189307,
-             1.864141, 1.864141, 13.034411, -10.951602, -16.553137,
-             7.562254, 7.562254, 14.668952, -14.845708, -14.845708],
-    'NVAL': [1.051427, 4.140107, 4.140107, 4.140107, -0.0984,
-             1.991697, 5.823847, -0.402713, -5.701758, 1.339339,
-             1.339339, 1.339339, -5.701758, 1.339339, 1.339339,
-             1.339339, 11.230403, -10.4268],
-    'CLEU': [-6.962741, 4.885399, -5.187889, 2.452722, -4.499086,
-             1.774852, 1.774852, 6.753184, -0.681514, -7.585943,
-             1.891475, 1.891475, 1.891475, -7.585943, 1.891475,
-             1.891475, 1.891475, 15.171887, -14.940464, -14.940464],
-    'CPRO': [-5.105888, 0.790848, 0.603158, 0.603158, 0.849159,
-             0.313424, 0.313424, -0.989471, 0.69427, 0.69427,
-             -2.434499, 1.41405, 12.083207, -14.025704, -14.025704],
-    'NILE': [0.566714, 4.243974, 4.243974, 4.243974, 0.468313,
-             1.878719, 3.434904, 0.388135, -6.778696, 1.725652,
-             1.725652, 1.725652, -0.705203, 0.366268, 0.366268,
-             -1.654585, 0.411824, 0.411824, 0.411824, 11.157514,
-             -10.4104],
-    'NPHE': [3.165214, 3.500504, 3.500504, 3.500504, 1.335695,
-             1.896941, 0.601336, 0.189512, 0.189512, 0.056489,
-             -2.536544, 2.503744, -2.919212, 2.611256, -2.201254,
-             2.421744, -2.921035, 2.611256, -2.534722, 2.503744,
-             11.157514, -10.4104],
-    'CTYR': [-6.962741, 4.885399, -3.671793, 1.989875, -1.370317,
-             0.892893, 0.892893, 0.442802, -3.502326, 3.243569,
-             -4.479041, 3.048591, 6.186471, -10.282844, 7.319898,
-             -4.479041, 3.048591, -3.502326, 3.243569, 14.244372,
-             -14.705396, -14.705396],
-    'CHID': [-6.962741, 4.885399, -3.168858, 2.004453, -1.906053,
-             1.02956, 1.02956, 0.533913, -7.092119, 6.842474,
-             3.507793, 2.583922, -10.257333, 1.824052, 2.261387,
-             13.876281, -14.606996, -14.606996],
-    'CMET': [-6.962741, 4.885399, -4.732331, 2.326988, -0.430046,
-             0.87467, 0.87467, 0.896537, 0.577647, 0.577647,
-             -4.905443, -0.685158, 1.138894, 1.138894, 1.138894,
-             14.601529, -14.769174, -14.769174],
-    'CPHE': [-6.962741, 4.885399, -3.32557, 2.000809, -1.747519,
-             0.807248, 0.807248, 1.005871, -2.368899, 2.5657,
-             -3.365659, 2.662278, -1.720185, 2.332454, -3.365659,
-             2.662278, -2.368899, 2.5657, 13.958282, -14.625218,
-             -14.625218],
-    'HIS': [-7.57501, 4.954643, -1.058716, 2.478233, -0.134845,
-            0.668758, 0.668758, 3.403926, -9.898353, 2.979346,
-            2.6149, -5.093133, 6.084426, -4.021662, 3.392992,
-            10.88418, -10.348444],
-    'CILE': [-6.962741, 4.885399, -5.648913, 2.505566, 0.661469,
-             1.395828, -6.374161, 1.860497, 1.860497, 1.860497,
-             -0.58858, 0.584936, 0.584936, -1.273739, 0.357157,
-             0.357157, 0.357157, 15.202865, -14.924064, -14.924064],
-    'ARG': [-6.339538, 5.005666, -4.805221, 2.842679, -0.012756,
-            0.595869, 0.595869, 0.71067, 0.519336, 0.519336,
-            0.885604, 1.251872, 1.251872, -9.648708, 6.297627,
-            14.716329, -15.720378, 8.159946, 8.159946, -15.720378,
-            8.159946, 8.159946, 13.37699, -10.740224],
-    'CASP': [-9.461018, 5.566913, -3.310992, 1.906053, -1.23365,
-             -0.386313, -0.386313, 16.128558, -14.873041, -14.873041,
-             13.222101, -14.371928, -14.371928],
-    'THR': [-7.57501, 4.954643, -0.708847, 1.834986, 6.658428,
-            0.078356, -4.442597, 1.169872, 1.169872, 1.169872,
-            -12.320097, 7.474787, 10.88418, -10.348444],
-    'MET': [-7.57501, 4.954643, -0.431869, 1.603562, 0.623203,
-            0.439157, 0.439157, 0.0328, 0.801781, 0.801781,
-            -4.987444, -0.976715, 1.246405, 1.246405, 1.246405,
-            10.88418, -10.348444],
-    'ASN': [-7.57501, 4.954643, 0.260579, 1.909697, -3.719171,
-            1.452317, 1.452317, 12.9925, -10.807646, -16.748116,
-            7.646077, 7.646077, 10.88418, -10.348444],
-    'HID': [-7.57501, 4.954643, 0.342579, 1.605385, -0.84187,
-            0.732536, 0.732536, -0.484713, -6.944519, 6.649317,
-            3.748327, 2.536544, -10.435911, 2.354321, 2.090098,
-            10.88418, -10.348444],
-    'CHIS': [-6.962741, 4.885399, -4.918199, 3.00668, -1.946142,
-             1.129783, 1.129783, 4.963755, -10.053243, 2.839034,
-             2.638589, -4.865354, 6.047981, -4.715931, 3.566104,
-             14.424773, -14.696285, -14.696285],
-    'ASP': [-9.408173, 5.350067, 0.69427, 1.603562, -0.552136,
-            -0.222312, -0.222312, 14.566907, -14.603351,
-            -14.603351, 9.778086, -10.603556],
-    'NASN': [3.281836, 3.500504, 3.500504, 3.500504, 0.670581,
-             2.243165, -0.515691, 0.938448, 0.938448, 10.629068,
-             -10.466889, -15.733134, 7.465676, 7.465676, 11.230403,
-             -10.4268],
-    'NGLU': [0.030978, 4.356952, 4.356952, 4.356952, 1.071471,
-             2.19032, 1.656407, -0.422757, -0.422757, -0.430046,
-             -0.574002, -0.574002, 14.736374, -14.922241, -14.922241,
-             10.242755, -10.731112],
-    'NHID': [2.809879, 3.577037, 3.577037, 3.577037, 1.75663,
-             1.745696, 0.471958, 0.380846, 0.380846, -0.72707,
-             -6.959096, 6.618339, 3.875883, 2.523789, -10.406756,
-             1.906053, 2.367077, 11.157514, -10.4104],
-    'LYS': [-6.339538, 5.005666, -4.373352, 2.5985, -0.17129,
-            0.659647, 0.659647, 0.340757, 0.18769, 0.18769,
-            -0.872848, 1.131605, 1.131605, -0.260579, 2.068231,
-            2.068231, -7.022874, 6.195582, 6.195582, 6.195582,
-            13.37699, -10.740224],
-    'TYR': [-7.57501, 4.954643, -0.025511, 1.596273, -0.276979,
-            0.537558, 0.537558, -0.020045, -3.47317, 3.095969,
-            -4.26584, 3.017613, 5.878514, -10.166221, 7.274342,
-            -4.26584, 3.017613, -3.47317, 3.095969, 10.88418,
-            -10.348444],
-    'NLYS': [1.760274, 3.945128, 3.945128, 3.945128, -0.027333,
-             2.150231, 0.386313, 0.515691, 0.515691, -0.087467,
-             0.22049, 0.22049, -1.107916, 1.153472, 1.153472,
-             -0.329824, 2.133831, 2.133831, -6.858874, 6.162782,
-             6.162782, 6.162782, 13.145567, -10.957069],
-    'NME': [-7.57501, 4.954643, -2.715123, 1.778496, 1.778496,
-            1.778496],
-    'NARG': [2.37801, 3.795705, 3.795705, 3.795705, -0.406357,
-             2.26321, 0.215023, 0.411824, 0.411824, 0.430046,
-             0.563069, 0.563069, 1.703785, 0.960315, 0.960315,
-             -10.295599, 6.54545, 15.089887, -15.840645, 8.189102,
-             8.189102, -15.840645, 8.189102, 8.189102, 13.145567,
-             -10.957069],
-    'CSER': [-6.962741, 4.885399, -4.96011, 2.376188, 2.046364,
-             1.481473, 1.481473, -11.870006, 8.152657, 14.783752,
-             -14.818374, -14.818374],
-    'CGLY': [-6.962741, 4.885399, -4.542819, 1.924275, 1.924275,
-             13.176545, -14.313617, -14.313617],
-    'CYS': [-7.57501, 4.954643, 0.388135, 2.048187, -2.243165,
-            2.02632, 2.02632, -5.683535, 3.522371, 10.88418,
-            -10.348444],
-    'NTHR': [3.301881, 3.524193, 3.524193, 3.524193, 0.061956,
-             1.980764, 8.225546, -0.58858, -4.653975, 1.142538,
-             1.142538, 1.142538, -12.325564, 7.416476, 11.230403,
-             -10.4268],
-    'LEU': [-7.57501, 4.954643, -0.943915, 1.680096, -2.008097,
-            0.832759, 0.832759, 6.434294, -0.657825, -7.50941,
-            1.82223, 1.82223, 1.82223, -7.50941, 1.82223,
-            1.82223, 1.82223, 10.88418, -10.348444],
-    'ILE': [-7.57501, 4.954643, -1.087871, 1.583518, 2.374366,
-            0.340757, -5.838425, 1.607207, 1.607207, 1.607207,
-            -0.783559, 0.430046, 0.430046, -1.202672, 0.338935,
-            0.338935, 0.338935, 10.88418, -10.348444],
-    'CCYS': [-6.962741, 4.885399, -2.979346, 2.543833, -3.637171,
-             2.618545, 2.618545, -5.652557, 3.768372, 13.661258,
-             -14.543218, -14.543218],
-    'NPRO': [-3.680905, 5.685358, 5.685358, -0.218668, 1.82223,
-             1.82223, -2.204898, 1.82223, 1.82223, -2.095565,
-             1.82223, 1.82223, 1.82223, 1.82223, 9.58493,
-             -9.11115],
-    'NTYR': [3.535126, 3.413037, 3.413037, 3.413037, 1.038671,
-             1.791252, 1.20085, 0.185867, 0.185867, -0.373557,
-             -3.648104, 3.134236, -4.079973, 3.00668, 5.71998,
-             -10.164399, 7.290742, -4.079973, 3.00668, -3.648104,
-             3.134236, 11.157514, -10.4104],
-    'GLN': [-7.57501, 4.954643, -0.056489, 1.548896, -0.0656,
-            0.311601, 0.311601, -1.175338, 0.641425, 0.641425,
-            12.666321, -11.090092, -17.141718, 7.7463, 7.7463,
-            10.88418, -10.348444],
-    'GLU': [-9.408173, 5.350067, 0.723425, 2.013564, 1.020449,
-            -0.315246, -0.315246, 0.247823, -0.774448, -0.774448,
-            14.67624, -14.920419, -14.920419, 9.778086, -10.603556],
-    'CGLN': [-6.962741, 4.885399, -4.096373, 2.244987, -1.209961,
-             0.823648, 0.823648, -0.382668, 0.369913, 0.369913,
-             12.925077, -11.111959, -17.44603, 7.842878, 7.842878,
-             14.167838, -14.654374, -14.654374],
-    'TRP': [-7.57501, 4.954643, -0.501113, 2.046364, -0.091111,
-            0.617736, 0.617736, -2.578455, -2.984813, 3.757438,
-            -6.228382, 6.217449, 2.514677, -4.73962, 2.864546,
-            -2.066409, 2.5821, -3.593438, 2.636767, -4.349663,
-            3.097791, 2.265032, 10.88418, -10.348444],
-    'CARG': [-6.343183, 5.036644, -5.590602, 2.636767, -0.681514,
-             0.676047, 0.676047, 1.355739, 0.337113, 0.337113,
-             2.029964, 0.852804, 0.852804, -10.138888, 6.339538,
-             15.248421, -15.920824, 8.187279, 8.187279, -15.920824,
-             8.187279, 8.187279, 15.592822, -15.062553, -15.062553],
-    'ALA': [-7.57501, 4.954643, 0.614092, 1.499695, -3.32557,
-            1.098805, 1.098805, 1.098805, 10.88418, -10.348444],
-    'VAL': [-7.57501, 4.954643, -1.594451, 1.765741, 5.439357,
-            -0.541202, -5.816558, 1.441384, 1.441384, 1.441384,
-            -5.816558, 1.441384, 1.441384, 1.441384, 10.88418,
-            -10.348444],
-    'CTHR': [-6.962741, 4.885399, -4.409797, 2.199432, 5.512246,
-             0.142134, -3.376592, 1.067827, 1.067827, 1.067827,
-             -11.837206, 7.505765, 14.231616, -14.658018, -14.658018],
-    'NASP': [1.424984, 4.008906, 4.008906, 4.008906, 0.532091,
-             2.079164, -0.428224, -0.307957, -0.307957, 14.931353,
-             -14.730907, -14.730907, 10.242755, -10.731112],
-    'CALA': [-6.962741, 4.885399, -3.183436, 1.944319, -3.813927,
-             1.392184, 1.392184, 1.392184, 14.08766, -14.678063,
-             -14.678063],
-    'GLY': [-7.57501, 4.954643, -0.459202, 1.271917, 1.271917,
-            10.88418, -10.348444],
