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