pyrotein 0.1.1

A tiny package for structure analysis of macromolecules.

Get started with pyrotein

A tiny package for structure analysis of macromolecules. Atomic coordinates retrieved from a PDB file are stored in two formats: list and dictionary. So you can create your own methods on top of either format. This package provides structure analysis capability based on distance matrix.

Install with pip

pip install pyrotein --user

Dependencies

This package has only one dependency -- numpy.

Why this package doesn't come with a visualization tool?

You can create graphics using your preferred visualization tools. For simplicity, it's a design choice not to include a specific visualization library in pyrotein. However, examples of using Gnuplot and matplotlib are included in the examples directory.

Structure analysis workflow

Import the library

import pyrotein as pr

Load a PDB structure

The following code snippet loads a PDB file 6cmo.pdb under pdb directory.

import pyrotein as pr
import os

# Read coordinates from a PDB file...
drc       =  "pdb"
pdb       =  "6cmo"
fl_pdb    = f"{pdb}.pdb"
pdb_path  = os.path.join(drc, fl_pdb)
atoms_pdb = pr.atom.read(pdb_path)

Create a lookup table to navigate the molecule

The method pr.atom.read returns molecular information encapsulated in a Python List. However, a lookup table can be very handy for tasks such as accesssing a particular atom CA from residue 1002 in chain A. The following example shows how to achieve it using lookup table.

Access an atom

# Create a lookup table for this pdb...
atom_dict = pr.atom.create_lookup_table(atoms_pdb)

# Demo: Access atom `CA` from residue 1002 in chain A
atom_dict["A"][1002]["CA"]

Select a segment by range

The following example demos how to select a segment of protein that represents visual rhodopsin from entry 6cmo.

# Create a lookup table for this pdb...
atom_dict = pr.atom.create_lookup_table(atoms_pdb)

# Fetch residues that form rhodopsin...
chain = "A"
nterm = 1
cterm = 348
rho_dict = pr.atom.extract_segment(atom_dict, chain, nterm, cterm)

Structure analysis capabilities

Extract coordinates from a segment

Coordinates of backbone atoms (N, CA, C, O) are essential for distance matrix analysis. The code below extracts coordinates from a segment of amino acids, which range from nterm to cterm in chain chain.

# Obtain coordinates...
xyzs = pr.atom.extract_backbone_xyz(atom_dict, chain, nterm, cterm)

Distance matrix

# Calculate distance matrix...
dmat = pr.distance.calc_dmat(xyzs, xyzs)

Root-Mean-Square-Deviation distance matrix

Let's say we are interested in chains specified in the file chains.dat.

# chains.dat
6cmo    R
6fk6    A
6fk7    A
6fk8    A
6fk9    A
6fka    A
6fkc    A
6fkd    A
6fuf    A
6nwe    A
6ofj    A
6ofj    B
6oy9    R
6oya    R
6pel    A
6pgs    A
6ph7    A
6qno    R

All entries in chains.dat have been stored in pdb directory.

The code below accumulates coordinates from chains specified in chains.dat. Don't worry. You can run the example code under the examples directory.

import os
import numpy as np
import pyrotein as pr

# Specify chains to process...
drc      = "pdb"
fl_chain = "chains.dat"
lines    = pr.utils.read_file(fl_chain)

# Define the backbone...
backbone = ["N", "CA", "C", "O"]

# Specify the range of atoms from rhodopsin...
nterm = 1
cterm = 348
len_backbone = (cterm - nterm + 1) * len(backbone)

# Initialize the matrix that stores accumulated coordinates...
dmats = np.zeros((len(lines), len_backbone, len_backbone))

# Accumulate coordinates...
for i_fl, (pdb, chain) in enumerate(lines):
    # Read coordinates from a PDB file...
    fl_pdb    = f"{pdb}.pdb"
    pdb_path  = os.path.join(drc, fl_pdb)
    atoms_pdb = pr.atom.read(pdb_path)

    # Create a lookup table for this pdb...
    atom_dict = pr.atom.create_lookup_table(atoms_pdb)

    # Obtain coordinates...
    xyzs = pr.atom.extract_backbone_xyz(atom_dict, chain, nterm, cterm)

    # Calculate individual distance matrix...
    dmat = pr.distance.calc_dmat(xyzs, xyzs)

    # Update the accumulated matrix...
    dmats[i_fl, :, :] = dmat[:, :]

# Calculate RMSD distance matrix...
rmsd_dmat = pr.distance.calc_rmsd_mats(dmats)

Examples

The examples directory contains two examples about distance matrix and RMSD distance matrix. Two visualization choices are provided via Gnuplot and matplotlib.

Here is an sample figure of a RMSD distance matrix in the examples directory.

Caveats

The warning RuntimeWarning: Mean of empty slice is triggered by np.nanmean when the input array has nothing but np.nan values.