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6th-streamlit-gmxtoolbox.py
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6th-streamlit-gmxtoolbox.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 14 16:15:48 2024
@author: dozeduck
"""
# import getopt
# import sys
import re
import os
import pandas as pd
import mimetypes
# import plotly
import plotly.graph_objs as go
import plotly.io as pio
# for PCA
import numpy as np
# from rpy2.robjects import r
# for histogram dist plot
import plotly.figure_factory as ff
# import argparse
# for bool values
import ast
# metal restraints adding
import math
# for Streamlit usage, wide screen display
import streamlit as st
st.set_page_config(layout="wide")
from tempfile import NamedTemporaryFile
import base64
# for contact map
import MDAnalysis as mda
from MDAnalysis.analysis import contacts
import csv
import matplotlib.pyplot as plt
#################################################################################################################################################
class plotly_go():
flag = ''
sasa_flag = ''
pca_flag = ''
time1 = []
values1 = []
sd1 = []
time2 = []
values2 = []
sd2 = []
time3 = []
values3 = []
sd3 = []
max_value = []
average_value = []
multi_flag = ''
def __init__(self, multi_files, output_name, renumber, rdf_cutoff, average, plot_name, nbin, size, move_average, mean_value, histogram, xaxis_name, yaxis_name, xaxis_size, yaxis_size, xy_font, title_font, legend_show, legend_font, font_family, font_color, grid_show, uploaded_filenames, l,r,t,b, violin, smooth):
if len(multi_files) >=1:
# print(multi_files)
file1 = multi_files[0]
self.flag_recognizer(file1, plot_name)
if self.pca_flag != 1 and self.flag != 'pca':
self.plotly_multy(multi_files, output_name, renumber, rdf_cutoff, average, plot_name, nbin, size, move_average, mean_value, histogram, xaxis_name, yaxis_name, xaxis_size, yaxis_size, xy_font, title_font, legend_show, legend_font, font_family, font_color, grid_show, self.flag, uploaded_filenames, l,r,t,b, violin)
elif self.pca_flag == 1:
self.plotly_pca(multi_files, output_name, renumber, rdf_cutoff, average, plot_name, nbin, size, move_average, mean_value, histogram, xaxis_name, yaxis_name, xaxis_size, yaxis_size, xy_font, title_font, legend_show, legend_font, font_family, font_color, grid_show, self.flag, uploaded_filenames, l,r,t,b, smooth)
elif self.flag == 'pca':
self.plotly_pca(multi_files, output_name, renumber, rdf_cutoff, average, plot_name, nbin, size, move_average, mean_value, histogram, xaxis_name, yaxis_name, xaxis_size, yaxis_size, xy_font, title_font, legend_show, legend_font, font_family, font_color, grid_show, self.flag, uploaded_filenames, l,r,t,b, smooth)
def flag_recognizer(self,file1, plot_name): # first method to be called in __main__, used for creating object and charactors.
flags_map = {
'rms,': 'rmsd',
'rmsd' : 'rmsd',
'rmsf,': 'rmsf',
'rmsf' : 'rmsf',
'sasa,': 'sasa',
'sasa' : 'sasa',
'gyrate,': 'gyrate',
'gyrate' : 'gyrate',
'dipoles,': 'dipoles',
'dipoles' : 'dipoles',
'distance,': 'distance',
'distance' : 'distance',
'rdf,': 'rdf',
'rdf' : 'rdf',
'convergence': 'convergence',
'anaeig,': 'pca',
'pca' : 'pca',
'angle,': 'angle',
'angle' : 'angle'
}
if file1.endswith(".xvg"):
with open(file1, 'r') as f:
lines = f.readlines()
if len(lines) >= 3:
try:
flag = lines[2].split()[5]
self.flag = flags_map.get(flag, flag)
except IndexError:
pass
if len(lines) >= 9 and '-or' in lines[8]:
self.sasa_flag = '-or'
if 'pca' in str(file1).lower() or '2dproj' in str(file1):
self.pca_flag = 1
print("I know you are plotting " + self.flag + " figures!")
elif file1.endswith(".csv"):
found = False
for key in flags_map:
if key.strip(',') in plot_name.lower():
found = True
self.flag = flags_map[key]
break
def consist(self,x_values):
seq1 = [x_values[0]]
seq2 = []
# seq3 = []
for i in range(1, len(x_values)):
# find the break point
if x_values[i] <= x_values[i-1]+2 and seq2 == []:
seq1.append(x_values[i])
else:
seq2.append(x_values[i])
return seq1, seq2
def read_data(self, file, x_name, renumber):
# 从文件中读取数据
x_data, y_data, sd_data = [], [], []
if file.endswith(".xvg"):
with open(file, "r") as f:
lines = f.readlines()
for line in lines:
if line.startswith("#") or line.startswith("@"):
continue
else:
# 解析数据行
split_line = line.split()
x_value = float(split_line[0])
y_value = float(split_line[1])
if x_name == 'Time (ps)': # 将时间从ps转换为ns
x_value /= 1000
if x_name == 'Residue' and renumber == 'true':
x_value = len(x_data) + 1
x_data.append(x_value)
y_data.append(y_value)
# 读取标准差（如果存在）
try:
sd_data.append(float(split_line[2]))
except IndexError:
pass
elif file.endswith(".csv"):
df = pd.read_csv(file, skiprows=0)
x_data = df[df.columns[0]].tolist() # 第一列作为X轴数据
if x_name == 'Time (ps)': # 将时间从ps转换为ns