-    'CTRP': [-6.962741, 4.885399, -3.797527, 2.317877, -1.352095,
-             0.905648, 0.905648, -1.450495, -3.294592, 3.722816,
-             -6.042515, 6.219271, 2.226765, -4.726865, 2.855434,
-             -1.858675, 2.552944, -4.16744, 2.746101, -3.347437,
-             2.716945, 1.964364, 13.954637, -14.597885, -14.597885],
-    'NMET': [2.90099, 3.615304, 3.615304, 3.615304, 0.402713,
-             2.033609, 1.576229, 0.227779, 0.227779, 0.608625,
-             0.532091, 0.532091, -5.054866, -0.62138, 1.087871,
-             1.087871, 1.087871, 11.157514, -10.4104],
-    'NHIS': [2.682323, 3.673616, 3.673616, 3.673616, 0.430046,
-             2.514677, 0.89107, 0.406357, 0.406357, 3.17068,
-             -10.166221, 3.287303, 2.545655, -5.067622, 6.057093,
-             -4.280418, 3.577037, 11.157514, -10.4104],
-    'SER': [-7.57501, 4.954643, -0.453735, 1.53614, 3.857661,
-            0.641425, 0.641425, -11.928318, 7.790033, 10.88418,
-            -10.348444],
-    'NGLY': [5.362823, 2.992102, 2.992102, 2.992102, -0.182223,
-             1.630896, 1.630896, 11.230403, -10.4268]}
-
-c_index = {'VAL': 14, 'CVAL': 14, 'NVAL': 16, 'GLN': 15,
-           'CGLN': 15, 'NGLN': 17, 'PHE': 18, 'CPHE': 18,
-           'NPHE': 20, 'ASN': 12, 'CASN': 12, 'NASN': 14,
-           'PRO': 12, 'CPRO': 12, 'NPRO': 14, 'THR': 12,
-           'CTHR': 12, 'NTHR': 14, 'ALA': 8, 'CALA': 8,
-           'NALA': 10, 'ILE': 17, 'CILE': 17, 'NILE': 19,
-           'SER': 9, 'CSER': 9, 'NSER': 11, 'LEU': 17,
-           'CLEU': 17, 'NLEU': 19, 'TRP': 22, 'CTRP': 22,
-           'NTRP': 24, 'GLU': 13, 'CGLU': 13, 'NGLU': 15,
-           'LYS': 20, 'CLYS': 20, 'NLYS': 22, 'CYS': 9,
-           'CCYS': 9, 'NCYS': 11, 'TYR': 19, 'CTYR': 19,
-           'NTYR': 21, 'HIS': 15, 'CHIS': 15, 'NHIS': 17,
-           'GLY': 5, 'CGLY': 5, 'NGLY': 7, 'ACE': 4,
-           'ASP': 10, 'CASP': 10, 'NASP': 12, 'ARG': 22,
-           'CARG': 22, 'NARG': 24, 'MET': 15, 'CMET': 15,
-           'NMET': 17, 'NME': 2}
-
-n_index = {'VAL': 0, 'CVAL': 0, 'NVAL': 0, 'GLN': 0,
-           'CGLN': 0, 'NGLN': 0, 'PHE': 0, 'CPHE': 0,
-           'NPHE': 0, 'ASN': 0, 'CASN': 0, 'NASN': 0,
-           'PRO': 0, 'CPRO': 0, 'NPRO': 0, 'THR': 0,
-           'CTHR': 0, 'NTHR': 0, 'ALA': 0, 'CALA': 0,
-           'NALA': 0, 'ILE': 0, 'CILE': 0, 'NILE': 0,
-           'SER': 0, 'CSER': 0, 'NSER': 0, 'LEU': 0,
-           'CLEU': 0, 'NLEU': 0, 'TRP': 0, 'CTRP': 0,
-           'NTRP': 0, 'GLU': 0, 'CGLU': 0, 'NGLU': 0,
-           'LYS': 0, 'CLYS': 0, 'NLYS': 0, 'CYS': 0,
-           'CCYS': 0, 'NCYS': 0, 'TYR': 0, 'CTYR': 0,
-           'NTYR': 0, 'HIS': 0, 'CHIS': 0, 'NHIS': 0,
-           'GLY': 0, 'CGLY': 0, 'NGLY': 0, 'ASP': 0,
-           'CASP': 0, 'NASP': 0, 'ARG': 0, 'CARG': 0,
-           'NARG': 0, 'MET': 0, 'CMET': 0, 'NMET': 0,
-           'NME': 0, 'ACE': None}
-
-vdw_params = {'name': np.array(['H', 'HO', 'HS', 'HC', 'H1', 'H2', 'H3', 'HP', 'HA', 'H4', 'H5',
-                                'HZ', 'O', 'O2', 'OH', 'OS', 'OP', 'C*', 'CI', 'C5', 'C4', 'CT',
-                                'CX', 'C', 'N', 'N3', 'S', 'SH', 'P', 'MG', 'C0', 'F', 'Cl', 'Br',
-                                'I', '2C', '3C', 'C8', 'CO'], np.str_),
-              'radius': np.array([0.6, 0., 0.6, 1.487, 1.387, 1.287, 1.187, 1.1,
-                                  1.459, 1.409, 1.359, 1.459, 1.6612, 1.6612, 1.721, 1.6837,
-                                  1.85, 1.908, 1.908, 1.908, 1.908, 1.908, 1.908, 1.908,
-                                  1.824, 1.824, 2., 2., 2.1, 0.7926, 1.7131, 1.75,
-                                  1.948, 2.22, 2.35, 1.908, 1.908, 1.908, 1.908], np.float32),
-              'well_depth': np.array([0.0157, 0., 0.0157, 0.0157, 0.0157, 0.0157,
-                                      0.0157, 0.0157, 0.015, 0.015, 0.015, 0.015,
-                                      0.21, 0.21, 0.2104, 0.17, 0.17, 0.086,
-                                      0.1094, 0.086, 0.086, 0.1094, 0.1094, 0.086,
-                                      0.17, 0.17, 0.25, 0.25, 0.2, 0.8947,
-                                      0.459789, 0.061, 0.265, 0.32, 0.4, 0.1094,
-                                      0.1094, 0.1094, 0.086], np.float32)}
-
-improper_params = {'name': np.array(['X-X-C-O', 'X-O2-C-O2', 'X-X-N-H', 'X-X-N2-H', 'X-X-NA-H',
-                                     'X-N2-CA-N2', 'X-CT-N-CT', 'X-CT-N-CX', 'X-X-CA-HA', 'X-X-CW-H4',
-                                     'X-X-CR-H5', 'X-X-CV-H4', 'X-X-CQ-H5', 'X-X-CK-H5', 'X-X-CP-H5',
-                                     'X-X-CM-H4', 'X-X-CM-HA', 'X-X-CS-H4', 'X-X-CS-HA', 'X-X-CA-H4',
-                                     'X-X-CA-H5', 'CB-CK-N*-CT', 'CB-CP-N*-CT', 'C-CM-N*-CT',
-                                     'C-CS-N*-CT', 'C-CS-CM-CT', 'CT-O-C-OH', 'CT-CV-CC-NA',
-                                     'CT-CW-CC-NB', 'CT-CW-CC-NA', 'CB-CT-C*-CW', 'CA-CA-CA-CT',
-                                     'C-CM-CM-CT', 'C-CS-CS-CT', 'CM-N2-CA-NC', 'CS-N2-CA-NC',
-                                     'CB-N2-CA-NC', 'N2-NA-CA-NC', 'CA-CA-C-OH', 'CA-CA-CA-OH',
-                                     'H5-O-C-OH', 'H5-O-C-OS', 'CM-CT-CM-HA', 'CS-CT-CS-HA',
-                                     'Br-CA-CA-CA', 'CM-H4-C-O', 'CS-H4-C-O', 'C-CT-N-H', 'C-CX-N-H',
-                                     'C-CT-N-O', 'X-X-C5-H5', 'CB-C5-N*-CT', 'X-X-C4-H4', 'X-X-C4-HA',
-                                     'C-C4-N*-CT', 'C-C4-C4-CT', 'C4-N2-CA-NC', 'CA-CA-C-OS',
-                                     'CR-CC-NA-P', 'X-O2-CO-O2', '2C-O-C-OH', 'CA-CA-CA-2C'], np.str_),
-                   'k': np.array([10.5, 10.5, 1., 1., 1., 10.5, 1., 1., 1.1, 1.1, 1.1,
-                                  1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.,
-                                  1., 1., 1., 1.1, 10.5, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1,
-                                  1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1,
-                                  1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1., 1.1, 1.1, 1.,
-                                  1.1, 1.1, 1.1, 1.1, 10.5, 10.5, 1.1], np.float32),
-                   'phi0': np.array([3.14159265, 3.14159265, 3.14159265, 3.14159265, 3.14159265,
-                                     3.14159265, 3.14159265, 3.14159265, 3.14159265, 3.14159265,
-                                     3.14159265, 3.14159265, 3.14159265, 3.14159265, 3.14159265,
-                                     3.14159265, 3.14159265, 3.14159265, 3.14159265, 3.14159265,
-                                     3.14159265, 3.14159265, 3.14159265, 3.14159265, 3.14159265,
-                                     3.14159265, 3.14159265, 3.14159265, 3.14159265, 3.14159265,
-                                     3.14159265, 3.14159265, 3.14159265, 3.14159265, 3.14159265,
-                                     3.14159265, 3.14159265, 3.14159265, 3.14159265, 3.14159265,
-                                     3.14159265, 3.14159265, 3.14159265, 3.14159265, 3.14159265,
-                                     3.14159265, 3.14159265, 3.14159265, 3.14159265, 3.14159265,
-                                     3.14159265, 3.14159265, 3.14159265, 3.14159265, 3.14159265,
-                                     3.14159265, 3.14159265, 3.14159265, 3.14159265, 3.14159265,
-                                     3.14159265, 3.14159265], np.float32),
-                   'periodicity': np.array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-                                            2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-                                            2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], np.int32)}
-
-dihedral_params = {'name': np.array(['X-C-C-X', 'X-C-CA-X', 'X-C-CB-X', 'X-C-CM-X', 'X-C-CS-X',
-                                     'X-C-CT-X', 'X-C-CX-X', 'X-C-N-X', 'X-C-N*-X', 'X-C-NA-X',
-                                     'X-C-NC-X', 'X-C-O-X', 'X-C-OH-X', 'X-C-OS-X', 'X-CA-CA-X',
-                                     'X-CA-CB-X', 'X-CA-CM-X', 'X-CA-CS-X', 'X-CA-CN-X', 'X-CA-CT-X',
-                                     'X-CA-N2-X', 'X-CA-NA-X', 'X-CA-NC-X', 'X-CA-OH-X', 'X-CB-CB-X',
-                                     'X-CB-CN-X', 'X-CB-N*-X', 'X-CB-NB-X', 'X-CB-NC-X', 'X-CC-CT-X',
-                                     'X-CC-CV-X', 'X-CC-CW-X', 'X-CC-NA-X', 'X-CC-NB-X', 'X-CD-CD-X',
-                                     'X-CD-CT-X', 'X-CD-CM-X', 'X-CD-CS-X', 'X-CK-N*-X', 'X-CK-NB-X',
-                                     'X-CP-N*-X', 'X-CP-NB-X', 'X-CM-CM-X', 'X-CM-CT-X', 'X-CM-N*-X',
-                                     'X-CM-OS-X', 'X-CS-CS-X', 'X-CS-CT-X', 'X-CS-N*-X', 'X-CS-OS-X',
-                                     'X-CN-NA-X', 'X-CQ-NC-X', 'X-CT-CT-X', 'X-CT-CX-X', 'X-CT-CY-X',
-                                     'X-CT-CZ-X', 'X-CT-N-X', 'X-CX-N-X', 'X-CT-N*-X', 'X-CT-N2-X',
-                                     'X-CT-NT-X', 'X-CT-N3-X', 'X-CX-N3-X', 'X-CT-OH-X', 'X-CT-OS-X',
-                                     'X-CT-S-X', 'X-CT-SH-X', 'X-C*-CB-X', 'X-C*-CT-X', 'X-C*-CW-X',
-                                     'X-CR-NA-X', 'X-CR-NB-X', 'X-CV-NB-X', 'X-CW-NA-X', 'X-OH-P-X',
-                                     'X-OS-P-X', 'X-CI-OS-X', 'X-CI-OH-X', 'X-CI-CT-X', 'X-C5-N*-X',
-                                     'X-C5-NB-X', 'X-C-C4-X', 'X-CA-C4-X', 'X-C4-C4-X', 'X-C4-CT-X',
-                                     'X-C4-N*-X', 'CT-OS-CT-CI', 'H1-CI-CT-OS', 'H1-CI-CT-OH',
-                                     'H1-CT-CI-OS', 'H1-CT-CI-OH', 'CI-CT-CT-CT', 'OP-P-OS-CA',
-                                     'CC-NA-P-OP', 'CR-NA-P-OP', 'OS-P-OS-CI', 'OH-P-OS-CI',
-                                     'CT-CT-CI-OS', 'CT-CT-CI-OH', 'HC-CT-C4-C4', 'C4-C4-C-O',
-                                     'OS-CT-N*-C5', 'OS-CT-N*-CP', 'OS-CT-N*-C4', 'OS-CT-N*-CS',
-                                     'C-N-CX-C', 'N-CX-C-N', 'CT-CT-N-C', 'CT-CX-N-C', 'CT-CT-C-N',
-                                     'CT-CX-C-N', 'CX-CT-C-N', 'H-N-C-O', 'CT-S-S-CT', 'OH-P-OS-CT',
-                                     'OS-P-OS-CT', 'H1-CT-C-O', 'H1-CX-C-O', 'HC-CT-C-O', 'HC-CT-CT-HC',
-                                     'HC-CT-CT-CT', 'HC-CT-CT-CX', 'HC-CT-CM-CM', 'HC-CT-CS-CS',
-                                     'HO-OH-CT-CT', 'HO-OH-CT-CX', 'HO-OH-C-O', 'CM-CM-C-O',
-                                     'CT-CM-CM-CT', 'CS-CS-C-O', 'CT-CS-CS-CT', 'CT-CT-CT-CT',
-                                     'CX-CT-CT-CT', 'CT-CT-NT-CT', 'CT-CT-OS-CT', 'CT-CT-OS-C',
-                                     'CT-OS-CT-OS', 'CT-OS-CT-N*', 'CT-CZ-CZ-HZ', 'O-C-OS-CT',
-                                     'OS-CT-N*-CK', 'OS-CT-N*-CM', 'OS-CT-CT-OS', 'OS-CT-CT-OH',
-                                     'OH-CT-CT-OH', 'F-CT-CT-F', 'Cl-CT-CT-Cl', 'Br-CT-CT-Br',
