Notebook

SimpleMetrics¶

@Author: 吴炜坤 | 槐喆

@email：weikun.wu@xtalpi.com zhe.huai@xtalpi.com

In [1]:

# 初始化
from pyrosetta import pose_from_pdb, init
init()

PyRosetta-4 2021 [Rosetta PyRosetta4.conda.mac.cxx11thread.serialization.python37.Release 2021.31+release.c7009b3115c22daa9efe2805d9d1ebba08426a54 2021-08-07T10:04:12] retrieved from: http://www.pyrosetta.org
(C) Copyright Rosetta Commons Member Institutions. Created in JHU by Sergey Lyskov and PyRosetta Team.
core.init: {0} Checking for fconfig files in pwd and ./rosetta/flags
core.init: {0} Rosetta version: PyRosetta4.conda.mac.cxx11thread.serialization.python37.Release r292 2021.31+release.c7009b3115c c7009b3115c22daa9efe2805d9d1ebba08426a54 http://www.pyrosetta.org 2021-08-07T10:04:12
core.init: {0} command: PyRosetta -ex1 -ex2aro -database /opt/miniconda3/lib/python3.7/site-packages/pyrosetta/database
basic.random.init_random_generator: {0} 'RNG device' seed mode, using '/dev/urandom', seed=1792538074 seed_offset=0 real_seed=1792538074 thread_index=0
basic.random.init_random_generator: {0} RandomGenerator:init: Normal mode, seed=1792538074 RG_type=mt19937

1. SimpleMetrics简介¶

SimpleMetrics是2018发布一个可以快速地提取和分析结构特征/性质的组件。

(图片来源: 晶泰科技团队)

SimpleMetrics的计算内容:

RealMetric 这些metrics计算并返回实数。如二面角、相互作用能、总能量、RMSD、SASA、序列相似度、数据的后处理等。
StringMetric 这些metrics计算并返回字符。如氨基酸序列、二级结构、相互作用对，用于Pymol可视化的残基等。

SimpleMetrics根据计算方式分类:

Standard Metrics: 返回一种metric
Composite Metrics: 返回多种metrics
Per-Residue Metrics: 返回选择残基中每个残基的metric

1.1 如何定义一个SimpleMetrics¶

SimpleMetrics计算器的选择

首先需要弄清楚，希望得到什么特征的计算。比如我想计算残基的可及溶剂表面积(SASA)时，我应该考虑我想计算的是每个残基的SASA，还是整体的SASA。以此来选定特定的SimpleMetrics计算器。以下用PerResidueSasaMetric作为demo引出用法和一些需要特别注意的细节。

注意1: 大部分的SimpleMetrics都支持使用ResidueSelector来指定或限定计算的范围。

注意2: 大部分的SimpleMetrics都支持使用set_output_as_pdb_nums来指定输出的编号使用PDB还是Pose编号。

In [2]:

from pyrosetta.rosetta.core.select.residue_selector import ResidueIndexSelector
from pyrosetta.rosetta.core.simple_metrics.per_residue_metrics import PerResidueSasaMetric

# 读取pose
pose = pose_from_pdb('./data/6LZ9_H_L.pdb')

# 定义SimpleMetrics计算器
per_residue_sasa = PerResidueSasaMetric()
sasa_sel = ResidueIndexSelector('16L-25L')  # 比如计算L链上16-25号残基每个残基的sasa值
per_residue_sasa.set_residue_selector(sasa_sel)
per_residue_sasa.set_output_as_pdb_nums(True)  # 输出时

core.chemical.GlobalResidueTypeSet: {0} Finished initializing fa_standard residue type set.  Created 983 residue types
core.chemical.GlobalResidueTypeSet: {0} Total time to initialize 0.683192 seconds.
core.import_pose.import_pose: {0} File './data/6LZ9_H_L.pdb' automatically determined to be of type PDB
core.conformation.Conformation: {0} Found disulfide between residues 21 94
core.conformation.Conformation: {0} current variant for 21 CYS
core.conformation.Conformation: {0} current variant for 94 CYS
core.conformation.Conformation: {0} current variant for 21 CYD
core.conformation.Conformation: {0} current variant for 94 CYD
core.conformation.Conformation: {0} Found disulfide between residues 141 206
core.conformation.Conformation: {0} current variant for 141 CYS
core.conformation.Conformation: {0} current variant for 206 CYS
core.conformation.Conformation: {0} current variant for 141 CYD
core.conformation.Conformation: {0} current variant for 206 CYD