scaled_list = [x / 1000 for x in x_data]
x_data = scaled_list
elif x_name in ['Residue', 'residue'] and renumber == 'true':
x_data = list(range(1,len(x_data)+1))
# 将除第一列外的所有列作为Y轴数据，每列一个Y数据序列
y_data = [df[col].tolist() for col in df.columns[1:]]
# CSV文件不包含标准差数据，因此sd_data保持为空
return x_data, y_data, sd_data
def extract_plot_details(self, multi_files, plot_name, xaxis_name, yaxis_name, flag, histogram):
traces_name_list = []
## Read XVG files
if multi_files[0].endswith(".xvg"):
regex = r"\[|\]|'"
# 提取或设置图表标题
if plot_name == 'auto detect':
with open(multi_files[0], "r") as f:
plot_title = re.sub(regex, "", str(re.findall('"([^"]*)"', f.readlines()[13])))
else:
plot_title = str(plot_name)
# 提取或设置X轴名称
if xaxis_name == 'auto detect':
with open(multi_files[0], "r") as f:
x_name = re.sub(regex, "", str(re.findall('"([^"]*)"', f.readlines()[14])))
else:
x_name = xaxis_name
# 提取或设置Y轴名称
if yaxis_name == 'auto detect':
with open(multi_files[0], "r") as f:
y_name = re.sub(regex, "", str(re.findall('"([^"]*)"', f.readlines()[15])))
if plot_title in ['Solvent Accessible Surface', 'Area per residue over the trajectory']:
y_name = 'Area (nm<sup>2</sup>)'
elif flag == 'dihedral_distribution' and histogram == 'true':
y_name = 'Probability'
else:
y_name = yaxis_name
## Read CSV files
elif multi_files[0].endswith(".csv"):
df = pd.read_csv(multi_files[0])
# 提取或设置图表标题
if plot_name == 'auto detect':
base_name = os.path.basename(multi_files[0])
filename = os.path.splitext(base_name)[0]
plot_title = str(filename)
else:
plot_title = str(plot_name)
# 提取或设置X轴名称
if xaxis_name == 'auto detect':
x_name = df.columns[0]
else:
x_name = xaxis_name
# 提取或设置Y轴名称
if yaxis_name == 'auto detect':
y_name = ''
traces_name_list.extend(df.columns[1:])
else:
y_name = yaxis_name
traces_name_list.extend(df.columns[1:])
return plot_title, x_name, y_name, traces_name_list
def define_trace(self, x_data, y_data, file_name, colour, violine='False', flag=0, labels=0, smooth=0):
# 创建并返回迹线
if flag == 'pca' and smooth != 'true':
trace = go.Scatter(
x=x_data,
y=y_data,
mode='markers',
marker=dict(
color=labels, # 设置颜色为标签的数值
colorscale=colour, # 颜色映射，你可以根据需要选择不同的颜色映射
colorbar=dict(title='Label Range'), # 添加颜色条
),
)
elif flag =='pca' and smooth == 'true':
trace = go.Heatmap(z=x_data, colorscale='Viridis', showscale=True, connectgaps=True, zsmooth='best', x=[-180, -120, -60, 60, 120,180], y=[-180, -120, -60, 60, 120,180])
elif violine != 'False':
trace = go.Violin(x0=str(file_name).split('.')[0], y=y_data, line=dict(color='black'), fillcolor=colour, name=str(file_name).split('.')[0], box_visible=True, meanline_visible=True, opacity=0.6)
elif smooth == 'true':
trace = go.Heatmap(z=x_data, colorscale='Viridis', showscale=True, connectgaps=True, zsmooth='best', x=[-180, -120, -60, 60, 120,180], y=[-180, -120, -60, 60, 120,180])
else:
trace = go.Scatter(x=x_data, y=y_data, line=dict(color=colour), name=str(file_name).split('.')[0])
return trace
def setup_layout(self, plot_title, title_font, x_name, y_name, xy_font, xaxis_size, yaxis_size, font_color, legend_show, legend_font, font_family, grid_show, l,r,t,b, violine='False', flag=0):
# 设置布局
if flag == 'pca':
legend_show = False
if violine != 'False':
x_name = ''
layout = go.Layout(
title=plot_title, title_x=0.5, title_y=0.99, font=dict(size=title_font, color=font_color),
xaxis=dict(title=x_name, titlefont=dict(size=xy_font, color=font_color, family=font_family), zeroline=False, autorange=True,
showgrid=grid_show, gridwidth=1, gridcolor='rgba(235,240,248,100)', tickfont=dict(size=30)),
yaxis=dict(title=y_name, titlefont=dict(size=xy_font, color=font_color, family=font_family), zeroline=False, autorange=True,
showgrid=grid_show, gridwidth=1, gridcolor='rgba(235,240,248,100)', tickfont=dict(size=30)),
legend=dict(x=1, y=1, orientation='v', font=dict(size=legend_font, color=font_color)), showlegend=legend_show,
plot_bgcolor='rgba(255, 255, 255, 0.1)',
paper_bgcolor='rgba(255, 255, 255, 0.2)',
margin=dict(l=int(l), r=int(r), t=int(t), b=int(b)),
width=xaxis_size, height=yaxis_size
)
return layout
def pca_bins_density_define(self, nbins, data):
# 确定边界
x_min, y_min = np.min(data, axis=0)
x_max, y_max = np.max(data, axis=0)
# 创建网格
n_bins = nbins
x_bins = np.linspace(x_min, x_max, n_bins + 1)
y_bins = np.linspace(y_min, y_max, n_bins + 1)
# 计算每个格子内的点的数量（密度）
density_matrix = np.zeros((n_bins, n_bins))
for x, y in data:
x_idx = np.digitize(x, x_bins) - 1
y_idx = np.digitize(y, y_bins) - 1
# 确保索引不超出density_matrix的范围
x_idx = min(x_idx, n_bins - 1)
y_idx = min(y_idx, n_bins - 1)
density_matrix[y_idx, x_idx] += 1 # 注意矩阵的索引和坐标系的方向
return density_matrix
def streamlit_download_file_plotly(self, download_name, content_file):
# 读取文件内容
with open(content_file, "rb") as file:
file_content = file.read()