-                                     'H1-CT-CT-OS', 'H1-CT-CT-OH', 'H1-CX-CT-OH', 'H1-CT-CT-F',
-                                     'H1-CT-CT-Cl', 'H1-CT-CT-Br', 'HC-CT-CT-OS', 'HC-CT-CT-OH',
-                                     'HC-CT-CT-F', 'HC-CT-CT-Cl', 'HC-CT-CT-Br', 'H1-CT-NT-EP',
-                                     'CT-CT-NT-EP', 'CT-C-N-EP', 'O-C-N-EP', 'H1-CT-OH-EP',
-                                     'CT-CT-OH-EP', 'H1-CT-OS-EP', 'H2-CT-OS-EP', 'CT-CT-OS-EP',
-                                     'CM-CM-OS-EP', 'HA-CM-OS-EP', 'H4-CM-OS-EP', 'CS-CS-OS-EP',
-                                     'HA-CS-OS-EP', 'H4-CS-OS-EP', 'N-CT-CT-OH', 'EP-S-S-CT',
-                                     'EP-S-S-EP', 'C8-CX-N-C', '2C-CX-N-C', '3C-CX-N-C', 'N-C-CX-C8',
-                                     'N-C-CX-2C', 'N-C-CX-3C', 'N-C-2C-HC', 'O-C-2C-HC', 'OH-C-2C-HC',
-                                     'CB-C*-2C-HC', 'CW-C*-2C-HC', 'X-C8-C8-X', 'C8-C8-C8-HC',
-                                     'CX-C8-C8-HC', 'HC-C8-C8-HC', 'X-C8-CX-X', 'X-C8-N2-X',
-                                     'X-C8-N3-X', 'X-CA-2C-X', '2C-CC-CV-H4', '2C-CC-CV-NB',
-                                     '2C-CC-CW-H4', '2C-CC-CW-NA', '2C-CC-NA-CR', '2C-CC-NA-H',
-                                     '2C-CC-NB-CR', 'CV-CC-2C-HC', 'CW-CC-2C-HC', 'NA-CC-2C-HC',
-                                     'NB-CC-2C-HC', 'O2-CO-2C-HC', 'H1-CT-S-2C', 'HC-CT-2C-HC',
-                                     'HC-CT-2C-3C', 'HC-CT-3C-CT', 'HC-CT-3C-CX', 'HC-CT-3C-H1',
-                                     'HC-CT-3C-HC', 'HC-CT-3C-OH', 'HC-CT-3C-2C', 'X-CX-2C-X',
-                                     'H1-CX-2C-OH', 'X-CX-3C-X', 'H1-CX-3C-OH', 'HO-OH-2C-H1',
-                                     'HO-OH-3C-H1', 'EP-S-S-2C', 'X-S-2C-X', 'X-SH-2C-X', 'X-2C-2C-X',
-                                     'CX-2C-2C-HC', 'HC-2C-2C-HC', 'CT-2C-3C-HC', 'CX-2C-3C-HC',
-                                     'HC-2C-3C-CT', 'HC-2C-3C-CX', 'HC-2C-3C-HC', 'C-CX-C8-C8',
-                                     'N-CX-C8-C8', 'N3-CX-C8-C8', 'CX-C8-C8-C8', 'C8-C8-C8-N2',
-                                     'C8-C8-N2-CA', 'C8-C8-C8-C8', 'C8-C8-C8-N3', 'C8-N2-CA-N2',
-                                     'H-N2-CA-N2', 'C8-C8-N3-H', 'C-CX-2C-SH', 'N-CX-2C-SH',
-                                     'N3-CX-2C-SH', 'CX-2C-SH-HS', 'C-CX-2C-CO', 'N-CX-2C-CO',
-                                     'N3-CX-2C-CO', 'CX-2C-CO-O2', 'C-CX-2C-C', 'N-CX-2C-C',
-                                     'N3-CX-2C-C', 'CX-2C-C-O', 'CX-2C-C-OH', '2C-C-OH-HO', 'CX-2C-C-N',
-                                     'C-CX-CT-CC', 'N-CX-CT-CC', 'N3-CX-CT-CC', 'CX-CT-CC-NA',
-                                     'CX-CT-CC-CV', 'CX-CT-CC-NB', 'C-CX-3C-CT', 'C-CX-3C-2C',
-                                     'N-CX-3C-CT', 'N-CX-3C-2C', 'N3-CX-3C-CT', 'N3-CX-3C-2C',
-                                     'CX-3C-2C-CT', 'CT-3C-2C-CT', 'C-CX-3C-OH', 'N-CX-3C-OH',
-                                     'N3-CX-3C-OH', 'CX-3C-OH-HO', 'CT-3C-OH-HO', 'C-CX-2C-3C',
-                                     'N-CX-2C-3C', 'N3-CX-2C-3C', 'CX-2C-3C-CT', 'C-CX-2C-OH',
-                                     'N-CX-2C-OH', 'N3-CX-2C-OH', 'CX-2C-OH-HO', 'C-CX-CT-C*',
-                                     'N-CX-CT-C*', 'N3-CX-CT-C*', 'CX-CT-C*-CB', 'CX-CT-C*-CW',
-                                     'C-CX-CT-CA', 'N-CX-CT-CA', 'N3-CX-CT-CA', 'CX-CT-CA-CA',
-                                     'CA-C-OH-HO', 'C-CX-2C-2C', 'N-CX-2C-2C', 'N3-CX-2C-2C',
-                                     'CX-2C-2C-C', '2C-2C-C-O', '2C-2C-C-OH', '2C-2C-C-N',
-                                     'CX-2C-2C-CO', '2C-2C-CO-O2', 'CX-2C-2C-S', '2C-2C-S-CT',
-                                     'C-CX-2C-S', 'N-CX-2C-S', 'N3-CX-2C-S', 'CX-2C-S-S', '2C-S-S-2C'], np.str_),
-                   'ks': np.array([[3.625, 0.0, 0.0, 0.0],
-                                   [3.625, 0.0, 0.0, 0.0],
-                                   [3.0, 0.0, 0.0, 0.0],
-                                   [2.175, 0.0, 0.0, 0.0],
-                                   [2.175, 0.0, 0.0, 0.0],
-                                   [0.0, 0.0, 0.0, 0.0],
-                                   [0.0, 0.0, 0.0, 0.0],
-                                   [2.5, 0.0, 0.0, 0.0],
-                                   [1.45, 0.0, 0.0, 0.0],
-                                   [1.35, 0.0, 0.0, 0.0],
-                                   [4.0, 0.0, 0.0, 0.0],
-                                   [2.8, 0.0, 0.0, 0.0],
-                                   [2.3, 0.0, 0.0, 0.0],
-                                   [2.7, 0.0, 0.0, 0.0],
-                                   [3.625, 0.0, 0.0, 0.0],
-                                   [3.5, 0.0, 0.0, 0.0],
-                                   [2.55, 0.0, 0.0, 0.0],
-                                   [2.55, 0.0, 0.0, 0.0],
-                                   [3.625, 0.0, 0.0, 0.0],
-                                   [0.0, 0.0, 0.0, 0.0],
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-                                             [1, 2, 3, 4],
-                                             [1, 2, 3, 4],
-                                             [1, 2, 3, 4],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [2, 1, 0, 0],
-                                             [2, 3, 0, 0],
-                                             [3, 2, 0, 0],
-                                             [3, 2, 0, 0],
-                                             [1, 2, 3, 0],
-                                             [1, 2, 3, 0],
-                                             [1, 2, 3, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 1, 0, 0],
-                                             [3, 1, 0, 0],
-                                             [3, 1, 0, 0],
-                                             [3, 1, 0, 0],
-                                             [2, 1, 0, 0],
-                                             [2, 3, 0, 0],
-                                             [2, 1, 0, 0],
-                                             [2, 3, 0, 0],
-                                             [2, 1, 0, 0],
-                                             [3, 2, 1, 0],
-                                             [3, 2, 1, 0],
-                                             [3, 2, 0, 0],
-                                             [3, 2, 0, 0],
-                                             [3, 1, 0, 0],
-                                             [3, 2, 1, 0],
-                                             [3, 2, 0, 0],
-                                             [1, 0, 0, 0],
-                                             [2, 1, 0, 0],
-                                             [2, 1, 0, 0],
-                                             [2, 1, 0, 0],
-                                             [3, 2, 0, 0],
-                                             [3, 2, 0, 0],
-                                             [3, 2, 0, 0],
-                                             [3, 1, 0, 0],
-                                             [3, 1, 0, 0],
-                                             [3, 1, 0, 0],
-                                             [3, 1, 0, 0],
-                                             [3, 1, 0, 0],
-                                             [3, 1, 0, 0],
-                                             [3, 1, 0, 0],
-                                             [3, 1, 0, 0],
-                                             [3, 1, 0, 0],
-                                             [3, 1, 0, 0],
-                                             [3, 1, 0, 0],
-                                             [3, 1, 0, 0],
-                                             [3, 1, 0, 0],
-                                             [3, 1, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [2, 0, 0, 0],
-                                             [2, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [2, 0, 0, 0],
-                                             [2, 0, 0, 0],
-                                             [2, 0, 0, 0],
-                                             [2, 0, 0, 0],
-                                             [2, 0, 0, 0],
-                                             [2, 0, 0, 0],
-                                             [1, 2, 3, 4],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [2, 0, 0, 0],
-                                             [3, 2, 1, 0],
-                                             [2, 0, 0, 0],
-                                             [2, 0, 0, 0],
-                                             [2, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [2, 0, 0, 0],
-                                             [2, 0, 0, 0],
-                                             [2, 0, 0, 0],
-                                             [2, 0, 0, 0],
-                                             [2, 0, 0, 0],
-                                             [2, 0, 0, 0],
-                                             [2, 0, 0, 0],
-                                             [2, 0, 0, 0],
-                                             [2, 0, 0, 0],
-                                             [2, 0, 0, 0],
-                                             [2, 0, 0, 0],
-                                             [2, 0, 0, 0],
-                                             [2, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 1, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 1, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 1, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 2, 1, 0],
-                                             [3, 0, 0, 0],
-                                             [3, 1, 0, 0],
-                                             [3, 2, 1, 0],
-                                             [3, 0, 0, 0],
-                                             [4, 2, 0, 0],
-                                             [4, 2, 0, 0],
-                                             [3, 0, 0, 0],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [1, 0, 0, 0],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [1, 0, 0, 0],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 2, 0, 0],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1],
-                                             [4, 3, 2, 1]], np.int32),
-                   'multiple_numbers': np.array([1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
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-                                                 1,
-                                                 1,
-                                                 1,
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-                                                 2,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 3,