每个Metrics都有custom_type, 也就是Metrics的名称，是可独立命名的。另外计算器的计算结果索引需要有一个“唯一”的索引号。默认来说，每种计算器都有自己的索引名。比如PerResidueSasaMetric计算器，他的索引名即为“res_sasa”。但是这个索引名是可以在apply时被重新自定义的:

In [3]:

per_residue_sasa.apply(pose, prefix='per_', suffix='')  # 使用前缀"per_", 后缀为“”, 此时索引名为“per_res_sasa”

(图片来源: 晶泰科技团队)

以上，metrics的计算已经完成。 特别注意: 每次计算的索引号不得有重复，否则直接报错！

In [4]:

# 再跑一次会怎么样？:
per_residue_sasa.apply(pose, prefix='per_', suffix='')  # 使用前缀"per_", 后缀为“”, 此时索引名为“per_res_sasa”

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-4-6ebfee108fc1> in <module>
      1 # 再跑一次会怎么样？:
----> 2 per_residue_sasa.apply(pose, prefix='per_', suffix='')  # 使用前缀"per_", 后缀为“”, 此时索引名为“per_res_sasa”

RuntimeError: 

File: /Volumes/MacintoshHD3/benchmark/W.fujii.release/rosetta.Fujii.release/_commits_/main/source/src/core/simple_metrics/util.cc:282


SimpleMetric error! 
 The data of type PerResidueSasaMetric with data output tag per_res_sasa already exists! 
Please use the prefix/suffix settings or set a custom_type for the metric.
  See the documentation for more:
  https://www.rosettacommons.org/docs/latest/scripting_documentation/RosettaScripts/SimpleMetrics/SimpleMetrics#effective-use-of-simplemetrics.
 Note: If this was intentional, please set the override option to true in RunSimpleMetricsMover

In [5]:

# 加个后缀名:
per_residue_sasa.apply(pose, prefix='per_', suffix='_suf')  # 使用前缀"per_", 后缀为“”, 此时索引名为“per_res_sasa_suf”

SimpleMetricData数据的储存与提取

在pose中，数据会储存在SimpleMetricData中，因此我们需要调用 get_sm_data(pose)来获取pose中的sm cache data,然后在通过SimpleMetricData来获取具体类型的值，主要有以下几类:

get_real_metric_data()
get_string_metric_data()
get_per_residue_real_metric_data()
get_composite_real_metric_data()

注意: 提取pose中Metric的data.key为字典的key值。但是我们需要知道每种metric的key值名称，如rmsd, sasa, residue_count等,直接使用切片将其取出,如不适用切片，默认返回所有值。

In [6]:

from pyrosetta.rosetta.core.simple_metrics import get_sm_data
sm_data = get_sm_data(pose)

根据分类，PerResidueSasaMetric的计算结果属于real_metric_data:

In [7]:

per_real_metric = sm_data.get_per_residue_real_metric_data()
print(per_real_metric)

map_std_string_std_map_unsigned_long_double_std_less_unsigned_long_std_allocator_std_pair_const_unsigned_long_double_t{per_res_sasa: {134:36.1789, 135:40.0819, 136:158.19, 137:5.63578, 138:53.4908, 139:1.02469, 140:44.6698, 141:1.44319, 142:146.688, 143:5.70025, }, per_res_sasa_suf: {134:36.1789, 135:40.0819, 136:158.19, 137:5.63578, 138:53.4908, 139:1.02469, 140:44.6698, 141:1.44319, 142:146.688, 143:5.70025, }}

计算的结果被存在一个叫map_std_string_std_map_unsigned_long_double_std_less_unsigned_long_std_allocator_std_pair_const_unsigned_long_double_t类型（真长。。）的字典中。

In [8]:

# 通过索引名直接获取数据:
per_real_metric['per_res_sasa']

Out[8]:

map_unsigned_long_double{134: 36.1789, 135: 40.0819, 136: 158.19, 137: 5.63578, 138: 53.4908, 139: 1.02469, 140: 44.6698, 141: 1.44319, 142: 146.688, 143: 5.70025}

In [9]:

# 获取134号残基的SASA值。
per_real_metric['per_res_sasa'][134]

Out[9]:

36.17887576545817

2. SimpleMetrics的更多应用实例¶

为了方便大家加深理解，本章节再举几个相关的例子进行说明:

RealMetrics
StringMetrics
PerResidueRealMetrics
CompositeRealMetrics

In [10]:

#加载与SimpleMetrics调用有关的class.
from pyrosetta import pose_from_pdb, init
from pyrosetta.rosetta.core.simple_metrics.metrics import *
from pyrosetta.rosetta.core.simple_metrics.per_residue_metrics import *
from pyrosetta.rosetta.core.simple_metrics.composite_metrics import *
from pyrosetta.rosetta.core.simple_metrics import *
init()
#通过第五章ResidueSelector的学习，相信对肝细胞生长因子抗体(PDB:6LZ9)已经很熟悉了，这里依然采用这个抗体，并从PDB中读入生成pose对象。
pose = pose_from_pdb('./data/6LZ9_H_L.pdb')

PyRosetta-4 2021 [Rosetta PyRosetta4.conda.mac.cxx11thread.serialization.python37.Release 2021.31+release.c7009b3115c22daa9efe2805d9d1ebba08426a54 2021-08-07T10:04:12] retrieved from: http://www.pyrosetta.org
(C) Copyright Rosetta Commons Member Institutions. Created in JHU by Sergey Lyskov and PyRosetta Team.
core.init: {0} Checking for fconfig files in pwd and ./rosetta/flags
core.init: {0} Rosetta version: PyRosetta4.conda.mac.cxx11thread.serialization.python37.Release r292 2021.31+release.c7009b3115c c7009b3115c22daa9efe2805d9d1ebba08426a54 http://www.pyrosetta.org 2021-08-07T10:04:12
core.init: {0} command: PyRosetta -ex1 -ex2aro -database /opt/miniconda3/lib/python3.7/site-packages/pyrosetta/database
basic.random.init_random_generator: {0} 'RNG device' seed mode, using '/dev/urandom', seed=-2065888904 seed_offset=0 real_seed=-2065888904 thread_index=0
basic.random.init_random_generator: {0} RandomGenerator:init: Normal mode, seed=-2065888904 RG_type=mt19937
core.import_pose.import_pose: {0} File './data/6LZ9_H_L.pdb' automatically determined to be of type PDB
core.conformation.Conformation: {0} Found disulfide between residues 21 94
core.conformation.Conformation: {0} current variant for 21 CYS
core.conformation.Conformation: {0} current variant for 94 CYS
core.conformation.Conformation: {0} current variant for 21 CYD
core.conformation.Conformation: {0} current variant for 94 CYD
core.conformation.Conformation: {0} Found disulfide between residues 141 206
core.conformation.Conformation: {0} current variant for 141 CYS
core.conformation.Conformation: {0} current variant for 206 CYS
core.conformation.Conformation: {0} current variant for 141 CYD
core.conformation.Conformation: {0} current variant for 206 CYD

(图片来源: 晶泰科技团队)

2.1 RealMetrics¶

RealMetrics可以很方便地计算选择残基的某一物理量，并通过get_real_metric_data()返回实数。

2.1.1 SelectedResidueCountMetric¶

这个Metric用于计算被ResidueSelector选择残基的数量。

In [11]:

# 比如选择抗体的重链，PDB链号为"H":
from pyrosetta.rosetta.core.select.residue_selector import ChainSelector
select_heavy_chain = ChainSelector('H')

(图片来源: 晶泰科技团队)

In [12]:

#下边将计算选择残基的数量，这里我们将前边提到的SimpleMetrics的名称和SimpleMetrics的前缀/后缀也分别简要说明。
# expample1 SimpleMetrics的名称
num = SelectedResidueCountMetric()
name = 'H_chian'
num.set_custom_type(name)  #设置名称
num.set_residue_selector(select_heavy_chain)
num.apply(pose) 

In [13]:

from pyrosetta.rosetta.core.simple_metrics import get_sm_data
sm_data = get_sm_data(pose)
real_metric = sm_data.get_real_metric_data()
real_metric['H_chian_selection_count']

Out[13]:

118.0

In [14]:

# expample2 SimpleMetrics的前缀/后缀
num = SelectedResidueCountMetric()
num.set_residue_selector(select_heavy_chain)
prefix = 'H_chain_'
num.apply(pose, prefix)

In [15]:

real_metric = sm_data.get_real_metric_data()
real_metric['H_chian_selection_count']