# 获取文件的 MIME 类型
mime_type, _ = mimetypes.guess_type(content_file)
# 创建下载按钮
st.download_button(
label=f"Download {download_name}",
data=file_content,
file_name=download_name,
mime=mime_type)
def plot_graph(self, data, layout, output_file_name):
# 使用数据和布局绘制图形
fig = go.Figure(data=data, layout=layout)
pio.write_image(fig, "/tmp/" + output_file_name)
self.streamlit_download_file_plotly(output_file_name, "/tmp/" + output_file_name)
def plot_histogram(self, histogram_data, group_labels, plot_title, output_file_name, colors, nbin):
# 处理直方图
fig_hist = ff.create_distplot(histogram_data, group_labels, colors=colors, bin_size=nbin, curve_type='normal')
fig_hist.update_layout(title_text=plot_title)
pio.write_image(fig_hist, "/tmp/" + output_file_name)
self.streamlit_download_file_plotly("hist_" + output_file_name, "/tmp/" + output_file_name)
def calculate_average(self, multi_files, xaxis_name, renumber):
# 计算平均值
sum_data = None
for file in multi_files:
x_data, y_data, _ = self.read_data(file, xaxis_name, renumber)
if sum_data is None:
sum_data = np.array(y_data)
else:
sum_data += np.array(y_data)
return sum_data / len(multi_files)
def output_average_file(self, output_file_name, average_value, multi_files, xaxis_name, renumber, x_data):
with open(output_file_name[:-4]+"_average.xvg", 'w') as f:
with open(multi_files[0], "r") as a:
lines = a.readlines()
for num in range(len(lines)):
if lines[num].startswith("#") or lines[num].startswith("@"):
f.write(lines[num])
else:
pass
for num in range(len(average_value)):
average_line = "{} {}\n".format(x_data[num], average_value[num])
f.write(average_line)
def moving_average(self, y_data, window_size):
# 计算移动平均
return np.convolve(y_data, np.ones(window_size) / window_size, mode='valid')
def plotly_multy(self, multi_files, output_name, renumber, rdf_cutoff, average, plot_name, nbin, size, move_average, mean_value, histogram, xaxis_name, yaxis_name, xaxis_size, yaxis_size, xy_font, title_font, legend_show, legend_font, font_family, font_color, grid_show, flag, uploaded_filenames, l,r,t,b, violin):
Plotly = ['#636EFA', '#EF553B', '#00CC96', '#AB63FA', '#FFA15A', '#19D3F3', '#FF6692', '#B6E880', '#FF97FF', '#FECB52']
data, histogram_data, group_labels = [], [], []
# 读取plot_title, x_name, y_name
plot_title, x_name, y_name, traces_name_list = self.extract_plot_details(multi_files, plot_name, xaxis_name, yaxis_name, flag, histogram)
# 读取数据并创建迹线
if multi_files[0].endswith(".xvg"):
for i, file in enumerate(multi_files):
x_data, y_data, _ = self.read_data(file, x_name, renumber)
trace = self.define_trace(x_data, y_data, uploaded_filenames[i], Plotly[i % len(Plotly)], violine=violin)
data.append(trace)
# 添加直方图数据
if histogram == 'true':
histogram_data.append(y_data)
group_labels.append(str(uploaded_filenames[i]).split('.')[0])
elif multi_files[0].endswith(".csv"):
for i, trace in enumerate(traces_name_list):
x_data, y_data, _ = self.read_data(multi_files[0], x_name, renumber)
trace = self.define_trace(x_data, y_data[i], trace, Plotly[i % len(Plotly)], violine=violin)
data.append(trace)
# 添加直方图数据
if histogram == 'true':
histogram_data.append(y_data)
group_labels.append(str(file).split('.')[0])
# change Time (ps) to Time (ns)
if x_name == 'Time (ps)':
x_name = 'Time (ns)'
# 设置布局
layout = self.setup_layout(plot_title, title_font, x_name, y_name, xy_font, xaxis_size, yaxis_size, font_color, legend_show, legend_font, font_family, grid_show, l, r, t , b, violine=violin)
# 绘制图形
self.plot_graph(data, layout, output_name)
# 处理直方图
if histogram == 'true':
self.plot_histogram(histogram_data, group_labels, plot_title, output_name, Plotly, nbin)
# 处理平均值
if average == 'true':
average_data = self.calculate_average(multi_files, xaxis_name, renumber)
average_trace = self.define_trace(x_data, average_data, "Average", 'black')
# data.append(average_trace)
data = average_trace
self.plot_graph(data, layout, "Average_" + output_name)
self.output_average_file("/tmp/" + output_name, average_data, multi_files, xaxis_name, renumber, x_data)
# 处理移动平均
if move_average != 0:
ma_data = []
for file in multi_files:
_, y_data, _ = self.read_data(file, xaxis_name, renumber)
ma_y_data = self.moving_average(y_data, move_average)
ma_trace = self.define_trace(x_data[move_average - 1:], ma_y_data, str(file).split('.')[0], Plotly[0])
ma_data.append(ma_trace)
self.plot_graph(ma_data, layout, "MovingAverage_" + output_name)
# # 调用上述方法
# for i, file in enumerate(multi_files):
# x_data, y_data, sd_data = self.read_data(file, xaxis_name, renumber)
# trace = self.define_trace(x_data, y_data, file, Plotly[i % len(Plotly)])
# data.append(trace)
# layout = self.setup_layout(plot_title, title_font, x_name, y_name, xy_font, xaxis_size, yaxis_size, font_color, legend_show, legend_font, font_family, grid_show)
# self.plot_graph(data, layout, output_name)
def plotly_pca(self, multi_files, output_name, renumber, rdf_cutoff, average, plot_name, nbin, size, move_average, mean_value, histogram, xaxis_name, yaxis_name, xaxis_size, yaxis_size, xy_font, title_font, legend_show, legend_font, font_family, font_color, grid_show, flag, uploaded_filenames, l, r, t ,b, smooth):