-                                                 2,
-                                                 1,
-                                                 1,
-                                                 3,
-                                                 3,
-                                                 3,
-                                                 3,
-                                                 2,
-                                                 2,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 3,
-                                                 3,
-                                                 3,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 3,
-                                                 3,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 3,
-                                                 2,
-                                                 1,
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-                                                 2,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 2,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 4,
-                                                 1,
-                                                 1,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 1,
-                                                 3,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 2,
-                                                 1,
-                                                 1,
-                                                 2,
-                                                 1,
-                                                 2,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 1,
-                                                 3,
-                                                 1,
-                                                 2,
-                                                 3,
-                                                 1,
-                                                 2,
-                                                 2,
-                                                 1,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 1,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 1,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 2,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4,
-                                                 4], np.int32)}
-
-angle_params = {'name': np.array(['HW-OW-HW', 'HW-HW-OW', 'C-C-O', 'C-C-OH', 'CA-C-CA', 'CA-C-OH',
-                                  'CA-C-OS', 'CC-NA-P', 'CR-NA-P', 'NA-P-OP', 'CB-C-NA', 'CB-C-O',
-                                  'CM-C-NA', 'CM-C-O', 'CS-C-NA', 'CS-C-O', 'CT-C-O', 'CX-C-O',
-                                  'CT-C-O2', 'CX-C-O2', 'CT-C-N', 'CX-C-N', 'CT-C-CT', 'CT-C-OS',
-                                  'CT-C-OH', 'CX-C-OH', 'N*-C-NA', 'N*-C-NC', 'N*-C-O', 'NA-C-O',
-                                  'NC-C-O', 'N-C-O', 'O-C-O', 'O-C-OH', 'O-C-OS', 'O2-C-O2',
-                                  'H4-C-C', 'H4-C-CM', 'H4-C-CS', 'H4-C-CT', 'H4-C-O', 'H4-C-OH',
-                                  'H5-C-N', 'H5-C-O', 'H5-C-OH', 'H5-C-OS', 'C-CA-CA', 'C-CA-HA',
-                                  'CA-CA-CA', 'CA-CA-CB', 'CA-CA-CT', 'CA-CA-HA', 'CA-CA-H4',
-                                  'CA-CA-OH', 'CA-CA-CN', 'CB-CA-HA', 'CB-CA-H4', 'CB-CA-N2',
-                                  'CB-CA-NC', 'CM-CA-N2', 'CM-CA-NC', 'CS-CA-N2', 'CS-CA-NC',
-                                  'CN-CA-HA', 'NA-CA-NC', 'N2-CA-NA', 'N2-CA-NC', 'N2-CA-N2',
-                                  'F-CA-CA', 'Cl-CA-CA', 'Br-CA-CA', 'I-CA-CA', 'C-CB-CB', 'C-CB-NB',
-                                  'CA-CB-CB', 'CA-CB-NB', 'CB-CB-N*', 'CB-CB-NB', 'CB-CB-NC',
-                                  'C*-CB-CA', 'C*-CB-CN', 'CA-CB-CN', 'N*-CB-NC', 'CD-CD-CM',
-                                  'CD-CD-CS', 'CD-CD-CT', 'CM-CD-CT', 'CS-CD-CT', 'HA-CD-HA',
-                                  'HA-CD-CD', 'HA-CD-CM', 'HA-CD-CS', 'H5-CK-N*', 'H5-CK-NB',
-                                  'N*-CK-NB', 'H5-CP-N*', 'H5-CP-NB', 'N*-CP-NB', 'C-CM-CM',
-                                  'C-CM-CT', 'C-CM-HA', 'C-CM-H4', 'CA-CM-CM', 'CA-CM-HA',
-                                  'CA-CM-H4', 'CM-CM-CT', 'CM-CM-HA', 'CM-CM-H4', 'CM-CM-N*',
-                                  'CM-CM-OS', 'H4-CM-N*', 'H4-CM-OS', 'HA-CM-HA', 'HA-CM-CD',
-                                  'HA-CM-CT', 'C-CS-CS', 'C-CS-CT', 'C-CS-HA', 'C-CS-H4', 'CA-CS-CS',
-                                  'CA-CS-HA', 'CA-CS-H4', 'CM-CS-CT', 'CS-CS-HA', 'CS-CS-H4',
-                                  'CS-CS-N*', 'CS-CS-OS', 'H4-CS-N*', 'H4-CS-OS', 'HA-CS-HA',
-                                  'HA-CS-CD', 'HA-CS-CT', 'NC-CQ-NC', 'H5-CQ-NC', 'H1-CT-H1',
-                                  'H1-CX-H1', 'H1-CT-N*', 'H1-CT-OH', 'H1-CT-OS', 'H1-CT-CM',
-                                  'H1-CT-CS', 'H1-CT-CY', 'H1-CT-CZ', 'H1-CT-N', 'H1-CX-N',
-                                  'H1-CT-S', 'H1-CT-SH', 'H1-CT-N2', 'H1-CT-NT', 'H2-CT-H2',
-                                  'H2-CT-N*', 'H2-CT-OS', 'HP-CT-HP', 'HP-CX-HP', 'HP-CT-N3',
-                                  'HP-CX-N3', 'HC-CT-HC', 'HC-CT-CM', 'HC-CT-CS', 'HC-CT-CD',
-                                  'HC-CT-CZ', 'C-CT-H1', 'C-CX-H1', 'C-CT-HP', 'C-CX-HP', 'C-CT-HC',
-                                  'C-CT-N', 'C-CX-N', 'C-CT-N3', 'C-CX-N3', 'C-CT-CT', 'C-CT-CX',
-                                  'C-CX-CT', 'C-CT-OS', 'CA-CT-HC', 'CC-CT-CT', 'CC-CT-CX',
-                                  'CC-CT-HC', 'CM-CT-CT', 'CM-CT-OS', 'CS-CT-CT', 'CS-CT-OS',
-                                  'CT-CT-CT', 'CT-CT-CX', 'CT-CT-HC', 'CX-CT-HC', 'CT-CT-H1',
-                                  'CT-CX-H1', 'CX-CT-H1', 'CT-CT-H2', 'CT-CT-HP', 'CT-CX-HP',
-                                  'CT-CT-N*', 'CT-CT-OH', 'CX-CT-OH', 'CT-CT-OS', 'CT-CT-S',
-                                  'CX-CT-S', 'CT-CT-SH', 'CX-CT-SH', 'CT-CT-CA', 'CX-CT-CA',
-                                  'CT-CT-N2', 'CT-CT-N', 'CT-CX-N', 'CT-CT-N3', 'CT-CX-N3',
-                                  'CT-CT-NT', 'CT-CT-CY', 'CT-CT-CZ', 'C*-CT-CT', 'C*-CT-CX',
-                                  'C*-CT-HC', 'OS-CT-OS', 'OS-CT-CY', 'OS-CT-CZ', 'OS-CT-N*',
-                                  'F-CT-F', 'F-CT-H1', 'F-CT-CT', 'F-CT-H2', 'Cl-CT-CT', 'Cl-CT-H1',
-                                  'Br-CT-CT', 'Br-CT-H1', 'I-CT-CT', 'CT-CC-NA', 'CT-CC-CV',
-                                  'CT-CC-NB', 'CV-CC-NA', 'CW-CC-NA', 'CW-CC-NB', 'CT-CC-CW',
-                                  'H5-CR-NA', 'H5-CR-NB', 'NA-CR-NA', 'NA-CR-NB', 'CC-CV-H4',
-                                  'CC-CV-NB', 'H4-CV-NB', 'CC-CW-H4', 'CC-CW-NA', 'C*-CW-H4',
-                                  'C*-CW-NA', 'H4-CW-NA', 'CB-C*-CT', 'CB-C*-CW', 'CT-C*-CW',
-                                  'CA-CN-CB', 'CA-CN-NA', 'CB-CN-NA', 'CT-CY-NY', 'CT-CZ-CZ',
-                                  'CZ-CZ-HZ', 'C-N-CT', 'C-N-CX', 'C-N-H', 'CT-N-H', 'CX-N-H',
-                                  'CT-N-CT', 'CT-N-CX', 'H-N-H', 'C-N*-CM', 'C-N*-CS', 'C-N*-CT',
-                                  'C-N*-H', 'CB-N*-CK', 'CB-N*-CP', 'CB-N*-CT', 'CB-N*-H',
-                                  'CK-N*-CT', 'CK-N*-H', 'CP-N*-CT', 'CP-N*-H', 'CM-N*-CT',
-                                  'CM-N*-H', 'CS-N*-CT', 'CS-N*-H', 'CA-N2-H', 'CA-N2-CT', 'CT-N2-H',
-                                  'H-N2-H', 'CT-N3-H', 'CX-N3-H', 'CT-N3-CT', 'CT-N3-CX', 'H-N3-H',
-                                  'CT-NT-H', 'CT-NT-CT', 'H-NT-H', 'C-NA-C', 'C-NA-CA', 'C-NA-H',
-                                  'CA-NA-H', 'CC-NA-CR', 'CC-NA-H', 'CR-NA-CW', 'CR-NA-H', 'CW-NA-H',
-                                  'CN-NA-CW', 'CN-NA-H', 'CB-NB-CK', 'CB-NB-CP', 'CC-NB-CR',
-                                  'CR-NB-CV', 'C-NC-CA', 'CA-NC-CB', 'CA-NC-CQ', 'CB-NC-CQ',
-                                  'C-OH-HO', 'CA-OH-HO', 'CT-OH-HO', 'HO-OH-P', 'C-OS-CT',
-                                  'CM-OS-CT', 'CS-OS-CT', 'CT-OS-CT', 'CT-OS-P', 'C-OS-P', 'P-OS-P',
-                                  'O2-P-OH', 'O2-P-O2', 'OP-P-OP', 'OP-P-OS', 'O2-P-OS', 'OH-P-OS',
-                                  'OS-P-OS', 'CT-S-CT', 'CT-S-S', 'CT-SH-HS', 'HS-SH-HS', 'CB-NB-EP',
-                                  'CC-NB-EP', 'CK-NB-EP', 'CP-NB-EP', 'CR-NB-EP', 'CV-NB-EP',
-                                  'C-NC-EP', 'CA-NC-EP', 'CB-NC-EP', 'CQ-NC-EP', 'CT-N3-EP',
-                                  'H-N3-EP', 'CT-NT-EP', 'H-NT-EP', 'C-O-EP', 'EP-O-EP', 'C-OH-EP',
-                                  'CT-OH-EP', 'HO-OH-EP', 'EP-OH-EP', 'C-OS-EP', 'CM-OS-EP',
-                                  'CS-OS-EP', 'CT-OS-EP', 'EP-OS-EP', 'CT-S-EP', 'CT-SH-EP',
-                                  'P-OS-EP', 'EP-S-EP', 'EP-SH-EP', 'HS-SH-EP', 'H1-CI-CT',
-                                  'H1-CI-H1', 'CI-CT-H1', 'CI-CT-OS', 'CI-CT-CT', 'OS-CI-H1',
-                                  'OS-CI-CT', 'P-OS-CI', 'OH-CI-H1', 'OH-CI-CT', 'HO-OH-CI',
-                                  'H5-C5-N*', 'H5-C5-NB', 'N*-C5-NB', 'CB-N*-C5', 'C5-N*-CT',
-                                  'CB-NB-C5', 'C4-C-NA', 'C4-C-O', 'C4-CA-N2', 'C4-CA-NC', 'C-C4-C4',
-                                  'C-C4-CT', 'C-C4-HA', 'C-C4-H4', 'CA-C4-C4', 'CA-C4-HA',
-                                  'CA-C4-H4', 'C4-C4-CT', 'C4-C4-HA', 'C4-C4-H4', 'C4-C4-N*',
-                                  'H4-C4-N*', 'H1-CT-C4', 'HC-CT-C4', 'C-N*-C4', 'C4-N*-CT',
-                                  'EP-S-S', 'N-C-2C', 'O-C-2C', 'OH-C-2C', 'CB-C*-2C', 'CW-C*-2C',
-                                  'C8-C8-C8', 'C8-C8-CX', 'C8-C8-H1', 'C8-C8-HC', 'C8-C8-HP',
-                                  'C8-C8-N2', 'C8-C8-N3', 'CX-C8-HC', 'H1-C8-H1', 'H1-C8-N2',
-                                  'HC-C8-HC', 'HP-C8-HP', 'HP-C8-N3', 'CA-CA-2C', 'CV-CC-2C',
-                                  'CW-CC-2C', 'NA-CC-2C', 'NB-CC-2C', 'O2-CO-O2', 'O2-CO-2C',
-                                  'HC-CT-2C', 'HC-CT-3C', 'C-CX-C8', 'C-CX-2C', 'C-CX-3C',
-                                  'C8-CX-H1', 'C8-CX-N', 'C8-CX-N3', 'H1-CX-2C', 'H1-CX-3C',
-                                  'HP-CX-C8', 'HP-CX-2C', 'HP-CX-3C', 'N-CX-2C', 'N-CX-3C',
-                                  'N3-CX-2C', 'N3-CX-3C', 'C8-N2-CA', 'C8-N2-H', 'C8-N3-H',
-                                  'HO-OH-2C', 'HO-OH-3C', 'CT-S-2C', '2C-S-S', 'HS-SH-2C', 'C-2C-CX',
-                                  'C-2C-HC', 'C-2C-2C', 'C*-2C-CX', 'C*-2C-HC', 'CA-2C-CX',
-                                  'CA-2C-HC', 'CC-2C-CX', 'CC-2C-HC', 'CO-2C-CX', 'CO-2C-HC',
-                                  'CO-2C-2C', 'CT-2C-HC', 'CT-2C-3C', 'CX-2C-H1', 'CX-2C-HC',
-                                  'CX-2C-OH', 'CX-2C-S', 'CX-2C-SH', 'CX-2C-2C', 'CX-2C-3C',