Out[15]:

118.0

再次提醒说明
通过比较可以发现，SimpleMetrics的名称和SimpleMetrics的前缀/后缀在apply时会有所差别，当然名称和前缀/后缀也可以同时使用。
每次运行这些名字都会保存，重复运行同一个Metric要指定不同的名字，否则会报错。

In [16]:

# 再来看抗体的残基基本信息:
print(pose.pdb_info())

PDB file name: ./data/6LZ9_H_L.pdb
 Pose Range  Chain    PDB Range  |   #Residues         #Atoms

0001 -- 0081    H 0002  -- 0082  |   0081 residues;    01283 atoms
0082 -- 0082    H 0082A -- 0082A |   0001 residues;    00011 atoms
0083 -- 0083    H 0082B -- 0082B |   0001 residues;    00011 atoms
0084 -- 0084    H 0082C -- 0082C |   0001 residues;    00019 atoms
0085 -- 0102    H 0083  -- 0100  |   0018 residues;    00271 atoms
0103 -- 0103    H 0100A -- 0100A |   0001 residues;    00010 atoms
0104 -- 0104    H 0100B -- 0100B |   0001 residues;    00021 atoms
0105 -- 0105    H 0100C -- 0100C |   0001 residues;    00021 atoms
0106 -- 0106    H 0100D -- 0100D |   0001 residues;    00010 atoms
0107 -- 0107    H 0100E -- 0100E |   0001 residues;    00017 atoms
0108 -- 0118    H 0101  -- 0111  |   0011 residues;    00160 atoms
0119 -- 0223    L 0001  -- 0105  |   0105 residues;    01600 atoms
                           TOTAL |   0223 residues;    03434 atoms

结果解读
抗体中含有H和L两条链，分别为1-118和119-223。SelectedResidueCountMetric计算结果与此一致。

2.1.2 SasaMetric¶

计算选择残基的溶剂可及表面积。

In [17]:

# 比如计算L链的溶剂可及表面积。
select_light_chain = ChainSelector('L')
sasa = SasaMetric(select_light_chain)
prefix = 'L_chain_'
sasa.apply(pose,prefix)

(图片来源: 晶泰科技团队)

In [18]:

real_metric = sm_data.get_real_metric_data()
real_metric['L_chain_sasa']

Out[18]:

4854.614232155741

2.2 StringMetrics¶

这个Metrics计算得到的结果通过get_string_metric_data来返回字符串。

2.2.1 SelectedResiduesMetric（非常实用！）¶

返回选择残基的poseID或PDBID, 可以快速获取ResidueSelector中到底都选择了哪些残基的列表！

In [19]:

#指定一段残基来给出相应的ID
from pyrosetta.rosetta.core.select.residue_selector import ResidueIndexSelector
range_selector = ResidueIndexSelector('18H-24H')
selected = range_selector.apply(pose)

In [20]:

selected_index = SelectedResiduesMetric(range_selector)
selected_index.set_output_in_rosetta_num(True) # True: poseID, False: PDBID
prefix = 'ID_'
selected_index.apply(pose,prefix)

In [21]:

from pyrosetta.rosetta.core.simple_metrics import get_sm_data
sm_data = get_sm_data(pose)
string_metric = sm_data.get_string_metric_data()
string_metric['ID_selection']

Out[21]:

'17,18,19,20,21,22,23'

In [22]:

#验证SelectedResiduesMetric返回值是否正确:
index_list = [index+1 for index, i in enumerate(selected) if i == 1]
print(index_list)

[17, 18, 19, 20, 21, 22, 23]

2.2.2 SelectedResiduesPyMOLMetric¶

返回pymol命令: 粘贴复制后可以用于在Pymol中选择selector中的残基。
这个Metric在第五章ResidueSelectors中得到了广泛使用，可以一目了然的知道选择器选择了哪些残基。

In [23]:

index_selector = ResidueIndexSelector('41H,43H,45H')
pymol_selected = SelectedResiduesPyMOLMetric()
pymol_selected.set_residue_selector(index_selector)
prefix = 'index_select_'
pymol_selected.apply(pose,prefix)

In [24]:

string_metric = sm_data.get_string_metric_data()
string_metric['index_select_pymol_selection']

Out[24]:

'select rosetta_sele, (chain H and resid 41,43,45)'

(图片来源: 晶泰科技团队)