data = []
color = ['rainbow']
# labels = []
# 使用 extract_plot_details 方法获取图表标题和轴标签
plot_title, x_name, y_name, traces_name_list = self.extract_plot_details(multi_files, plot_name, xaxis_name, yaxis_name, flag, histogram)
if xaxis_name == 'auto detect':
x_name = 'PC1 (nm)'
if yaxis_name == 'auto detect':
y_name = 'PC2 (nm)'
# 处理 PCA 数据
if smooth != 'true':
if multi_files[0].endswith(".xvg"):
for i, file in enumerate(multi_files):
x_data, y_data, _ = self.read_data(file, "PC1", renumber) # 假设 "PC1" 和 "PC2" 是合适的轴名称
labels = [x for x in range(len(y_data))]
# 使用 define_trace 创建迹线
trace = self.define_trace(x_data, y_data, file, 'rainbow', flag=flag, labels=labels) # 假设使用 'rainbow' 作为颜色
data.append(trace)
elif multi_files[0].endswith(".csv"):
for i, trace in enumerate(traces_name_list):
x_data, y_data, _ = self.read_data(multi_files[0], "PC1", renumber)
labels = [x for x in range(len(y_data[i]))]
trace = self.define_trace(x_data, y_data[i], multi_files[0], 'rainbow', flag=flag, labels=labels)
data.append(trace)
layout = self.setup_layout(plot_title, title_font, x_name, y_name, xy_font, xaxis_size, yaxis_size, font_color, legend_show, legend_font, font_family, grid_show, l, r, t ,b, flag=flag)
elif smooth == 'true':
if multi_files[0].endswith(".xvg"):
for i, file in enumerate(multi_files):
x_data, y_data, _ = self.read_data(file, "PC1", renumber) # 假设 "PC1" 和 "PC2" 是合适的轴名称
points = [list(pair) for pair in zip(x_data, y_data[i])]
density_matrix = self.pca_bins_density_define(nbin, points)
# 使用 define_trace 创建迹线
trace = self.define_trace(density_matrix, density_matrix, file, 'rainbow', flag=flag, smooth=smooth) # 假设使用 'rainbow' 作为颜色
data.append(trace)
elif multi_files[0].endswith(".csv"):
for i, trace in enumerate(traces_name_list):
x_data, y_data, _ = self.read_data(multi_files[0], "PC1", renumber)
points = [list(pair) for pair in zip(x_data, y_data[0])]
density_matrix = self.pca_bins_density_define(nbin, points)
trace = self.define_trace(density_matrix, density_matrix, multi_files[0], 'rainbow', flag=flag, smooth=smooth)
data.append(trace)
# 使用 setup_layout 设置布局
layout = self.setup_layout(plot_title, title_font, xaxis_name, yaxis_name, xy_font, xaxis_size, yaxis_size, font_color, legend_show, legend_font, font_family, grid_show, l, r, t ,b, flag=flag)
# 使用 plot_graph 绘制图形
self.plot_graph(data, layout, "Scatter_" + output_name)
##########################################################################################################################################################################################
class mr(): # read content from the uploaded file directly.
head = ''
total_atom = 0
resid = []
resname= []
atomname = []
index = []
x = []
y = []
z = []
xyz = []
last = ''
metals = []
coordinators = []
metal1 = 0
metal2 = 0
metal3 = 0
metal4 = 0
metal5 = 0
metal6 = 0
metal7 = 0
atom1 = []
atom2 = []
atom3 = []
atom4 = []
atom5 = []
atom6 = []
atom7 = []
def __init__(self, gro,num_neighbours, distance_value, atom_list, metal_list, residue_list, bond_strength, angle_strength):
self.output = ""
self.GROreader(gro)
self.MetalMiner(metal_list)
self.coordinator(num_neighbours, distance_value, atom_list, metal_list, residue_list)
# self.bond_cal(atom6,bond_strength)
# self.pair_cal()
# self.angle_cal(angle_strength)
self.bond_cal( self.metal1, self.atom1, self.metal2, self.atom2, self.metal3, self.atom3, self.metal4, self.atom4, self.metal5, self.atom5, self.metal6, self.atom6, bond_strength)
self.pair_cal( self.metal1, self.atom1, self.metal2, self.atom2, self.metal3, self.atom3, self.metal4, self.atom4, self.metal5, self.atom5, self.metal6, self.atom6)
self.angle_cal( self.metal1, self.atom1, self.metal2, self.atom2, self.metal3, self.atom3, self.metal4, self.atom4, self.metal5, self.atom5, self.metal6, self.atom6, angle_strength)
def GROreader(self,gro):
lines = gro.splitlines() # for streamlit 如果 'gro' 是一个二进制文件，使用 gro.decode().splitlines()
# extra lines
self.head = lines[0].strip()
self.total_atom = int(lines[1])
self.last = lines[-1]
# 忽略前两行和最后一行
lines = lines[2:-1]
# 逐行解析内容
for line in lines:
line = line.strip() # 去除首尾空格和换行符
match = re.match(r'(\d+)([A-Za-z]{2,})', line)
if match:
self.resid.append(int(match.group(1)))
self.resname.append(str(match.group(2)))
self.atomname.append(str(line.split()[1])) # The 3rd column is the atom name C CA CD1 CD2 and so on
self.index.append(int(line.split()[2])) # Column 4 is the residue name TYR ALA etc.