-                                  'H1-2C-H1', 'H1-2C-OH', 'H1-2C-S', 'H1-2C-SH', 'H1-2C-2C',
-                                  'HC-2C-HC', 'HC-2C-2C', 'HC-2C-3C', 'S-2C-2C', 'CT-3C-CT',
-                                  'CT-3C-CX', 'CT-3C-H1', 'CT-3C-HC', 'CT-3C-OH', 'CT-3C-2C',
-                                  'CX-3C-H1', 'CX-3C-HC', 'CX-3C-OH', 'CX-3C-2C', 'H1-3C-OH',
-                                  'HC-3C-2C'], np.str_),
-                'k': np.array([100., 0., 80., 80., 63., 70., 70., 76.7, 76.7,
-                               42.9, 70., 80., 70., 80., 70., 80., 80., 80.,
-                               70., 70., 70., 70., 63., 80., 80., 80., 70.,
-                               70., 80., 80., 80., 80., 80., 80., 80., 80.,
-                               50., 50., 50., 50., 50., 50., 50., 50., 50.,
-                               50., 63., 50., 63., 63., 70., 50., 50., 70.,
-                               63., 50., 50., 70., 70., 70., 70., 70., 70.,
-                               50., 70., 70., 70., 70., 70., 70., 70., 70.,
-                               63., 70., 63., 70., 70., 70., 70., 63., 63.,
-                               63., 70., 63., 63., 70., 70., 70., 35., 50.,
-                               50., 50., 50., 50., 70., 50., 50., 70., 63.,
-                               70., 50., 50., 63., 50., 50., 70., 50., 50.,
-                               70., 80., 50., 50., 35., 50., 50., 63., 70.,
-                               50., 50., 63., 50., 50., 70., 50., 50., 70.,
-                               80., 50., 50., 35., 50., 50., 70., 50., 35.,
-                               35., 50., 50., 50., 50., 50., 50., 50., 50.,
-                               50., 50., 50., 50., 50., 35., 50., 50., 35.,
-                               35., 50., 50., 35., 50., 50., 50., 50., 50.,
-                               50., 50., 50., 50., 63., 63., 80., 80., 63.,
-                               63., 63., 60., 50., 63., 63., 50., 63., 50.,
-                               63., 50., 40., 40., 50., 50., 50., 50., 50.,
-                               50., 50., 50., 50., 50., 50., 50., 50., 50.,
-                               50., 50., 63., 63., 80., 80., 80., 80., 80.,
-                               80., 63., 63., 63., 63., 50., 160., 50., 50.,
-                               50., 77., 50., 50., 50., 50., 50., 50., 50.,
-                               50., 70., 70., 70., 70., 70., 70., 70., 50.,
-                               50., 70., 70., 50., 70., 50., 50., 70., 50.,
-                               70., 50., 70., 63., 70., 63., 70., 70., 80.,
-                               80., 50., 50., 50., 50., 50., 50., 50., 50.,
-                               35., 70., 70., 70., 50., 70., 70., 70., 50.,
-                               70., 50., 70., 50., 70., 50., 70., 50., 50.,
-                               50., 50., 35., 50., 50., 50., 50., 35., 50.,
-                               50., 35., 70., 70., 50., 50., 70., 50., 70.,
-                               50., 50., 70., 50., 70., 70., 70., 70., 70.,
-                               70., 70., 70., 50., 50., 55., 45., 60., 60.,
-                               60., 60., 100., 100., 100., 45., 140., 140., 100.,
-                               100., 45., 45., 62., 68., 43., 35., 150., 150.,
-                               150., 150., 150., 150., 150., 150., 150., 150., 150.,
-                               150., 150., 150., 150., 150., 150., 150., 150., 150.,
-                               150., 150., 150., 150., 150., 150., 150., 150., 150.,
-                               150., 150., 50., 35., 50., 50., 40., 50., 50.,
-                               100., 50., 50., 55., 50., 50., 70., 70., 70.,
-                               70., 70., 80., 70., 70., 63., 70., 50., 50.,
-                               63., 50., 50., 70., 50., 50., 70., 50., 50.,
-                               50., 70., 70., 150., 70., 80., 80., 70., 70.,
-                               40., 40., 50., 50., 50., 80., 80., 50., 35.,
-                               50., 35., 35., 50., 70., 70., 70., 70., 70.,
-                               80., 70., 50., 50., 63., 63., 63., 50., 80.,
-                               80., 50., 50., 50., 50., 50., 80., 80., 80.,
-                               80., 50., 50., 50., 55., 55., 62., 68., 43.,
-                               63., 50., 63., 63., 50., 63., 50., 63., 50.,
-                               63., 50., 63., 50., 40., 50., 50., 50., 50.,
-                               50., 40., 40., 35., 50., 50., 50., 50., 35.,
-                               50., 50., 50., 40., 40., 50., 50., 50., 40.,
-                               50., 50., 50., 40., 50., 50.], np.float32),
-                'b': np.array([1.82421813, 2.22948359, 2.0943951, 2.0943951, 2.0943951,
-                               2.0943951, 2.0943951, 2.18340689, 2.18340689, 1.78686809,
-                               1.94255146, 2.24798408, 1.99142068, 2.18689755, 1.99142068,
-                               2.18689755, 2.10137642, 2.10137642, 2.04203522, 2.04203522,
-                               2.03505391, 2.03505391, 2.04203522, 2.00712864, 1.91986218,
-                               1.91986218, 2.01410996, 2.06996049, 2.11010307, 2.10486708,
-                               2.13802833, 2.14500965, 2.19911486, 2.0943951, 2.18166156,
-                               2.19911486, 2.0943951, 2.00712864, 2.00712864, 2.00712864,
-                               2.0943951, 2.0943951, 2.0943951, 2.07694181, 1.8675023,
-                               1.8675023, 2.0943951, 2.0943951, 2.0943951, 2.0943951,
-                               2.0943951, 2.0943951, 2.0943951, 2.0943951, 2.0943951,
-                               2.0943951, 2.0943951, 2.15548163, 2.04727121, 2.09614043,
-                               2.12057504, 2.09614043, 2.12057504, 2.0943951, 2.15199097,
-                               2.02458193, 2.0821778, 2.0943951, 2.11184839, 2.07345115,
-                               2.07345115, 2.07345115, 2.08043247, 2.26892803, 2.04727121,
-                               2.31081593, 1.85353967, 1.92684349, 2.22878545, 2.35444916,
-                               1.89891823, 2.02807259, 2.20260552, 2.0943951, 2.0943951,
-                               2.0943951, 2.0943951, 2.0943951, 2.07694181, 2.0943951,
-                               2.0943951, 2.0943951, 2.14762764, 2.14762764, 1.98793002,
-                               2.14762764, 2.14762764, 1.98793002, 2.10661241, 2.08915911,
-                               2.08915911, 2.08915911, 2.04203522, 2.15199097, 2.15199097,
-                               2.08915911, 2.08915911, 2.08915911, 2.11533905, 2.18166156,
-                               2.07868714, 1.97222205, 2.0943951, 2.0943951, 2.0943951,
-                               2.10661241, 2.08915911, 2.08915911, 2.08915911, 2.04203522,
-                               2.15199097, 2.15199097, 2.08915911, 2.08915911, 2.08915911,
-                               2.11533905, 2.18166156, 2.07868714, 1.97222205, 2.0943951,
-                               2.0943951, 2.0943951, 2.25322006, 2.01498262, 1.91113553,
-                               1.91113553, 1.91113553, 1.91113553, 1.91113553, 1.91113553,
-                               1.91113553, 1.91986218, 1.91986218, 1.91113553, 1.91113553,
-                               1.91113553, 1.91113553, 1.91113553, 1.91113553, 1.91113553,
-                               1.91113553, 1.91113553, 1.91113553, 1.91113553, 1.91113553,
-                               1.91113553, 1.91113553, 1.91113553, 1.91113553, 1.91113553,
-                               1.91986218, 1.91113553, 1.91113553, 1.91113553, 1.91113553,
-                               1.91113553, 1.92160751, 1.92160751, 1.94080613, 1.94080613,
-                               1.9390608, 1.9390608, 1.9390608, 1.91113553, 1.91113553,
-                               1.97396738, 1.97396738, 1.91113553, 1.93731547, 1.91113553,
-                               1.93731547, 1.91113553, 1.91113553, 1.91113553, 1.91113553,
-                               1.91113553, 1.91113553, 1.91113553, 1.91113553, 1.91113553,
-                               1.91113553, 1.91113553, 1.91113553, 1.91113553, 1.91113553,
-                               1.91113553, 2.00189265, 2.00189265, 1.89542757, 1.89542757,
-                               1.98967535, 1.98967535, 1.94080613, 1.91462619, 1.91462619,
-                               1.94080613, 1.94080613, 1.94080613, 1.91986218, 1.91986218,
-                               2.01760062, 2.01760062, 1.91113553, 1.76278254, 1.91986218,
-                               1.91986218, 1.91113553, 1.90415421, 1.91113553, 1.90240888,
-                               1.91113553, 1.89368224, 1.89368224, 1.88495559, 1.85877565,
-                               1.85004901, 2.0943951, 2.0943951, 2.0943951, 2.0943951,
-                               2.0943951, 2.0943951, 2.0943951, 2.0943951, 2.0943951,
-                               2.0943951, 2.0943951, 2.0943951, 2.0943951, 2.0943951,
-                               2.0943951, 2.0943951, 2.0943951, 1.8971729, 2.0943951,
-                               2.24449342, 1.85703032, 2.18166156, 2.14151899, 2.31779725,
-                               1.82212374, 3.14159265, 3.14159265, 3.14159265, 2.12755636,
-                               2.12755636, 2.0943951, 2.06018665, 2.06018665, 2.05948852,
-                               2.05948852, 2.0943951, 2.12232037, 2.12232037, 2.0525072,
-                               2.08043247, 1.83957703, 1.83957703, 2.1956242, 2.1956242,
-                               2.24798408, 2.24798408, 2.24798408, 2.24798408, 2.11533905,
-                               2.11533905, 2.11533905, 2.11533905, 2.0943951, 2.15024564,
-                               2.06646983, 2.0943951, 1.91113553, 1.91113553, 1.91113553,
-                               1.91113553, 1.91113553, 1.91113553, 1.91113553, 1.91113553,
-                               2.20609617, 2.18515222, 2.03854457, 2.05948852, 2.0943951,
-                               2.0943951, 2.0943951, 2.0943951, 2.0943951, 1.94778745,
-                               2.14850031, 1.81165176, 1.81165176, 2.04203522, 2.04203522,
-                               2.10312175, 1.95825942, 2.06996049, 1.93731547, 1.97222205,
-                               1.97222205, 1.89368224, 1.89368224, 2.04203522, 2.04203522,
-                               2.04203522, 1.91113553, 2.10312175, 2.10312175, 2.10312175,
-                               1.88896985, 2.09264977, 2.09264977, 1.88896985, 1.88896985,
-                               1.79070781, 1.79070781, 1.72613063, 1.80990643, 1.67551608,
-                               1.60692464, 2.19911486, 2.19911486, 2.19911486, 2.19911486,
-                               2.19911486, 2.19911486, 2.0943951, 2.0943951, 2.0943951,
-                               2.0943951, 1.91113553, 1.91113553, 1.91113553, 1.91113553,