2.3 PerResidueRealMetrics¶

这个Metrics与RealMetrics功能相同，但计算得到的是每个残基的物理量。
所有的PerResidueRealMetric都可以指定输出类型为PDBID或者是poseID 利用选项output_as_pdb_nums即可。

2.3.1 PerResidueEnergyMetric¶

计算每个残基的总能(或某一个能量类型如fa_atr, fa_rep, fa_elec等)，返回能量的值是经过权重。通过分解总能得到每个氨基酸的能量。

这个计算器有两种使用的模式:

Example1: 残基能量分解
Example2: 两个Pose之间的每个残基能量对比差计算

In [25]:

#expample1 计算H链11到20号残基的fa_rep类型能量
range_sel = ResidueIndexSelector('11H-20H')

In [26]:

from pyrosetta.rosetta.core.scoring import fa_atr, fa_rep, fa_elec
from pyrosetta import create_score_function
per_residue_energy = PerResidueEnergyMetric()
per_residue_energy.set_residue_selector(range_sel) 
per_residue_energy.set_scoretype(fa_rep)
scorefxn = create_score_function('ref2015')
per_residue_energy.set_scorefunction(scorefxn)
per_residue_energy.apply(pose,'per_')

core.scoring.etable: {0} Starting energy table calculation
core.scoring.etable: {0} smooth_etable: changing atr/rep split to bottom of energy well
core.scoring.etable: {0} smooth_etable: spline smoothing lj etables (maxdis = 6)
core.scoring.etable: {0} smooth_etable: spline smoothing solvation etables (max_dis = 6)
core.scoring.etable: {0} Finished calculating energy tables.
basic.io.database: {0} Database file opened: scoring/score_functions/hbonds/ref2015_params/HBPoly1D.csv
basic.io.database: {0} Database file opened: scoring/score_functions/hbonds/ref2015_params/HBFadeIntervals.csv
basic.io.database: {0} Database file opened: scoring/score_functions/hbonds/ref2015_params/HBEval.csv
basic.io.database: {0} Database file opened: scoring/score_functions/hbonds/ref2015_params/DonStrength.csv
basic.io.database: {0} Database file opened: scoring/score_functions/hbonds/ref2015_params/AccStrength.csv
basic.io.database: {0} Database file opened: scoring/score_functions/rama/fd/all.ramaProb
basic.io.database: {0} Database file opened: scoring/score_functions/rama/fd/prepro.ramaProb
basic.io.database: {0} Database file opened: scoring/score_functions/omega/omega_ppdep.all.txt
basic.io.database: {0} Database file opened: scoring/score_functions/omega/omega_ppdep.gly.txt
basic.io.database: {0} Database file opened: scoring/score_functions/omega/omega_ppdep.pro.txt
basic.io.database: {0} Database file opened: scoring/score_functions/omega/omega_ppdep.valile.txt
basic.io.database: {0} Database file opened: scoring/score_functions/P_AA_pp/P_AA
basic.io.database: {0} Database file opened: scoring/score_functions/P_AA_pp/P_AA_n
core.scoring.P_AA: {0} shapovalov_lib::shap_p_aa_pp_smooth_level of 1( aka low_smooth ) got activated.
basic.io.database: {0} Database file opened: scoring/score_functions/P_AA_pp/shapovalov/10deg/kappa131/a20.prop
basic.io.database: {0} Database file opened: scoring/score_functions/elec_cp_reps.dat
core.scoring.elec.util: {0} Read 40 countpair representative atoms
core.pack.dunbrack.RotamerLibrary: {0} shapovalov_lib_fixes_enable option is true.
core.pack.dunbrack.RotamerLibrary: {0} shapovalov_lib::shap_dun10_smooth_level of 1( aka lowest_smooth ) got activated.
core.pack.dunbrack.RotamerLibrary: {0} Binary rotamer library selected: /opt/miniconda3/lib/python3.7/site-packages/pyrosetta/database/rotamer/shapovalov/StpDwn_0-0-0/Dunbrack10.lib.bin
core.pack.dunbrack.RotamerLibrary: {0} Using Dunbrack library binary file '/opt/miniconda3/lib/python3.7/site-packages/pyrosetta/database/rotamer/shapovalov/StpDwn_0-0-0/Dunbrack10.lib.bin'.
core.pack.dunbrack.RotamerLibrary: {0} Dunbrack 2010 library took 0.17968 seconds to load from binary

