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from benchmark import get_cath
from benchmark import config
import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path
import numpy as np
import seaborn as sns
import matplotlib
matplotlib.use('Agg')
def format_secondary(df):
secondary=[[],[],[],[]]
for i,chain in df.iterrows():
for structure, residue in zip(list(chain.dssp), list(chain.sequence)):
if structure == "H" or structure == "I" or structure == "G":
secondary[0].append(residue)
elif structure == "E":
secondary[1].append(residue)
elif structure == "B" or structure == "T" or structure == "S":
secondary[2].append(residue)
else:
secondary[3].append(residue)
return secondary
def describe_set(dataset,path_to_pdb):
plt.ioff()
df = get_cath.read_data("cath-domain-description-file.txt")
filtered_df = get_cath.filter_with_user_list(df, Path(dataset))
df_with_sequence = get_cath.append_sequence(filtered_df, Path(path_to_pdb))
#testing set has multiple chains from the same PDB, need to drop repeats for resolution plots.
resolution=df_with_sequence.drop_duplicates(subset=['PDB']).resolution.values
fig,ax=plt.subplots(2,5,figsize=(25,10))
hist=np.histogram(resolution,bins=6,range=(0,3))
counts=hist[0]/len(resolution)
ax[0][0].bar(range(len(counts)),counts)
ax[0][0].set_xlabel(r'Resolution, $\AA$')
ax[0][0].set_ylabel('Fraction of structures')
ax[0][0].set_xticks([0,1,2,3,4,5])
ax[0][0].set_xticklabels(['[0, 0.5)','[0.5, 1)','[1, 1.5)','[1.5, 2)','[2, 2.5)','[2.5, 3]'])
colors = sns.color_palette()
arch=filtered_df.drop_duplicates(subset=['class'])['class'].values
grouped=filtered_df.groupby(by=['class','architecture']).count()
previous_position=0
gs = ax[0, 0].get_gridspec()
for a in ax[0, 1:]:
a.remove()
ax_big = fig.add_subplot(gs[0, 1:])
for x in arch:
if x==1 or x==2 or x==3 or x==4:
architectures=grouped.loc[x]
ax_big.bar(range(previous_position,previous_position+architectures.shape[0]),architectures.PDB.values/filtered_df.shape[0],color=colors[x],label=config.classes[x])
previous_position+=architectures.shape[0]
#combine 4 and 6 for siplicity
if x==6:
architectures=grouped.loc[6]
ax_big.bar(range(previous_position,previous_position+architectures.shape[0]),architectures.PDB.values/filtered_df.shape[0],color=colors[4])
#get names
cls_arch=[f"{x[0]}.{x[1]}" for x in grouped.index]
names=[config.architectures[label] for label in cls_arch]
ax_big.set_xticks(range(grouped.shape[0]))
ax_big.set_xticklabels(names, rotation=90, fontdict={"horizontalalignment": "center", "size": 12})
ax_big.set_ylabel('Fraction of structures')
ax_big.set_title('CATH architectures')
#make space for legend
ax_big.set_xlim(-0.8,grouped.shape[0]+4)
ax_big.legend()
#get secondary structures, filtering with training set will get multiple CATH entries for the same chain.
secondary=format_secondary(df_with_sequence)
#plot residue distribution
ss_types=["Helices", "Sheets", "Structured loops", "Random"]
for x in range(len(secondary)):
ax[1][x].bar(config.acids,np.unique(secondary[x],return_counts=True)[1]/len(secondary[x]))
ax[1][x].set_ylabel('Fraction of structures')
ax[1][x].set_xlabel('Amino acids')
ax[1][x].set_title(ss_types[x])
#flatten the list
all_structures=[x for y in secondary for x in y]
ax[1][4].bar(config.acids,np.unique(all_structures,return_counts=True)[1]/len(all_structures))
ax[1][4].set_ylabel('Fraction of structures')
ax[1][4].set_xlabel('Amino acids')
ax[1][4].set_title('All structures')
plt.tight_layout()
plt.savefig(dataset+'.pdf')
describe_set("/home/s1706179/project/sequence-recovery-benchmark/nmr_benchmark.txt","/home/shared/datasets/pdb/") |