PSP - MetaPredict 预测蛋白质序列的内源性无序区域 (IDRs)

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MetaPredict 算法简介：

内源性无序区域（Intrinsically Disordered Regions，即IDRs）在所有生命领域中都普遍存在，并发挥着各种功能作用。虽然折叠结构域通常可以用一个三维结构来很好地描述，但是 IDRs 存在于一系列相互转化的状态中，称为集合体。这种结构异质性意味着 IDRs 在 PDB 中大部分缺失，导致缺乏从序列预测集合体构象特性的计算方法。在这里，我们结合了合理的序列设计、大规模的分子模拟和深度学习，开发 ALBATROSS，一个用于从序列预测 IDR 集合体尺寸的深度学习模型。ALBATROSS 能够瞬间预测蛋白质组范围内的集合体平均特性。ALBATROSS 轻量、易用，既可以作为一个本地安装的软件包，也可以作为一个点击式的云端界面。我们首先通过检验IDRs中序列-集合体关系的泛化性，来展示我们的预测器的适用性。然后，我们利用 ALBATROSS 的高通量特性，来表征 IDRs 在蛋白质组内外的新出现的生物物理行为。

使用工具 metapredict：

• GitHub：metapredict: A machine learning-based tool for predicting protein disorder.

• 使用文档：https://metapredict.readthedocs.io/en/latest/

Paper：Direct prediction of intrinsically disordered protein conformational properties from sequence

• 更新时间 2023-05-28

在线接口：https://metapredict.net/

参考的测试文档，来自 @盼盼： https://wiki.biomap-int.com/pages/viewpage.action?pageId=100631778

1. 工程配置

测试 T1157s1_A1029.fasta，来自于 CASP15 ：

>A
DRVRALRRETVEMFYYGFDNYMKVAFPEDELRPVSCTPLTRDLKNPRNFELNDVLGNYSLTLIDSLSTLAILASAPAEDSGTGPKALRDFQDGVAALVEQYGDGRPGPSGVGRRARGFDLDSKVQVFETVIRGVGGLLSAHLFAIGALPITGYQPLRQEDDLFNPPPIPWPNGFTYDGQLLRLALDLAQRLLPAFYTKTGLPYPRVNLRHGIPFYVNSPLHEDPPAKGTTEGPPEITETCSAGAGSLVLEFTVLSRLTGDPRFEQAAKRAFWAVWYRKSQIGLIGAGVDAEQGHWIGTYSVIGAGADSFFEYALKSHILLSGHALPNQTHPSPLHKDVNWMDPNTLFEPLSDAENSAESFLEAWHHAHAAIKRHLYSEREHPHYDNVNLWTGSLVSHWVDSLGAYYSGLLVLAGEVDEAIETNLLYAAIWTRYAALPERWSLREKTVEGGLGWWPLRPEFIESTYHLYRATKDPWYLYVGEMVLRDITRRCWTPCGWAGLQNVLSGEKSDRMESFFLGETTKYMYLLFDDDHPLNKLDASFVFTTEGHPLILPKPKSARRSRNSPRSSQKALTVYQGEGFTNSCPPRPSITPLSGSVIAARDDIYHPARMVDLHLLTTSKHALDGGQMSGQHMAKSNYTLYPWTLPPELLPSNGTCAKVYQPHEVTLEFASNTQQVLGGSAFNFMLSGQNLERLSTDRIRVLSLSGLKITLQLVEEGEREWRVTKLNGIPLGRDEYVVINRAILGDVSDPRFNLVRDPVIAKLQQLHQVNLLDDTTTEEHPDNLDTLDTASAIDLPQDQSSDSEVPDPANLSALLPDLSSFVKSLFARLSNLTSPSPDPSSNLPLNVVINQTAILPTGIGAAPLPPAASNSPSGAPIPVFGPVPESLFPWKTIYAAGEACAGPLPDSAPRENQVILIRRGGCSFSDKLANIPAFTPSEESLQLVVVVSDDEHEGQSGLVRPLLDEIQHTPGGMPRRHPIAMVMVGGGETVYQQLSVASAIGIQRRYYIESSGVKVKNIIVDDGDGGVDG