In [27]:

per_real_metric = sm_data.get_per_residue_real_metric_data()
per_real_metric['per_res_energy']

Out[27]:

map_unsigned_long_double{10: 0.141929, 11: 0.917974, 12: 1.55712, 13: 0.765261, 14: 0.137776, 15: 2.04166, 16: 0.549227, 17: 3.96559, 18: 0.523508, 19: 3.02056}

In [28]:

#expample2 计算两个pose间每个残基的能量差,这里需要引入另一个pose
com_pose = pose_from_pdb('./data/3K2U_H_L.pdb')
#3K2U与6LZ9的结构是非常相似的

core.import_pose.import_pose: {0} File './data/3K2U_H_L.pdb' automatically determined to be of type PDB
core.conformation.Conformation: {0} [ WARNING ] missing heavyatom:  OXT on residue THR:CtermProteinFull 121
core.conformation.Conformation: {0} [ WARNING ] missing heavyatom:  OXT on residue ARG:CtermProteinFull 229
core.conformation.Conformation: {0} Found disulfide between residues 22 96
core.conformation.Conformation: {0} current variant for 22 CYS
core.conformation.Conformation: {0} current variant for 96 CYS
core.conformation.Conformation: {0} current variant for 22 CYD
core.conformation.Conformation: {0} current variant for 96 CYD
core.conformation.Conformation: {0} Found disulfide between residues 144 209
core.conformation.Conformation: {0} current variant for 144 CYS
core.conformation.Conformation: {0} current variant for 209 CYS
core.conformation.Conformation: {0} current variant for 144 CYD
core.conformation.Conformation: {0} current variant for 209 CYD

(图片来源: 晶泰科技团队)

In [29]:

per_residue_energy = PerResidueEnergyMetric()
per_residue_energy.set_residue_selector(range_sel) 
per_residue_energy.set_scoretype(fa_rep)
scorefxn = create_score_function('ref2015')
per_residue_energy.set_scorefunction(scorefxn)
per_residue_energy.set_comparison_pose(com_pose)  # <-主要变化的设置在这里;
per_residue_energy.apply(pose,'com_pre_')

In [30]:

per_real_metric = sm_data.get_per_residue_real_metric_data()
per_real_metric['com_pre_res_energy']

Out[30]:

map_unsigned_long_double{10: 0.0422257, 11: 0.0217457, 12: -1.09715, 13: -0.255306, 14: -1.16531, 15: 1.88455, 16: 0.385523, 17: 3.58768, 18: -1.32255, 19: 2.705}

2.4 Composite Metrics¶

同时返回多个物理量，并通过get_composite_real_metric_data来返回实数。

2.4.1 CompositeEnergyMetric¶

计算总能量中每一项能量值，功能与TotalEnergyMetric部分重合，但是比其方便，可以一次输出所有的值。

In [31]:

# 计算两个pose的H链30到50号残基的各项能量差
from pyrosetta.rosetta.core.simple_metrics.composite_metrics import CompositeEnergyMetric
composite_sel = ResidueIndexSelector('30H-50H')
composite_energy = CompositeEnergyMetric(composite_sel)
composite_energy.set_comparison_pose(com_pose) #如果不指定比较的pose，仅计算pose的各项能量
scorefxn = create_score_function('ref2015')
composite_energy.set_scorefunction(scorefxn)
composite_energy.apply(pose,'30H-50H_')

In [32]:

from pyrosetta.rosetta.core.simple_metrics import get_sm_data
sm_data = get_sm_data(pose)
composite_real_metric = sm_data.get_composite_real_metric_data()
composite_real_metric['30H-50H_composite_energy']

Out[32]:

map_std_string_double{dslf_fa13: 0, fa_atr: -3.88672, fa_dun: -2.83299, fa_elec: 1.73887, fa_intra_rep: -0.0201983, fa_intra_sol_xover4: -0.675227, fa_rep: -12.926, fa_sol: 7.53877, hbond_bb_sc: 0.0272416, hbond_lr_bb: -0.36422, hbond_sc: 1.24952, hbond_sr_bb: 0.455738, lk_ball_wtd: -1.62749, omega: 3.88765, p_aa_pp: -0.0911932, pro_close: -3.84744, rama_prepro: 0.450053, ref: -1.64251, yhh_planarity: 1.2478e-07}