-                               2.0943951, 2.0943951, 2.0943951, 1.91113553, 1.91113553,
-                               1.91113553, 1.91113553, 1.91113553, 1.91113553, 1.91113553,
-                               1.91113553, 1.57079633, 1.57079633, 1.91113553, 3.14159265,
-                               3.14159265, 1.57079633, 1.91113553, 1.91113553, 1.91113553,
-                               1.91113553, 1.91113553, 1.91113553, 1.91113553, 2.10312175,
-                               1.91113553, 1.91113553, 1.89368224, 2.14762764, 2.14762764,
-                               1.98793002, 1.83957703, 2.24798408, 1.81165176, 1.99142068,
-                               2.18689755, 2.09614043, 2.12057504, 2.10661241, 2.08915911,
-                               2.08915911, 2.08915911, 2.04203522, 2.15199097, 2.15199097,
-                               2.08915911, 2.08915911, 2.08915911, 2.11533905, 2.07868714,
-                               1.91113553, 1.91113553, 2.12232037, 2.11533905, 1.68773339,
-                               2.03505391, 2.10137642, 1.91986218, 2.24449342, 2.18166156,
-                               1.91113553, 1.91113553, 1.91113553, 1.91113553, 1.91113553,
-                               1.94080613, 1.94080613, 1.91113553, 1.91113553, 1.91113553,
-                               1.91113553, 1.91113553, 1.91113553, 2.0943951, 2.0943951,
-                               2.0943951, 2.0943951, 2.0943951, 2.19911486, 2.04203522,
-                               1.91113553, 1.91113553, 1.9390608, 1.9390608, 1.9390608,
-                               1.91113553, 1.91462619, 1.94080613, 1.91113553, 1.91113553,
-                               1.91113553, 1.91113553, 1.91113553, 1.91462619, 1.91462619,
-                               1.94080613, 1.94080613, 2.15024564, 2.06646983, 1.91113553,
-                               1.89368224, 1.89368224, 1.72613063, 1.80990643, 1.67551608,
-                               1.9390608, 1.91113553, 1.9390608, 2.01760062, 1.91113553,
-                               1.98967535, 1.91113553, 1.97396738, 1.91113553, 1.9390608,
-                               1.91113553, 1.9390608, 1.91113553, 1.91113553, 1.91113553,
-                               1.91113553, 1.91113553, 2.00189265, 1.89542757, 1.91113553,
-                               1.91113553, 1.91113553, 1.91113553, 1.91113553, 1.91113553,
-                               1.91113553, 1.91113553, 1.91113553, 1.91113553, 2.00189265,
-                               1.91113553, 1.91113553, 1.91113553, 1.91113553, 1.91113553,
-                               1.91113553, 1.91113553, 1.91113553, 1.91113553, 1.91113553,
-                               1.91113553, 1.91113553], np.float32)}
-
-bond_params = {'name': np.array(['OW-HW', 'HW-HW', 'C-C', 'C-CA', 'C-CB', 'C-CM', 'C-CS', 'C-CT',
-                                 'C-CX', 'C-N', 'C-N*', 'C-NA', 'C-NC', 'C-O', 'C-O2', 'C-OH',
-                                 'C-OS', 'C-H4', 'C-H5', 'CA-CA', 'CA-CB', 'CA-CM', 'CA-CS',
-                                 'CA-CN', 'CA-CT', 'CA-HA', 'CA-H4', 'CA-N2', 'CA-NA', 'CA-NC',
-                                 'CA-OH', 'CB-CB', 'CB-N*', 'CB-NB', 'CB-NC', 'CD-HA', 'CD-CD',
-                                 'CD-CM', 'CD-CS', 'CD-CT', 'CK-H5', 'CK-N*', 'CK-NB', 'CP-H5',
-                                 'CP-N*', 'CP-NB', 'CM-CM', 'CM-CT', 'CM-HA', 'CM-H4', 'CM-H5',
-                                 'CM-N*', 'CM-OS', 'CS-CS', 'CS-CT', 'CS-HA', 'CS-H4', 'CS-H5',
-                                 'CS-N*', 'CS-OS', 'CQ-H5', 'CQ-NC', 'CT-CT', 'CX-CT', 'CT-HC',
-                                 'CT-H1', 'CX-H1', 'CT-H2', 'CT-H3', 'CT-HP', 'CX-HP', 'CT-N*',
-                                 'CT-N2', 'CT-OH', 'CT-OS', 'C*-HC', 'C*-CB', 'C*-CT', 'C*-CW',
-                                 'CB-CN', 'CC-CT', 'CC-CV', 'CC-CW', 'CC-NA', 'CC-NB', 'CN-NA',
-                                 'CR-H5', 'CR-NA', 'CR-NB', 'CT-N', 'CX-N', 'CT-N3', 'CX-N3',
-                                 'CT-NT', 'CT-S', 'CT-SH', 'CT-CY', 'CT-CZ', 'CV-H4', 'CV-NB',
-                                 'CW-H4', 'CW-NA', 'CY-NY', 'CZ-CZ', 'CZ-HZ', 'OP-P', 'O2-P',
-                                 'OH-P', 'OS-P', 'NA-P', 'H-N2', 'H-N*', 'H-NA', 'H-N', 'H-N3',
-                                 'H-NT', 'HO-OH', 'HO-OS', 'HS-SH', 'S-S', 'F-CT', 'Cl-CT', 'Br-CT',
-                                 'I-CT', 'F-CA', 'Cl-CA', 'I-CA', 'Br-CA', 'EP-O', 'EP-OH', 'EP-OS',
-                                 'EP-N3', 'EP-NT', 'EP-NB', 'EP-NC', 'EP-S', 'EP-SH', 'CI-H1',
-                                 'CI-CT', 'OS-CI', 'OH-CI', 'C5-H5', 'C5-N*', 'C5-NB', 'C-C4',
-                                 'CA-C4', 'C4-C4', 'C4-CT', 'C4-HA', 'C4-H4', 'C4-N*', 'C-2C',
-                                 'C*-2C', 'C8-C8', 'C8-CX', 'C8-H1', 'C8-HC', 'C8-HP', 'C8-N2',
-                                 'C8-N3', 'CA-2C', 'CC-2C', 'CO-O2', 'CO-2C', 'CT-2C', 'CT-3C',
-                                 'CX-2C', 'CX-3C', 'H1-2C', 'H1-3C', 'HC-2C', 'HC-3C', 'OH-2C',
-                                 'OH-3C', 'S-2C', 'SH-2C', '2C-2C', '2C-3C'], np.str_),
-               'k': np.array([553., 553., 310., 469., 447., 410., 410., 317., 317., 490., 424.,
-                              418., 457., 570., 656., 450., 450., 367., 367., 469., 469., 427.,
-                              427., 469., 317., 367., 367., 481., 427., 483., 450., 520., 436.,
-                              414., 461., 367., 469., 549., 549., 317., 367., 440., 529., 367.,
-                              440., 529., 549., 317., 367., 367., 367., 448., 480., 549., 317.,
-                              367., 367., 367., 448., 480., 367., 502., 310., 310., 340., 340.,
-                              340., 340., 340., 340., 340., 337., 337., 320., 320., 367., 388.,
-                              317., 546., 447., 317., 512., 518., 422., 410., 428., 367., 477.,
-                              488., 337., 337., 367., 367., 367., 227., 237., 400., 400., 367.,
-                              410., 367., 427., 600., 600., 400., 525., 525., 230., 230., 250.,
-                              434., 434., 434., 434., 434., 434., 553., 553., 274., 166., 367.,
-                              232., 159., 148., 386., 193., 171., 172., 600., 600., 600., 600.,
-                              600., 600., 600., 600., 600., 340., 310., 320., 320., 367., 440.,
-                              529., 410., 427., 549., 317., 367., 367., 448., 317., 317., 310.,
-                              310., 340., 340., 340., 337., 367., 317., 317., 656., 317., 310.,
-                              310., 310., 310., 340., 340., 340., 340., 320., 320., 227., 237.,
-                              310., 310.], np.float32),
-               'b': np.array([0.9572, 1.5136, 1.525, 1.409, 1.419, 1.444, 1.444, 1.522,
-                              1.522, 1.335, 1.383, 1.388, 1.358, 1.229, 1.25, 1.364,
-                              1.323, 1.08, 1.08, 1.4, 1.404, 1.433, 1.433, 1.4,
-                              1.51, 1.08, 1.08, 1.34, 1.381, 1.339, 1.364, 1.37,
-                              1.374, 1.391, 1.354, 1.08, 1.4, 1.35, 1.35, 1.51,
-                              1.08, 1.371, 1.304, 1.08, 1.371, 1.304, 1.35, 1.51,
-                              1.08, 1.08, 1.08, 1.365, 1.24, 1.35, 1.51, 1.08,
-                              1.08, 1.08, 1.365, 1.24, 1.08, 1.324, 1.526, 1.526,
-                              1.09, 1.09, 1.09, 1.09, 1.09, 1.09, 1.09, 1.475,
-                              1.463, 1.41, 1.41, 1.08, 1.459, 1.495, 1.352, 1.419,
-                              1.504, 1.375, 1.371, 1.385, 1.394, 1.38, 1.08, 1.343,
-                              1.335, 1.449, 1.449, 1.471, 1.471, 1.471, 1.81, 1.81,
-                              1.458, 1.459, 1.08, 1.394, 1.08, 1.381, 1.15, 1.206,
-                              1.056, 1.48, 1.48, 1.61, 1.61, 1.84, 1.01, 1.01,
-                              1.01, 1.01, 1.01, 1.01, 0.96, 0.96, 1.336, 2.038,
-                              1.38, 1.766, 1.944, 2.166, 1.359, 1.727, 2.075, 1.89,
-                              0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.7,
-                              0.7, 1.09, 1.526, 1.41, 1.41, 1.08, 1.371, 1.304,
-                              1.444, 1.433, 1.35, 1.51, 1.08, 1.08, 1.365, 1.522,
-                              1.495, 1.526, 1.526, 1.09, 1.09, 1.09, 1.463, 1.471,
-                              1.51, 1.504, 1.25, 1.522, 1.526, 1.526, 1.526, 1.526,
-                              1.09, 1.09, 1.09, 1.09, 1.41, 1.41, 1.81, 1.81,
-                              1.526, 1.526], np.float32)}
diff --git a/MindSPONGE/mindsponge/python/common/r3.py b/MindSPONGE/mindsponge/python/common/r3.py
deleted file mode 100644
index ee058e44e4c5d00459d7dfdeeabc594fa6fcd0f9..0000000000000000000000000000000000000000
--- a/MindSPONGE/mindsponge/python/common/r3.py
+++ /dev/null
@@ -1,188 +0,0 @@
-# Copyright 2021-2022 @ Shenzhen Bay Laboratory &
-#                       Peking University &
-#                       Huawei Technologies Co., Ltd
-#
-# This code is a part of MindSPONGE:
-# MindSpore Simulation Package tOwards Next Generation molecular modelling.
-#
-# MindSPONGE is open-source software based on the AI-framework:
-# MindSpore (https://www.mindspore.cn/)
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""r3"""
-
-import numpy as np
-import mindspore.numpy as mnp
-
-
-def vecs_sub(v1, v2):
-    """Computes v1 - v2."""
-    return v1 - v2
-
-
-def vecs_robust_norm(v, epsilon=1e-8, use_numpy=False):
-    """Computes norm of vectors 'v'."""
-    if use_numpy:
-        return np.sqrt(np.square(v[0]) + np.square(v[1]) + np.square(v[2]) + epsilon)
-    return mnp.sqrt(mnp.sum(mnp.square(v), axis=-1) + epsilon)
-
-
-def vecs_robust_normalize(v, epsilon=1e-8, use_numpy=False):
-    """Normalizes vectors 'v'."""
-
-    norms = vecs_robust_norm(v, epsilon, use_numpy=use_numpy)
-    if use_numpy:
-        return v / norms[None, ...]
-    return v / norms[..., None]
-
-
-def vecs_dot_vecs(v1, v2, use_numpy=False):
-    """Dot product of vectors 'v1' and 'v2'."""
-    if use_numpy:
-        return np.sum(v1 * v2, axis=0)
-    return mnp.sum(v1 * v2, axis=-1)
-
-
-def vecs_cross_vecs(v1, v2, use_numpy=False):
-    """Cross product of vectors 'v1' and 'v2'."""
-    if use_numpy:
-        out = np.array((v1[1] * v2[2] - v1[2] * v2[1],
-                        v1[2] * v2[0] - v1[0] * v2[2],
-                        v1[0] * v2[1] - v1[1] * v2[0]))
-        return out
-    return mnp.concatenate(((v1[..., 1] * v2[..., 2] - v1[..., 2] * v2[..., 1])[..., None],
-                            (v1[..., 2] * v2[..., 0] -
-                             v1[..., 0] * v2[..., 2])[..., None],
-                            (v1[..., 0] * v2[..., 1] - v1[..., 1] * v2[..., 0])[..., None]), axis=-1)
-
-
-def rots_from_two_vecs(e0_unnormalized, e1_unnormalized, use_numpy=False):
-    """Create rotation matrices from unnormalized vectors for the x and y-axes."""
