Download data/dataset_visualization/dataset_visualization.py from introvoyz041/PDBench: direct link, hf CLI and curl.
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https://huggingface.co/datasets/introvoyz041/PDBench/resolve/main/data/dataset_visualization/dataset_visualization.py
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hf download hf://datasets/introvoyz041/PDBench/data/dataset_visualization/dataset_visualization.py
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curl -L -o dataset_visualization.py https://huggingface.co/datasets/introvoyz041/PDBench/resolve/main/data/dataset_visualization/dataset_visualization.py
4.05 kB
| 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/") |