安装 Python 包：

 pip install metapredict==2.61

import metapredict as meta

2. 函数调用

2.1 核心函数 Predict Disorder Batch

测试，计算 Residue Disorder 的概率值0~1，值越大表示越可能是 Disorder 位点，阈值取 0.5，映射成0、1二值化，1 表示 disorder，0 表示 fold。

def predict_batch(seq_list):
    if not isinstance(seq_list, list):
        seq_list = [seq_list]

    output = meta.predict_disorder_batch(seq_list)
    assert len(seq_list) == len(output)
    res_list = []
    for sample in output:
        sample_disorder = sample[1]
        print(f"disorder range: {
      
      np.min(sample_disorder)}~{
      
      np.max(sample_disorder)}")
        sample_disorder_idx = list(np.where(sample_disorder > 0.5, 1, 0))
        print(f"sample_disorder_idx: {
      
      sample_disorder_idx}")

        # 获取 disorder 区间
        d_list, tmp_list = [], []
        for i, v in enumerate(sample_disorder_idx):
            if v == 1:  # 无序
                tmp_list.append(i)
            else:
                if tmp_list:
                    d_list.append(copy.copy(tmp_list))
                    tmp_list = []
        domain = []
        for r in d_list:
            if (r[-1] - r[0]) >= 2:
                domain.append([r[0], r[-1]])
        # seq, disorder_idx, domain
        res_list.append([sample[0], sample_disorder_idx, domain])
    return res_list

其中，阈值 0.5，来自于算法建议值。

Altering the disorder theshhold - To alter the disorder threshold, simply set disorder_threshold=my_value where my_value is a float. The higher the threshold value, the more conservative metapredict will be for designating a region as disordered. Default = 0.5 (V2) and 0.42 (legacy).

输出：

disorder range: 0.0~0.9868000149726868
seq:
DRVRALRRETVEMFYYGFDNYMKVAFPEDELRPVSCTPLTRDLKNPRNFELNDVLGNYSLTLIDSLSTLAILASAPAEDSGTGPKALRDFQDGVAALVEQYGDGRPGPSGVGRRARGFDLDSKVQVFETVIRGVGGLLSAHLFAIGALPITGYQPLRQEDDLFNPPPIPWPNGFTYDGQLLRLALDLAQRLLPAFYTKTGLPYPRVNLRHGIPFYVNSPLHEDPPAKGTTEGPPEITETCSAGAGSLVLEFTVLSRLTGDPRFEQAAKRAFWAVWYRKSQIGLIGAGVDAEQGHWIGTYSVIGAGADSFFEYALKSHILLSGHALPNQTHPSPLHKDVNWMDPNTLFEPLSDAENSAESFLEAWHHAHAAIKRHLYSEREHPHYDNVNLWTGSLVSHWVDSLGAYYSGLLVLAGEVDEAIETNLLYAAIWTRYAALPERWSLREKTVEGGLGWWPLRPEFIESTYHLYRATKDPWYLYVGEMVLRDITRRCWTPCGWAGLQNVLSGEKSDRMESFFLGETTKYMYLLFDDDHPLNKLDASFVFTTEGHPLILPKPKSARRSRNSPRSSQKALTVYQGEGFTNSCPPRPSITPLSGSVIAARDDIYHPARMVDLHLLTTSKHALDGGQMSGQHMAKSNYTLYPWTLPPELLPSNGTCAKVYQPHEVTLEFASNTQQVLGGSAFNFMLSGQNLERLSTDRIRVLSLSGLKITLQLVEEGEREWRVTKLNGIPLGRDEYVVINRAILGDVSDPRFNLVRDPVIAKLQQLHQVNLLDDTTTEEHPDNLDTLDTASAIDLPQDQSSDSEVPDPANLSALLPDLSSFVKSLFARLSNLTSPSPDPSSNLPLNVVINQTAILPTGIGAAPLPPAASNSPSGAPIPVFGPVPESLFPWKTIYAAGEACAGPLPDSAPRENQVILIRRGGCSFSDKLANIPAFTPSEESLQLVVVVSDDEHEGQSGLVRPLLDEIQHTPGGMPRRHPIAMVMVGGGETVYQQLSVASAIGIQRRYYIESSGVKVKNIIVDDGDGGVDG
disorder list:
[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
disordered domains:
[[224, 230], [553, 570], [772, 810], [832, 880]]

2.2 Disorder Domains (Official)

predict_disorder_domains 的 disorder 预测值与 predict_disorder_batch 一致，只是选择 domains 的条件不同，范围更大。

源码：

output = meta.predict_disorder_domains(seq)
print(output)
print(f"disorder: {output.disorder}")
print(f"disordered_domain_boundaries: {output.disordered_domain_boundaries}")
for boundary in output.disordered_domain_boundaries:
    s = boundary[0]
    e = boundary[1]
    print(f"disorder: {np.min(output.disorder[s:e])} ~ {np.max(output.disorder[s:e])}")
print(f"folded_domain_boundaries: {output.folded_domain_boundaries}")
print(f"disordered_domains: {output.disordered_domains}")
print(f"folded_domains: {output.folded_domains}")