self.x.append(float(line.split()[3])) # The 5th column is the name of the chain it is on
self.y.append(float(line.split()[4])) # The sixth column is the residue number
self.z.append(float(line.split()[5])) # Column 7 is the x-coordinate of the atom
self.xyz.append([float(line.split()[3]),float(line.split()[4]),float(line.split()[5])])
def MetalMiner(self, metal_list):
print(metal_list)
for i in range(len(self.atomname)):
if self.atomname[i] in metal_list and self.resname[i] in metal_list:
self.metals.append(self.index[i])
# the index in list should -1
for i in range(len(self.metals)):
try:
setattr(self, f'metal{i+1}', self.metals[i] - 1)
except IndexError:
pass
metals_name = [self.atomname[i-1] for i in self.metals]
sentence = "The metals atom index are: {}".format(list(zip(metals_name, self.metals)))
print(sentence)
# print(self.metal1,self.metal2,self.metal3)
def coordinator(self, num_neighbours, distance_value, atom_list, metal_list, residue_list):
# find the atom index
if self.metal1 != 0:
atom_distances = {}
for i in range(len(self.index)):
if self.resname[i] in residue_list and self.atomname[i] in atom_list:
dist = self.distance(self.metal1, i)
if dist <= distance_value:
atom_distances[i] = dist
# 对满足条件的原子按照距离 metal 的距离进行排序
sorted_atoms = sorted(atom_distances.items(), key=lambda x: x[1])
# 仅保留前 num_neighbours 个邻居
self.atom1 = [atom_index for atom_index, _ in sorted_atoms[:num_neighbours]]
if self.metal2 != 0:
atom_distances = {}
for i in range(len(self.index)):
if self.resname[i] in residue_list and self.atomname[i] in atom_list:
dist = self.distance(self.metal2, i)
if dist <= distance_value:
atom_distances[i] = dist
# 对满足条件的原子按照距离 metal 的距离进行排序
sorted_atoms = sorted(atom_distances.items(), key=lambda x: x[1])
# 仅保留前 num_neighbours 个邻居
self.atom2 = [atom_index for atom_index, _ in sorted_atoms[:num_neighbours]]
if self.metal3 != 0:
atom_distances = {}
for i in range(len(self.index)):
if self.resname[i] in residue_list and self.atomname[i] in atom_list:
dist = self.distance(self.metal3, i)
if dist <= distance_value:
atom_distances[i] = dist
# 对满足条件的原子按照距离 metal 的距离进行排序
sorted_atoms = sorted(atom_distances.items(), key=lambda x: x[1])
# 仅保留前 num_neighbours 个邻居
self.atom3 = [atom_index for atom_index, _ in sorted_atoms[:num_neighbours]]
if self.metal4 != 0:
atom_distances = {}
for i in range(len(self.index)):
if self.resname[i] in residue_list and self.atomname[i] in atom_list:
dist = self.distance(self.metal4, i)
if dist <= distance_value:
atom_distances[i] = dist
# 对满足条件的原子按照距离 metal 的距离进行排序
sorted_atoms = sorted(atom_distances.items(), key=lambda x: x[1])
# 仅保留前 num_neighbours 个邻居
self.atom4 = [atom_index for atom_index, _ in sorted_atoms[:num_neighbours]]
if self.metal5 != 0:
atom_distances = {}
for i in range(len(self.index)):
if self.resname[i] in residue_list and self.atomname[i] in atom_list:
dist = self.distance(self.metal5, i)
if dist <= distance_value:
atom_distances[i] = dist
# 对满足条件的原子按照距离 metal 的距离进行排序
sorted_atoms = sorted(atom_distances.items(), key=lambda x: x[1])
# 仅保留前 num_neighbours 个邻居
self.atom5 = [atom_index for atom_index, _ in sorted_atoms[:num_neighbours]]
if self.metal6 != 0:
atom_distances = {}
for i in range(len(self.index)):
if self.resname[i] in residue_list and self.atomname[i] in atom_list:
dist = self.distance(self.metal6, i)
if dist <= distance_value:
atom_distances[i] = dist
# 对满足条件的原子按照距离 metal 的距离进行排序
sorted_atoms = sorted(atom_distances.items(), key=lambda x: x[1])
# 仅保留前 num_neighbours 个邻居
self.atom6 = [atom_index for atom_index, _ in sorted_atoms[:num_neighbours]]
if self.metal7 != 0:
atom_distances = {}
for i in range(len(self.index)):
if self.resname[i] in residue_list and self.atomname[i] in atom_list:
dist = self.distance(self.metal7, i)
if dist <= distance_value:
atom_distances[i] = dist
# 对满足条件的原子按照距离 metal 的距离进行排序
sorted_atoms = sorted(atom_distances.items(), key=lambda x: x[1])
# 仅保留前 num_neighbours 个邻居
self.atom7 = [atom_index for atom_index, _ in sorted_atoms[:num_neighbours]]
def distance(self, index1, index2):
distance = math.sqrt((self.x[index2] - self.x[index1])**2 + (self.y[index2] - self.y[index1])**2 + (self.z[index2] - self.z[index1])**2)
return distance
def calculate_distance(self, point1, point2):
x1, y1, z1 = point1
x2, y2, z2 = point2
distance = math.sqrt((x2 - x1)**2 + (y2 - y1)**2 + (z2 - z1)**2)
return distance
def calculate_angle(self, point1, point2, point3):
vector_ab = np.array(point2) - np.array(point1)
vector_bc = np.array(point2) - np.array(point3)
dot_product = np.dot(vector_ab, vector_bc)
norm_ab = np.linalg.norm(vector_ab)
norm_bc = np.linalg.norm(vector_bc)
cos_angle = dot_product / (norm_ab * norm_bc)
angle_rad = np.arccos(cos_angle)
angle_deg = np.degrees(angle_rad)
return angle_deg
def bond_cal(self, metal1, atom1, metal2, atom2, metal3, atom3, metal4, atom4, metal5, atom5, metal6, atom6, bond_strength):
for i in [1,2,3,4,5,6]:
if locals()["metal" + str(i)] !=0:
metal = locals()["metal" + str(i)]+1
atom = [i + 1 for i in locals()["atom" + str(i)]]
metal_point = [self.x[metal-1],self.y[metal-1],self.z[metal-1]]
print("; please add below to topol.top's distance part")
self.output += "; please add below to topol.top's distance part\n" # for streamlit
for i in atom:
locals()["atom" + str(i)] = [self.x[i-1],self.y[i-1],self.z[i-1]]
print("%5d%6d%6d%7.2f%9d" % (metal, i, 6, self.calculate_distance(metal_point, locals()["atom" + str(i)]), bond_strength))
self.output += "%5d%6d%6d%7.2f%9d\n" % (metal, i, 6, self.calculate_distance(metal_point, locals()["atom" + str(i)]), bond_strength) # for streamlit
def pair_cal(self, metal1, atom1, metal2, atom2, metal3, atom3, metal4, atom4, metal5, atom5, metal6, atom6):
for i in [1,2,3,4,5,6]:
if locals()["metal" + str(i)] !=0:
metal = locals()["metal" + str(i)]+1
atom = [i + 1 for i in locals()["atom" + str(i)]]
target_resid = [self.resid[x-1] for x in atom]
print("; I added the pairs - so the zn will not nonbonded interact with the cyx residues")
self.output += "; add the pairs \n" # for streamlit
for i in range(len(self.atomname)):
if self.atomname[i] == 'CA' and self.resid[i] in target_resid:
print("%5d%6d%6d" % (metal,i+1,1))
self.output += "%5d%6d%6d\n" % (metal,i+1,1) # for streamlit
def angle_cal(self, metal1, atom1, metal2, atom2, metal3, atom3, metal4, atom4, metal5, atom5, metal6, atom6, angle_strength):
for i in [1,2,3,4,5,6]:
if locals()["metal" + str(i)] !=0:
metal = locals()["metal" + str(i)]+1
atom = [i + 1 for i in locals()["atom" + str(i)]]
# define how many neighbour atoms
neighbour = len(atom)
if neighbour >= 2:
metal_point = [self.x[metal-1],self.y[metal-1],self.z[metal-1]]
for i in atom:
locals()["atom" + str(i)] = [self.x[i-1],self.y[i-1],self.z[i-1]]
print("[ angle_restraints ]")
self.output += "[ angle_restraints ]\n" # for streamlit
if neighbour == 2:
print("%5d%6d%6d%6d%5d%9.2f%9d%9d" % (metal, atom[0], metal, atom[1],1,self.calculate_angle(locals()["atom" + str(atom[0])],metal_point, locals()["atom" + str(atom[1])]),angle_strength, 1))
self.output += "%5d%6d%6d%6d%5d%9.2f%9d%9d\n" % (metal, atom[0], metal, atom[1],1,self.calculate_angle(locals()["atom" + str(atom[0])],metal_point, locals()["atom" + str(atom[1])]),angle_strength, 1) # for streamlit
elif neighbour == 3:
print("%5d%6d%6d%6d%5d%9.2f%9d%9d" % (metal, atom[0], metal, atom[1],1,self.calculate_angle(locals()["atom" + str(atom[0])],metal_point, locals()["atom" + str(atom[1])]),angle_strength, 1))
print("%5d%6d%6d%6d%5d%9.2f%9d%9d" % (metal, atom[0], metal, atom[2],1,self.calculate_angle(locals()["atom" + str(atom[0])],metal_point, locals()["atom" + str(atom[2])]),angle_strength, 1))
print("%5d%6d%6d%6d%5d%9.2f%9d%9d" % (metal, atom[1], metal, atom[2],1,self.calculate_angle(locals()["atom" + str(atom[1])],metal_point, locals()["atom" + str(atom[2])]),angle_strength, 1))
self.output += "%5d%6d%6d%6d%5d%9.2f%9d%9d\n" % (metal, atom[0], metal, atom[1],1,self.calculate_angle(locals()["atom" + str(atom[0])],metal_point, locals()["atom" + str(atom[1])]),angle_strength, 1) # for streamlit
self.output += "%5d%6d%6d%6d%5d%9.2f%9d%9d\n" % (metal, atom[0], metal, atom[2],1,self.calculate_angle(locals()["atom" + str(atom[0])],metal_point, locals()["atom" + str(atom[2])]),angle_strength, 1) # for streamlit
self.output += "%5d%6d%6d%6d%5d%9.2f%9d%9d\n" % (metal, atom[1], metal, atom[2],1,self.calculate_angle(locals()["atom" + str(atom[1])],metal_point, locals()["atom" + str(atom[2])]),angle_strength, 1) # for streamlit
elif neighbour == 4:
print("%5d%6d%6d%6d%5d%9.2f%9d%9d" % (metal, atom[0], metal, atom[1],1,self.calculate_angle(locals()["atom" + str(atom[0])],metal_point, locals()["atom" + str(atom[1])]),angle_strength, 1))
print("%5d%6d%6d%6d%5d%9.2f%9d%9d" % (metal, atom[1], metal, atom[2],1,self.calculate_angle(locals()["atom" + str(atom[1])],metal_point, locals()["atom" + str(atom[2])]),angle_strength, 1))
print("%5d%6d%6d%6d%5d%9.2f%9d%9d" % (metal, atom[2], metal, atom[3],1,self.calculate_angle(locals()["atom" + str(atom[2])],metal_point, locals()["atom" + str(atom[3])]),angle_strength, 1))
print("%5d%6d%6d%6d%5d%9.2f%9d%9d" % (metal, atom[0], metal, atom[3],1,self.calculate_angle(locals()["atom" + str(atom[0])],metal_point, locals()["atom" + str(atom[3])]),angle_strength, 1))
self.output += "%5d%6d%6d%6d%5d%9.2f%9d%9d\n" % (metal, atom[0], metal, atom[1],1,self.calculate_angle(locals()["atom" + str(atom[0])],metal_point, locals()["atom" + str(atom[1])]),angle_strength, 1) # for streamlit
self.output += "%5d%6d%6d%6d%5d%9.2f%9d%9d\n" % (metal, atom[1], metal, atom[2],1,self.calculate_angle(locals()["atom" + str(atom[1])],metal_point, locals()["atom" + str(atom[2])]),angle_strength, 1) # for streamlit
self.output += "%5d%6d%6d%6d%5d%9.2f%9d%9d\n" % (metal, atom[2], metal, atom[3],1,self.calculate_angle(locals()["atom" + str(atom[2])],metal_point, locals()["atom" + str(atom[3])]),angle_strength, 1) # for streamlit
