building script for inspecting pdb

scripts/examples/inspect.py

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+#!/usr/bin/env python
+import os
+from Bio.PDB import *
+from biopandas.pdb import PandasPdb
+from collections import defaultdict, OrderedDict
+import pandas as pd
+from functools import reduce
+#%% see verison of pandas
+#print(pd.__version__)
+
+#%%
+homedir = os.path.expanduser('~')
+os.chdir(homedir + '/git/LSHTM_analysis/scripts/examples')
+# link
+#https://www.pythonprogramming.in/pandas-count-distinct-values-of-one-column-depend-on-another-column.html
+#https://datascience.stackexchange.com/questions/32328/export-pandas-to-dictionary-by-combining-multiple-row-values
+
+# 3 way merge
+#https://stackoverflow.com/questions/23668427/pandas-three-way-joining-multiple-dataframes-on-columns
+#https://stackoverflow.com/questions/52223045/merge-multiple-dataframes-based-on-a-common-column
+
+#%% Read data
+file_list = ['7bvf_b.pdb']
+file_list = ['3byw.pdb']
+#file_list = ['7bvf_b.pdb', 'pnca_complex.pdb', '3byw']
+#%%
+
+for pdb in file_list:
+    print(pdb)
+    p = PDBParser()
+    structure = p.get_structure(pdb, pdb)
+    for model in structure:
+        for chain in model:
+            for residue in chain:
+                for atom in residue:
+                    #print(atom)
+
+#%% biopandas
+pdb_dict = {}
+for pdb_id in file_list:
+    ppdb = PandasPdb()
+    pdb_file = ppdb.read_pdb(pdb_id)
+    #dir(pdb_file)
+    atm_df = pdb_file.df['ATOM']
+    #print('column names:', atm_df.columns)
+     
+    pdb_chains = list(set(atm_df['chain_id']))
+    print('pdb chains:', pdb_chains)
+    
+    total_chains = len(pdb_chains)
+    print('total no. of chains:', total_chains)
+    
+    chain_info = {}
+    #atm_df_s = atm_df.sort_values(by=['atom_number', 'chain_id', 'residue_number'])
+    c_start = atm_df.groupby('chain_id').residue_number.min()
+    print(c_start)
+    c_start_df = pd.DataFrame({'chain_id': c_start.index, 'start_res': c_start.values})    
+    
+    c_end = atm_df.groupby('chain_id').residue_number.max()
+    print(c_end)
+    c_end_df = pd.DataFrame({'chain_id': c_end.index, 'end_res': c_end.values})   
+    
+    c_length = atm_df.groupby('chain_id').residue_number.nunique()
+    print(c_length)
+    c_length_df = pd.DataFrame({'chain_id': c_length.index, 'chain_len': c_length.values})   
+    
+    # combine 3 series into and assign 'chain_id' as a column
+    # using rlambda function works well (as it should work with whatever number of dataframes you want to merge)
+    # using pd.concat creates extra chain id cols
+    c_df = reduce(lambda left,right: pd.merge(left,right, on = 'chain_id'), [c_start_df, c_end_df, c_length_df])
+    
+    # convert df to dict with 'chain_id' as key and columns as list of values
+    chain_dict = c_df.set_index('chain_id').T.to_dict('list')
+    print(chain_dict)
+#%% Idea
+#protein_name: total_chains: 8, total ligands/hetatm = 3
+#df of chain details
+#chain start_res end_res len_chain
+#pdb tools script separate one chain 
+
+# remove water and