输出：

DisorderObject for sequence with 1029 residues, 2 IDRs, and 3 folded domains
Available dot variables are:
  .sequence
  .disorder
  .disordered_domain_boundaries
  .folded_domain_boundaries
  .disordered_domains
  .folded_domains

disorder: [0.5593 0.5207 0.4646 ... 0.7331 0.7067 0.6378]
disordered_domain_boundaries: [[553, 571], [772, 880]]
disorder: 0.5241 ~ 0.854
disorder: 0.2163 ~ 0.9868
folded_domain_boundaries: [[0, 553], [571, 772], [880, 1029]]
disordered_domains: ['KPKSARRSRNSPRSSQKA', 'DDTTTEEHPDNLDTLDTASAIDLPQDQSSDSEVPDPANLSALLPDLSSFVKSLFARLSNLTSPSPDPSSNLPLNVVINQTAILPTGIGAAPLPPAASNSPSGAPIPVF']
folded_domains: ['DRVRALRRETVEMFYYGFDNYMKVAFPEDELRPVSCTPLTRDLKNPRNFELNDVLGNYSLTLIDSLSTLAILASAPAEDSGTGPKALRDFQDGVAALVEQYGDGRPGPSGVGRRARGFDLDSKVQVFETVIRGVGGLLSAHLFAIGALPITGYQPLRQEDDLFNPPPIPWPNGFTYDGQLLRLALDLAQRLLPAFYTKTGLPYPRVNLRHGIPFYVNSPLHEDPPAKGTTEGPPEITETCSAGAGSLVLEFTVLSRLTGDPRFEQAAKRAFWAVWYRKSQIGLIGAGVDAEQGHWIGTYSVIGAGADSFFEYALKSHILLSGHALPNQTHPSPLHKDVNWMDPNTLFEPLSDAENSAESFLEAWHHAHAAIKRHLYSEREHPHYDNVNLWTGSLVSHWVDSLGAYYSGLLVLAGEVDEAIETNLLYAAIWTRYAALPERWSLREKTVEGGLGWWPLRPEFIESTYHLYRATKDPWYLYVGEMVLRDITRRCWTPCGWAGLQNVLSGEKSDRMESFFLGETTKYMYLLFDDDHPLNKLDASFVFTTEGHPLILP', 'LTVYQGEGFTNSCPPRPSITPLSGSVIAARDDIYHPARMVDLHLLTTSKHALDGGQMSGQHMAKSNYTLYPWTLPPELLPSNGTCAKVYQPHEVTLEFASNTQQVLGGSAFNFMLSGQNLERLSTDRIRVLSLSGLKITLQLVEEGEREWRVTKLNGIPLGRDEYVVINRAILGDVSDPRFNLVRDPVIAKLQQLHQVNLL', 'GPVPESLFPWKTIYAAGEACAGPLPDSAPRENQVILIRRGGCSFSDKLANIPAFTPSEESLQLVVVVSDDEHEGQSGLVRPLLDEIQHTPGGMPRRHPIAMVMVGGGETVYQQLSVASAIGIQRRYYIESSGVKVKNIIVDDGDGGVDG']

2.3 Predict pLDDT

metapredict 支持预测 Residue 的 pLDDT 值，用于评估序列的质量。

源码：

output = meta.predict_pLDDT(seq)
print(f"mean_plddt: {np.mean(output)}")

2.4 绘图 Graph Disorder

直接调用 graph_disorder，即可绘图：

meta.graph_disorder(seq, pLDDT_scores=True)

Disorder Scores 与 pLDDT 成负相关，绘图如下：

3. 测试结构

使用 ChimeraX 测试结构，命令脚本如下：

@classmethod
def get_chimerax_select_cmd(cls, seq_list, mod_num="1"):
    """
    [kaɪˈmɪrə]
    select #1:553-571 #1:772-880
    """
    res_list = cls.predict_batch(seq_list)
    r_str_list = []
    for res in res_list:
        domains = res[2]
        c_list = ["select"]
        for domain in domains:
            c_str = f"#{
      
      mod_num}:{
      
      domain[0]}-{
      
      domain[1]}"
            c_list.append(c_str)
        r_str = " ".join(c_list)
        r_str_list.append(r_str)
    return r_str_list