self.output += "%5d%6d%6d%6d%5d%9.2f%9d%9d\n" % (metal, atom[0], metal, atom[3],1,self.calculate_angle(locals()["atom" + str(atom[0])],metal_point, locals()["atom" + str(atom[3])]),angle_strength, 1) # for streamlit
def GROwriter(self, gro):
print(self.head)
print("%5d" % (self.total_atom))
for i in range(len(self.resid)):
print("%5d%-3s%7s%5d%8.3f%8.3f%8.3f" % (self.resid[i], self.resname[i], self.atom[i], self.index[i], self.x[i], self.y[i], self.z[i]))
print(self.last)
##########################################################################################################################################################################################
class gromerger(): # read uploaded files
def __init__(self, receptor_gro, ligand_gro, ligand_itp, receptor_top, rec_name, lig_name, ligitp_name, rectop_name):
self.merge_gro_files(receptor_gro, ligand_gro, ligand_itp, receptor_top, rec_name, lig_name, ligitp_name, rectop_name)
def streamlit_download_file(self, download_name, content_file):
# Download topol.top file #
# 打开 content_file 文件并读取其内容
with open(content_file, 'r') as top_file:
content = top_file.read()
# 添加一个下载按钮，传递 receptor_top_content 作为文件内容
st.download_button(
label = "Download " + download_name,
data = content,
key = download_name,
file_name = download_name
)
def merge_gro_files(self, receptor_gro, ligand_gro, ligand_itp, receptor_top, rec_name, lig_name, ligitp_name, rectop_name):
# Merge the two gro files
with open(ligand_gro, 'r') as ligand_file, open(receptor_gro, 'r') as receptor_file:
ligand_lines = ligand_file.readlines()[1:-1]
receptor_lines = receptor_file.readlines()
a = int(receptor_lines[1].split()[0])
b = int(ligand_lines[0].split()[0])
c = a + b
receptor_lines[1] = f"{c}\n"
with open(ligand_gro, 'w') as complex_file:
complex_file.writelines(receptor_lines[0:-1])
complex_file.writelines(ligand_lines[1:])
complex_file.writelines(receptor_lines[-1])
# Edit the topol.top file
# with open('include.dat', 'w') as include_file:
# include_file.write("; Include ligand topology\n")
# include_file.write(f"#include \"{ligitp_name}\"\n")
# include_file.write("#ifdef POSRES_LIG\n")
# include_file.write("#include \"posre_lig.itp\"\n")
# include_file.write("#endif\n")
# adding content to the end of topol file
with open(ligand_itp, 'r') as ligand_itp_file:
# find ligand molecule name
nrexcl_line = None
lines = ligand_itp_file.readlines()
for i in range(len(lines)):
if 'nrexcl' in lines[i]:
nrexcl_line = lines[i+1].strip()
break
if nrexcl_line:
B = nrexcl_line.split()[0]
with open(receptor_top, 'r') as topol_file:
topol_lines = topol_file.readlines()
# add lig_GMX.itp record to topol.top
for i, line in enumerate(topol_lines):
if 'moleculetype' in line:
topol_lines.insert(i, f"#endif\n")
topol_lines.insert(i, f"#include \"posre_lig.itp\"\n")
topol_lines.insert(i, f"#ifdef POSRES_LIG\n")
topol_lines.insert(i, f"#include \"{ligitp_name}\"\n")
topol_lines.insert(i, f"; Include ligand topology\n")
break
# add ligand molecule type to topol.top
for i, line in enumerate(topol_lines):
if 'molecules' in line:
topol_lines.insert(-1, f"{B} 1\n")
break
with open(receptor_top, 'w') as topol_file:
topol_file.writelines(topol_lines)
# download topol.top
self.streamlit_download_file(rectop_name, receptor_top)
self.streamlit_download_file("complex.gro", ligand_gro)
##########################################################################################################################################################################################
class contact_map_detect(): # read uploaded files
protein = ''
ligand = ''
def __init__(self, topol, traj, lig, output, distance):
contact_map = self.calculate_contact(topol, traj, lig, output, distance)
self.plot(contact_map, output_name, distance)
self.csv_writer(contact_map, output_name)
def calculate_contact(self, topol, traj, lig, output, distance):
# 加载蛋白质和配体的拓扑和轨迹文件
u = mda.Universe(topol, traj)
# 选择蛋白质和配体
self.protein = u.select_atoms('protein')
self.ligand = u.select_atoms('resname ' + lig)
# 初始化接触图矩阵
contact_map = np.zeros((len(u.trajectory), len(self.protein.residues)))
# 计算每帧的接触情况
for ts in u.trajectory:
y_ticks = []
y_labels= []
frame_index = ts.frame
for i, residue in enumerate(self.protein.residues):
min_dist = np.min(contacts.distance_array(residue.atoms.positions, self.ligand.positions))
y_ticks.append(i)
y_labels.append(f'{residue.resid} {residue.resname}')
if min_dist < distance:
contact_map[frame_index, i] = 1
return contact_map
def plot(self, contact_map, output_name, distance):
resid_list = [i for i in range(self.protein.residues.resids[0], len(self.protein.residues.resids)+self.protein.residues.resids[0], 5)]
resname_list =[]
for i, residue in enumerate(self.protein.residues):
if residue.resid in resid_list:
resname_list.append(residue.resname)
i_list = range(len(resid_list))
# print(resid_list)
# print(resname_list)
plt.imshow(contact_map.T, aspect='auto', origin='lower', cmap='Greys')