-
-    # Normalize the unit vector for the x-axis, e0.
-    e0 = vecs_robust_normalize(e0_unnormalized, use_numpy=use_numpy)
-
-    # make e1 perpendicular to e0.
-    c = vecs_dot_vecs(e1_unnormalized, e0, use_numpy=use_numpy)
-    if use_numpy:
-        e1 = e1_unnormalized - c[None, ...] * e0
-    else:
-        e1 = e1_unnormalized - c[..., None] * e0
-    e1 = vecs_robust_normalize(e1, use_numpy=use_numpy)
-
-    # Compute e2 as cross product of e0 and e1.
-    e2 = vecs_cross_vecs(e0, e1, use_numpy=use_numpy)
-
-    if use_numpy:
-        rots = np.array((e0[0], e1[0], e2[0], e0[1],
-                         e1[1], e2[1], e0[2], e1[2], e2[2]))
-        return rots
-
-    rots = mnp.concatenate(
-        (mnp.concatenate([e0[..., 0][None, ...], e1[..., 0][None, ...], e2[..., 0][None, ...]], axis=0)[None, ...],
-         mnp.concatenate([e0[..., 1][None, ...], e1[..., 1]
-                          [None, ...], e2[..., 1][None, ...]], axis=0)[None, ...],
-         mnp.concatenate([e0[..., 2][None, ...], e1[..., 2][None, ...], e2[..., 2][None, ...]], axis=0)[None, ...]),
-        axis=0)
-    return rots
-
-
-def rigids_from_3_points(
-        point_on_neg_x_axis,  # shape (...)
-        origin,  # shape (...)
-        point_on_xy_plane,  # shape (...)
-        use_numpy=False
-):  # shape (...)
-    """Create Rigids from 3 points. """
-    m = rots_from_two_vecs(
-        e0_unnormalized=vecs_sub(origin, point_on_neg_x_axis),
-        e1_unnormalized=vecs_sub(point_on_xy_plane, origin),
-        use_numpy=use_numpy)
-    return m, origin
-
-
-def invert_rots(m, use_numpy=False):
-    """Computes inverse of rotations 'm'."""
-    if use_numpy:
-        out = np.array((m[0], m[3], m[6],
-                        m[1], m[4], m[7],
-                        m[2], m[5], m[8]))
-        return out
-    return mnp.transpose(m, (1, 0, 2, 3, 4))
-
-
-def rots_mul_vecs(m, v, use_numpy=False):
-    """Apply rotations 'm' to vectors 'v'."""
-    if use_numpy:
-        out = np.array((m[0] * v[0] + m[1] * v[1] + m[2] * v[2],
-                        m[3] * v[0] + m[4] * v[1] + m[5] * v[2],
-                        m[6] * v[0] + m[7] * v[1] + m[8] * v[2]))
-        return out
-    return mnp.concatenate(((m[0][0] * v[..., 0] + m[0][1] * v[..., 1] + m[0][2] * v[..., 2])[..., None],
-                            (m[1][0] * v[..., 0] + m[1][1] *
-                             v[..., 1] + m[1][2] * v[..., 2])[..., None],
-                            (m[2][0] * v[..., 0] + m[2][1] * v[..., 1] + m[2][2] * v[..., 2])[..., None]), axis=-1)
-
-
-def invert_rigids(rot, trans, use_numpy=False):
-    """Computes group inverse of rigid transformations 'r'."""
-    inv_rots = invert_rots(rot, use_numpy=use_numpy)
-    t = rots_mul_vecs(inv_rots, trans, use_numpy=use_numpy)
-    inv_trans = -t
-    return inv_rots, inv_trans
-
-
-def vecs_add(v1, v2):
-    """Add two vectors 'v1' and 'v2'."""
-    return v1 + v2
-
-
-def rigids_mul_vecs(rot, trans, v, use_numpy=False):
-    """Apply rigid transforms 'r' to points 'v'."""
-    return vecs_add(rots_mul_vecs(rot, v, use_numpy=use_numpy), trans)
-
-
-def rigids_mul_rots(x, y, use_numpy=False):
-    """numpy version of getting results rigids x multiply rots y"""
-    rigids = (rots_mul_rots(x[0], y, use_numpy=use_numpy), x[1])
-    return rigids
-
-
-def rots_mul_rots(x, y, use_numpy=False):
-    """numpy version of getting result of rots x multiply rots y"""
-    vecs0 = rots_mul_vecs(x, (y[0], y[3], y[6]), use_numpy)
-    vecs1 = rots_mul_vecs(x, (y[1], y[4], y[7]), use_numpy)
-    vecs2 = rots_mul_vecs(x, (y[2], y[5], y[8]), use_numpy)
-    return np.array((vecs0[0], vecs1[0], vecs2[0], vecs0[1], vecs1[1], vecs2[1], vecs0[2], vecs1[2], vecs2[2]))
-
-
-def vecs_from_tensor(inputs):
-    """get vectors from input tensor"""
-    num_components = inputs.shape[-1]
-    assert num_components == 3
-    return np.array((inputs[..., 0], inputs[..., 1], inputs[..., 2]))
-
-
-def rigids_to_tensor_flat12(inputs):  # outputs shape (..., 12)
-    """transfer rigids to flat tensor"""
-    return np.stack(list(inputs[0]) + list(inputs[1]), axis=-1)
-
-
-def rots_from_tensor3x3(inputs):
-    """get rotation from inputs tensor"""
-    assert inputs.shape[-1] == 3
-    assert inputs.shape[-2] == 3
-    return np.array((inputs[..., 0, 0], inputs[..., 0, 1], inputs[..., 0, 2],
-                     inputs[..., 1, 0], inputs[..., 1, 1], inputs[..., 1, 2],
-                     inputs[..., 2, 0], inputs[..., 2, 1], inputs[..., 2, 2]))
diff --git a/MindSPONGE/mindsponge/python/data/data_loader.py b/MindSPONGE/mindsponge/python/data/data_loader.py
deleted file mode 100644
index d96a1cc5a79b7a3b476b7eeb9a38a3b4356691f1..0000000000000000000000000000000000000000
--- a/MindSPONGE/mindsponge/python/data/data_loader.py
+++ /dev/null
@@ -1,247 +0,0 @@
-# Copyright 2021 The AIMM Group at Shenzhen Bay Laboratory & Peking University & Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""data loader"""
-import math
-import parmed as pmd
-import numpy as np
-
-from ..common.constants import TIME_UNIT, CHARGE_UNIT, SUPPORT_TYPE, KB
-
-
-class system:
-    """system"""
-
-    def __init__(self, config):
-        if config.file_type not in SUPPORT_TYPE:
-            return None
-        self.mode = config.mode
-        self.dt = config.dt
-        self.target_temperature = config.target_temperature
-        self.gamma_ln = 1.0
-        if 'gamma_ln' in vars(config):
-            self.gamma_ln = config.gamma_ln
-        elif 'langevin_gamma' in vars(config):
-            self.gamma_ln = config.langevin_gamma
-        self.gamma_ln = self.gamma_ln / TIME_UNIT
-        self.exp_gamma = math.exp(-1 * self.gamma_ln * self.dt)
-        self.sqrt_gamma = math.sqrt((1. - self.exp_gamma * self.exp_gamma) * self.target_temperature * KB)
-
-        if "coordinates_file" in vars(config):
-            self._system = pmd.load_file(config.topology_file, xyz=config.coordinates_file)
-            self._crd = pmd.load_file(config.coordinates_file)
-        else:
-            self._system = pmd.load_file(config.topology_file, xyz=config.coordinates_file)
-        self.atom_numbers = len(self._system.atoms)
-        self.system_freedom = 3 * self.atom_numbers
-        self.coordinates = self._system.coordinates.astype(np.float32)
-        self.box_len = self._crd.box.astype(np.float32)[:3]
-        self.velocities = None
-        if self._crd.hasvels:
-            self.velocities = self._crd.velocities
-            self.velocities = self.velocities / TIME_UNIT
-        else:
-            self.velocities = np.zeros([self.atom_numbers, 3]).astype(np.float32)
-        self._residue()
-        self._atom_info()
-        self._virtual_atom()
-        self._bond()
-        self._angle()
-        self._dihedral_nb14()
-        self._lennard_jones()
-        self._exclude()
-        self._simple_constrain(config)
-
-    def _residue(self):
-        self.residue_numbers = len(self._system.residues)
-        self.residues = []
-        for residue in self._system.residues:
-            self.residues.append(len(residue))
-
-    def _atom_info(self):
-        self.mass = []
-        self.charge = []
-        for atom in self._system.atoms:
-            self.mass.append(atom.mass)
-            self.charge.append(atom.charge * CHARGE_UNIT)
-        self.mass = np.array(self.mass).astype(np.float32)
-        self.inverse_mass = 1.0 / self.mass
-        self.charge = np.array(self.charge).astype(np.float32)
-
-    def _virtual_atom(self):
-        virtual_atoms = []
-        for residue in self._system.residues:
-            if residue.name in ("WAT", "HOH"):
-                if len(residue.atoms) == 4:
-                    virtual_bond = residue.atoms[3].bonds[0]
-                    if residue.atoms[1].bonds[0].atom1 == residue.atoms[0] or residue.atoms[1].bonds[0].atom2 == \
-                            residue.atoms[0]:
-                        ho_bond = residue.atoms[1].bonds[0].type.req
-                        hh_bond = residue.atoms[1].bonds[1].type.req
-                    else:
-                        ho_bond = residue.atoms[1].bonds[1].type.req
-                        hh_bond = residue.atoms[1].bonds[0].type.req
-                    factor = virtual_bond.type.req / (4 * ho_bond * ho_bond - hh_bond * hh_bond) ** 0.5
-                    virtual_atoms.append([2, residue.atoms[3].idx, residue.atoms[0].idx,
-                                          residue.atoms[1].idx, residue.atoms[2].idx, factor, factor])
-                    self._system.bonds.remove(virtual_bond)
-        if virtual_atoms:
-            self.virtual_atoms = virtual_atoms
-
-    def _bond(self):
-        self.bond_numbers = len(self._system.bonds)
-        bond = []
-        for _bond in self._system.bonds:
-            bond.append([_bond.atom1.idx, _bond.atom2.idx, _bond.type.k, _bond.type.req])
-        bond = np.array(bond)
-        self.bond_atom_a = bond[:, 0].astype(np.int32)
-        self.bond_atom_b = bond[:, 1].astype(np.int32)
-        self.bond_k = bond[:, 2].astype(np.float32)
-        self.bond_r0 = bond[:, 3].astype(np.float32)
-
-    def _angle(self):
-        self.angle_numbers = len(self._system.angles)
-        angle = []
-        for _angle in self._system.angles:
-            angle.append([_angle.atom1.idx, _angle.atom2.idx, _angle.atom3.idx, _angle.type.k,
-                          _angle.type.utheteq.value_in_unit(pmd.unit.radian)])
-        angle = np.array(angle)
-        self.angle_atom_a = angle[:, 0].astype(np.int32)
-        self.angle_atom_b = angle[:, 1].astype(np.int32)
-        self.angle_atom_c = angle[:, 2].astype(np.int32)
-        self.angle_k = angle[:, 3].astype(np.float32)
-        self.angle_theta0 = angle[:, 4].astype(np.float32)
-
-    def _dihedral_nb14(self):
-        self.dihedral_numbers = len(self._system.dihedrals)
-        if self.dihedral_numbers:
-            self.dihedral_numbers = 0
-            dihedral = []
-            towrite = ""
-            for _dihedral in self._system.dihedrals:
-                if _dihedral.type.phi_k != 0:
-                    dihedral.append([_dihedral.atom1.idx, _dihedral.atom2.idx, _dihedral.atom3.idx, _dihedral.atom4.idx,
-                                     _dihedral.type.per, _dihedral.type.phi_k,
-                                     _dihedral.type.uphase.value_in_unit(pmd.unit.radian)])
-                    self.dihedral_numbers += 1
-            dihedral = np.array(dihedral)
-            self.dihedral_atom_a = dihedral[:, 0].astype(np.int32)
-            self.dihedral_atom_b = dihedral[:, 1].astype(np.int32)
-            self.dihedral_atom_c = dihedral[:, 2].astype(np.int32)
-            self.dihedral_atom_d = dihedral[:, 3].astype(np.int32)
-            self.dihedral_ipn = dihedral[:, 4].astype(np.float32)
-            self.dihedral_pn = dihedral[:, 4].astype(np.float32)
-            self.dihedral_pk = dihedral[:, 5].astype(np.float32)
-            self.dihedral_gamc = np.float32(np.cos(dihedral[:, 6]) * dihedral[:, 5])
-            self.dihedral_gams = np.float32(np.sin(dihedral[:, 6]) * dihedral[:, 5])
-
-            self.nb14_numbers = 0
-            nb14 = []