输出：

ChimeraX: select #1:224-230 #1:553-570 #1:772-810 #1:832-880

测试结构，绿色高亮就是无序区：

其他

参考：

• Paper - Direct Prediction of Intrinsically Disordered Protein Conformational Properties From Sequence

• metapredict online (v2.3)

• chimerax-example-scripts-commands

关于 ChatGPT 的翻译 Prompt：

您好，如果你准备好了，请回答“是”，以下是一段蛋白质相关的段落，你作为一个生物学家，帮忙翻译成中文。"[需要翻译的段落]"

源码如下：

#!/usr/bin/env python
# -- coding: utf-8 --
"""
Copyright (c) 2022. All rights reserved.
Created by C. L. Wang on 2023/6/29
"""
import copy
import os

import metapredict as meta
import numpy as np

from protein_utils.seq_utils import get_seq_from_fasta
from root_dir import DATA_DIR


class SeqIdrPredictor(object):
    """
    序列的 IDRs 区域预测
    pip install metapredict==2.61
    """
    def __init__(self):
        pass

    @staticmethod
    def predict_batch(seq_list):
        """
        核心函数
        """
        if not isinstance(seq_list, list):
            seq_list = [seq_list]

        output = meta.predict_disorder_batch(seq_list)
        assert len(seq_list) == len(output)
        res_list = []
        for sample in output:
            sample_disorder = sample[1]
            print(f"disorder range: {
      
      np.min(sample_disorder)}~{
      
      np.max(sample_disorder)}")
            sample_disorder_idx = list(np.where(sample_disorder > 0.5, 1, 0))

            # 获取 disorder 区间
            d_list, tmp_list = [], []
            for i, v in enumerate(sample_disorder_idx):
                if v == 1:  # 无序
                    tmp_list.append(i)
                else:
                    if tmp_list:
                        d_list.append(copy.copy(tmp_list))
                        tmp_list = []
            domains = []
            for r in d_list:
                if (r[-1] - r[0]) >= 2:
                    domains.append([r[0], r[-1]])
            # seq, disorder_idx, domain
            res_list.append([sample[0], sample_disorder_idx, domains])
        return res_list

    @classmethod
    def get_chimerax_select_cmd(cls, seq_list, mod_num="1"):
        """
        [kaɪˈmɪrə]
        select #1:553-571 #1:772-880
        """
        res_list = cls.predict_batch(seq_list)
        r_str_list = []
        for res in res_list:
            domains = res[2]
            c_list = ["select"]
            for domain in domains:
                c_str = f"#{
      
      mod_num}:{
      
      domain[0]}-{
      
      domain[1]}"
                c_list.append(c_str)
            r_str = " ".join(c_list)
            r_str_list.append(r_str)
        return r_str_list

    @staticmethod
    def predict_disorder_domains(seq, is_print=False):
        output = meta.predict_disorder_domains(seq)
        if is_print:
            output = meta.predict_disorder_domains(seq)
            print(output)
            print(f"disorder: {
      
      output.disorder}")
            print(f"disordered_domain_boundaries: {
      
      output.disordered_domain_boundaries}")
            for boundary in output.disordered_domain_boundaries:
                s = boundary[0]
                e = boundary[1]
                print(f"disorder: {
      
      np.min(output.disorder[s:e])} ~ {
      
      np.max(output.disorder[s:e])}")
            print(f"folded_domain_boundaries: {
      
      output.folded_domain_boundaries}")
            print(f"disordered_domains: {
      
      output.disordered_domains}")
            print(f"folded_domains: {
      
      output.folded_domains}")
        return output


def main():
    fasta_dir = os.path.join(DATA_DIR, "CASP15-Monomer-Targets-56", "fasta")
    fasta_path = os.path.join(fasta_dir, "T1157s1_A1029.fasta")
    seq = get_seq_from_fasta(fasta_path)[0]
    sip = SeqIdrPredictor()
    res_list = sip.predict_batch(seq)
    print(f"seq:\n{
      
      res_list[0][0]}")
    print(f"disorder list:\n{
      
      res_list[0][1]}")
    print(f"disordered domains:\n{
      
      res_list[0][2]}")

    sip.predict_disorder_domains(seq, is_print=True)

    output = meta.predict_pLDDT(seq)
    print(f"mean_plddt: {
      
      np.mean(output)}")
    meta.graph_disorder(seq, pLDDT_scores=True)

    r_str_list = sip.get_chimerax_select_cmd(seq)
    print(f"ChimeraX: {
      
      r_str_list[0]}")


if __name__ == '__main__':
    main()