plt.xlabel('Time (ns)')
plt.yticks(ticks=resid_list, labels=['' if (resid_list[i]-self.protein.residues.resids[0]) % 20 != 0 else f'{resid_list[i]} {resname_list[i]}' for i in i_list])
plt.ylabel('Residue Index')
plt.title('Protein-Ligand Contact Map')
plt.suptitle('Distance < ' + str(distance))
plt.colorbar(label='Contact', ticks=[0, 1])
# plt.show()
plt.savefig("/tmp/" + output_name)
self.streamlit_download_file_plotly(output_name, "/tmp/" + output_name)
def csv_writer(self, contact_map, output_name):
# 将contact_map数据写入CSV文件
with open('/tmp/ligand_contact.csv', 'w', newline='') as f:
writer = csv.writer(f)
# 写入标题行，假设每列代表一个残基，每行代表一个时间帧
# header = ['Frame'] + [f'Residue_{i}' for i in range(1, len(protein.residues) + 1)]
header = ['Time (ns)'] + [f'{residue.resid}_{residue.resname}' for i, residue in enumerate(self.protein.residues)]
writer.writerow(header)
# 写入每一行的数据
for frame_index, contacts_ in enumerate(contact_map):
row = [frame_index] + contacts_.tolist()
writer.writerow(row)
self.streamlit_download_file("ligand_contact.csv", '/tmp/ligand_contact.csv')
# print("Contact map data has been written to 'ligand_contact.csv'.")
def streamlit_download_file(self, download_name, content_file):
# Download topol.top file #
# 打开 content_file 文件并读取其内容
with open(content_file, 'r') as top_file:
content = top_file.read()
# 添加一个下载按钮，传递 receptor_top_content 作为文件内容
st.download_button(
label = "Download " + download_name,
data = content,
key = download_name,
file_name = download_name
)
def streamlit_download_file_plotly(self, download_name, content_file):
# 读取文件内容
with open(content_file, "rb") as file:
file_content = file.read()
# 获取文件的 MIME 类型
mime_type, _ = mimetypes.guess_type(content_file)
# 创建下载按钮
st.download_button(
label=f"Download {download_name}",
data=file_content,
file_name=download_name,
mime=mime_type)
# download topol.top
##########################################################################################################################################################################################
class pep2lig():
pdb = ''
pepname = ''
chain = 'A'
resnum = 1
atomic_index = []
atomic_name = []
residue_name = []
chain_name = []
residue_index = []
X_peratom = []
Y_peratom = []
Z_peratom = []
bfactor_per_factor = []
temp_factor = []
Atomtype_per_atom = []
def __init__(self, pdb, pepname):
self.pdb = pdb
self.pepname = pepname
self.converter(pdb, pepname, self.chain, self.resnum)
self.PDBwriter("/tmp/modified_PEP.pdb")
def converter(self, pdb, pepname, chain, resnum):
with open(pdb, 'r') as infile:
for line in infile: # iterate each line in file "f"
if(line.split()[0] in["ATOM","HETATM"]): # Judgment Sentence，Used to split each row and then determine whether the first column of the row == ATOM or HETATM
self.atomic_index.append(int(line[6:11].strip())) # The second column is the atomic number
self.atomic_name.append(line[12:16].strip()) # The 3rd column is the atom name C CA CD1 CD2 and so on
# self.residue_name.append(line[17:20].strip()) # Column 4 is the residue name TYR ALA etc.
self.residue_name.append(pepname)
# self.chain_name.append(line[21].strip()) # The 5th column is the name of the chain it is on
self.chain_name.append(chain)
# self.residue_index.append(int(line[22:26].strip())) # The sixth column is the residue number
self.residue_index.append(resnum)
self.X_peratom.append(float(line[30:38].strip()))
self.Y_peratom.append(float(line[38:46].strip()))
self.Z_peratom.append(float(line[46:54].strip()))
self.bfactor_per_factor.append(float(line[54:60].strip()) if line[54:60].strip() else 0.0)
self.temp_factor.append(float(line[60:66].strip()) if line[60:66].strip() else 0.0 )
try:
self.Atomtype_per_atom.append(line[76:78].strip())
except:
self.Atomtype_per_atom.append(" ")
def PDBwriter(self,filename):
f = open(filename, "w") # e.g: f = linesplit[0]+"_PO3.pdb"
for i in range (0 ,len(self.atomic_index)): # Create a loop, i is a sequence starting from 0, and the number of atoms is the length
print("%4s%7d %-4s%1s%2s%4d %8.3f%8.3f%8.3f%6.2f%6.2f%12s" % ("ATOM" , # Formatted output, %4s, right-aligned, the output occupies 4 columns in total. If the length is less than 4 columns, the left end will be filled with spaces. If it is greater than 4 columns, the actual length will be output as a string
self.atomic_index[i], # %7d, right-aligned, the output occupies a total of 7 columns, if the length is less than 7 columns, the left end is filled with spaces, signed decimal certificate integer
self.atomic_name[i], # %-4s, left-aligned, the output occupies a total of 4 columns, if the length is less than 4 columns, the right end is filled with spaces, if it is greater than 4 columns, the actual length is output as a string
self.residue_name[i], # %1s, right-aligned, the output occupies a total of 1 column. If it is less than 1 column, it will be filled with spaces from the left end. If it is greater than 1 column, the actual length will be output as a string