-            for _dihedral in self._system.dihedrals:
-                if _dihedral.type.scnb != 0 and _dihedral.type.scee != 0 and not _dihedral.ignore_end:
-                    nb14.append([_dihedral.atom1.idx, _dihedral.atom4.idx,
-                                 1.0 / _dihedral.type.scnb, 1.0 / _dihedral.type.scee])
-                    self.nb14_numbers += 1
-            self.nb14_atom_a = np.array(nb14)[:, 0].astype(np.int32)
-            self.nb14_atom_b = np.array(nb14)[:, 1].astype(np.int32)
-            self.nb14_lj_scale_factor = np.array(nb14)[:, 2].astype(np.float32)
-            self.nb14_cf_scale_factor = np.array(nb14)[:, 3].astype(np.float32)
-
-    def _lennard_jones(self):
-        LJ_depth = self._system.LJ_depth
-        LJ_radius = self._system.LJ_radius
-        self.atom_type_numbers = len(LJ_depth)
-        LJ_idx = []
-        for atom in self._system.atoms:
-            LJ_idx.append(atom.nb_idx - 1)
-
-        def getLJ_A(i, j):
-            return (np.sqrt(LJ_depth[i] * LJ_depth[j]) * ((LJ_radius[i] + LJ_radius[j])) ** 12)
-
-        def getLJ_B(i, j):
-            return (np.sqrt(LJ_depth[i] * LJ_depth[j]) * 2 * ((LJ_radius[i] + LJ_radius[j])) ** 6)
-
-        self.LJ_A = []
-        for i in range(self.atom_type_numbers):
-            temp = []
-            for j in range(i + 1):
-                temp.append(getLJ_A(i, j))
-            self.LJ_A.extend(temp)
-        self.LJ_B = []
-        for i in range(self.atom_type_numbers):
-            temp = []
-            for j in range(i + 1):
-                temp.append(getLJ_B(i, j))
-            self.LJ_B.extend(temp)
-        _LJ_idx = []
-        for i in range(self.atom_numbers):
-            _LJ_idx.append(LJ_idx[i])
-
-        self.LJ_A = np.float32(np.array(self.LJ_A) * 12.0)
-        self.LJ_B = np.float32(np.array(self.LJ_B) * 6.0)
-        self.LJ_type = np.array(_LJ_idx).astype(np.float32)
-
-    def _exclude(self):
-        self.excluded_atom_numbers = 0
-        self.excluded_atom = []
-        for atom in self._system.atoms:
-            temp = 0
-            exclusions_temp = atom.bond_partners + atom.angle_partners + \
-                              atom.dihedral_partners + atom.tortor_partners + atom.exclusion_partners
-            exclusions = []
-            for atom_e in exclusions_temp:
-                if (atom_e > atom):
-                    exclusions.append("%d" % atom_e.idx)
-                    temp += 1
-            exclusions.sort(key=lambda x: int(x))
-            self.excluded_atom.append(exclusions)
-
-    def _simple_constrain(self, config):
-        def add_bond_to_constrain_pair(constrain_mass, atom_mass, atom_a, atom_b, bond_r):
-            bond_pair = []
-            s = 0
-            for i in range(self.bond_numbers):
-                mass_a = atom_mass[atom_a[i]]
-                mass_b = atom_mass[atom_b[i]]
-                if (float(mass_a) < constrain_mass and mass_a > 0) or \
-                        (float(mass_b) < constrain_mass and mass_b > 0):
-                    constrain_k = \
-                        atom_mass[atom_a[i]] * atom_mass[atom_b[i]] / (atom_mass[atom_a[i]] + atom_mass[atom_b[i]])
-                    bond_pair.append([atom_a[i], atom_b[i], bond_r[i], constrain_k])
-                    s += 1
-            return bond_pair
-
-        def add_angle_to_constrain_pair(agnle_num, atom_a, atom_b, atom_c, theta, mass):
-            raise NotImplementedError
-
-        constrain_mass = 3.0 if "constrain_mass" not in vars(config) else float(config.constrain_mass)
-        bond_pair = add_bond_to_constrain_pair(constrain_mass, self.mass, self.bond_atom_a, self.bond_atom_b,
-                                               self.bond_r0)
-        angle_pair = []
-        self.volume = float(self.box_len[0] * self.box_len[1] * self.box_len[2])
-        half_exp_gamma_plus_half = 0.5 * (1. + self.exp_gamma)
-        if config.mode == "Minimization":
-            exp_gamma = 0.0
-        self.iteration_numbers = 25 if "iteration_numbers" not in vars(config) else config.iteration_numbers
-        step_length = 1 if "step_length" not in vars(config) else float(config.iteration_numbers)
-        extra_numbers = 0
-
-        self.dt_inverse = 1 / self.dt
-        constrain_pair_numbers = len(bond_pair) + len(angle_pair) + extra_numbers
-        self.system_freedom -= constrain_pair_numbers
-        self.constrain_pair = []
-        for i in range(len(bond_pair)):
-            self.constrain_pair.append(bond_pair[i])
-            self.constrain_pair[i][-1] = step_length / half_exp_gamma_plus_half \
-                                         * self.constrain_pair[i][-1]
diff --git a/MindSPONGE/mindsponge/python/loss/__init__.py b/MindSPONGE/mindsponge/python/loss/__init__.py
deleted file mode 100644
index ef5cc82b5147efe17b71f1e84ba8c797584879a9..0000000000000000000000000000000000000000
--- a/MindSPONGE/mindsponge/python/loss/__init__.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# Copyright 2021-2022 @ Shenzhen Bay Laboratory &
-#                       Peking University &
-#                       Huawei Technologies Co., Ltd
-#
-# This code is a part of MindSPONGE:
-# MindSpore Simulation Package tOwards Next Generation molecular modelling.
-#
-# MindSPONGE is open-source software based on the AI-framework:
-# MindSpore (https://www.mindspore.cn/)
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-""" loss init"""
-
-from .violation_loss import get_violation_loss
diff --git a/MindSPONGE/mindsponge/python/loss/violation_loss.py b/MindSPONGE/mindsponge/python/loss/violation_loss.py
deleted file mode 100644
index a2edce1f90c4243088a5e54ccd4c435e10926344..0000000000000000000000000000000000000000
--- a/MindSPONGE/mindsponge/python/loss/violation_loss.py
+++ /dev/null
@@ -1,100 +0,0 @@
-# Copyright 2021-2022 @ Shenzhen Bay Laboratory &
-#                       Peking University &
-#                       Huawei Technologies Co., Ltd
-#
-# This code is a part of MindSPONGE:
-# MindSpore Simulation Package tOwards Next Generation molecular modelling.
-#
-# MindSPONGE is open-source software based on the AI-framework:
-# MindSpore (https://www.mindspore.cn/)
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""violation loss calculation."""
-
-import numpy as np
-
-import mindspore as ms
-from mindspore import Tensor
-from mindspore.ops import operations as P
-
-from ..metrics.structure_violations import find_structural_violations
-from ..common import residue_constants
-from ..common.utils import get_pdb_info
-
-C_ONE_HOT = np.array([0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], np.float32)
-N_ONE_HOT = np.array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], np.float32)
-ATOMTYPE_RADIUS = np.array([1.55, 1.7, 1.7, 1.7, 1.52, 1.7, 1.7, 1.7, 1.52, 1.52, 1.8, 1.7, 1.7, 1.7, 1.55, 1.55, 1.52,
-                            1.52, 1.8, 1.7, 1.7, 1.7, 1.7, 1.55, 1.55, 1.55, 1.52, 1.52, 1.7, 1.55, 1.55, 1.52, 1.7,
-                            1.7, 1.7, 1.55, 1.52])
-DISTS_MASK_I = np.eye(14, 14)
-LOWER_BOUND, UPPER_BOUND, RESTYPE_ATOM14_BOUND_STDDEV = \
-    residue_constants.make_atom14_dists_bounds(overlap_tolerance=1.5, bond_length_tolerance_factor=12.0)
-
-VIOLATION_TOLERANCE_ACTOR = 12.0
-CLASH_OVERLAP_TOLERANCE = 1.5
-CYS_SG_IDX = 5
-
-
-def get_violation_loss(pdb_path=None, atom14_atom_exists=None, residue_index=None, residx_atom14_to_atom37=None,
-                       atom14_positions=None, aatype=None):
-    """calculate violations loss for a given pdb or pdb info
-    Args:
-        pdb_path: absolute path of pdb
-        atom14_atom_exists: weather atom exists in atom14 order from residue constants (ndarray or Tensor in float32)
-        residue_index: residue index from pdb (ndarray or Tensor in float32)
-        residx_atom14_to_atom37:map atom14 residue_index to atom37 (ndarray or Tensor in int32)
-        atom14_positions:atom positions in atom14 order (ndarray or Tensor in float32)
-        aatype:amino acid type (ndarray or Tensor in int32)
-
-    Returns:
-        pdb's violations loss
-    """
-    if pdb_path:
-        features = get_pdb_info(pdb_path)
-
-        atom14_atom_exists = Tensor(features.get("atom14_gt_exists")).astype(ms.float32)
-        residue_index = Tensor(features.get("residue_index")).astype(ms.float32)
-        residx_atom14_to_atom37 = Tensor(features.get("residx_atom14_to_atom37")).astype(ms.int32)
-        atom14_positions = Tensor(features.get("atom14_gt_positions")).astype(ms.float32)
-        aatype = Tensor(features.get("aatype")).astype(ms.int32)
-    else:
-        atom14_atom_exists = Tensor(atom14_atom_exists).astype(ms.float32) \
-            if isinstance(atom14_atom_exists, np.ndarray) else atom14_atom_exists
-        residue_index = Tensor(residue_index).astype(ms.float32) \
-            if isinstance(residue_index, np.ndarray) else residue_index
-        residx_atom14_to_atom37 = Tensor(residx_atom14_to_atom37).astype(ms.int32) \
-            if isinstance(residx_atom14_to_atom37, np.ndarray) else residx_atom14_to_atom37
-        atom14_positions = Tensor(atom14_positions).astype(ms.float32) \
-            if isinstance(atom14_positions, np.ndarray) else atom14_positions
-        aatype = Tensor(aatype).astype(ms.int32) if isinstance(aatype, np.ndarray) else aatype
-
-    lower_bound_t = Tensor(LOWER_BOUND, ms.float32)
-    upper_bound_t = Tensor(UPPER_BOUND, ms.float32)
-    atom_type_radius_t = Tensor(ATOMTYPE_RADIUS, ms.float32)
-    c_one_hot_t = Tensor(C_ONE_HOT, ms.int32)
-    n_one_hot_t = Tensor(N_ONE_HOT, ms.int32)
-    dists_mask_i_t = Tensor(DISTS_MASK_I, ms.int32)
-    cys_sg_idx_t = Tensor(CYS_SG_IDX, ms.int32)
-
-    (bonds_c_n_loss_mean, angles_ca_c_n_loss_mean, angles_c_n_ca_loss_mean, _, _, _, clashes_per_atom_loss_sum,
-     _, per_atom_loss_sum, _, total_per_residue_violations_mask) = \
-        find_structural_violations(atom14_atom_exists, residue_index, aatype, residx_atom14_to_atom37,
-                                   atom14_positions, VIOLATION_TOLERANCE_ACTOR,
-                                   CLASH_OVERLAP_TOLERANCE, lower_bound_t, upper_bound_t, atom_type_radius_t,
-                                   c_one_hot_t, n_one_hot_t, dists_mask_i_t, cys_sg_idx_t)
-    num_atoms = P.ReduceSum()(atom14_atom_exists.astype(ms.float32))
-    structure_violation_loss = bonds_c_n_loss_mean + angles_ca_c_n_loss_mean + angles_c_n_ca_loss_mean +\
-                               P.ReduceSum()(clashes_per_atom_loss_sum + per_atom_loss_sum) / (1e-6 + num_atoms)
-
-    return structure_violation_loss, total_per_residue_violations_mask
diff --git a/MindSPONGE/setup.py b/MindSPONGE/setup.py
index b927bc4e299901dcb26960601b8b2379872034e6..a38d925d97c7840d2a09c2131f461477cb2c133f 100644
--- a/MindSPONGE/setup.py
+++ b/MindSPONGE/setup.py
@@ -25,7 +25,7 @@ cur_dir = os.path.dirname(os.path.realpath(__file__))
 pkg_dir = os.path.join(cur_dir, 'build')
 package_name = os.getenv('SPONGE_PACKAGE_NAME').replace("\n", "")
 package_data = {
-    '': ["*.txt", "libs/*.so", "bin/*"]
+    '': ["*.txt", "libs/*.so", "bin/*", "*.mol2", "*.frcmod", "*.dat", "*.itp", "*.yaml"]
 }
 
 def read_version():