proteinpy - handling protein structures using dataframes

proteinpy is a bioinformatic package for parsing and analysis of protein structure data (PDB, DSSP and CCP4 format) using pandas dataframes. This is a tool developed for my research work on computational protein structural physics. In structural biology, there hasn't been a handy Python package that converts PDB files to pandas dataframe, which allows using various machine learning methods to get biophysical insights from PDB bank. Therefore I decided to create this package.

Installation and Usage

proteinpy can be installed from Github by cloning this repository to your local directory.

git clone https://github.com/haocai1992/proteinpy.git

If you don't have git, install git (on Linux) before cloning:

sudo apt-get install git

After cloning this repository, run the following code:

cd proteinpy
sudo python setup.py install

Replace python with your specific version if required, for example python3, or pypy3.

Usage and Examples

Import proteinpy package

Before using proteinpy, you will need to import it in your python file:

from proteinpy import *

Parse PDB/DSSP/CCP4 file

You can parse your local files into a Parser object in one line:

# Parse local PDB (and DSSP/CCP4) file in your local directory.
parser = Parser('3eiy')

Use Structure class to analyze protein structures

Then, you can analyze protein structures using dataframe:

# protein structure dataframe.
structure = parser.structure

You can get dataframes for different sets of atoms in this structure:

# all atoms.
allatoms = structure.allatoms

# all residue atoms.
resatoms = structure.resatoms

# all water (oxygen) atoms.
waters = structure.waters

# all "buried" water atoms - water that are not exposed to 
# the external surface.
buriedwaters = structure.buriedwaters

You can also get the 3D coordinates (x, y, z) of a specified atom in the structure:

special_atom = '3eiy.A.3.ARG.CA'
special_atom_coords = structure.get_coordinate(special_atom)

Use Contact class to study protein-protein and protein-water interactions

The main purpose of my research was to look at intra-molecular interactions. Therefore I created a Contact class to store interactions between atoms:

# protein contact dataframe.
contact = parser.contact

You can get dataframes for different kinds of atom-atom contacts:

# all-to-all atom contact.
all2all_contact = contact.all2all

# residue-to-residue contact.
res2res_contact = contact.res2res

# water-to-water contact.
water2water_contact = contact.water2water

# water-to-residue contact.
water2res_contact = contact.water2res

For any two atoms in contact, you can also get a (x, y, z) vector connecting them.

# get a vector for two atoms.
vec = contact.get_vector(atom1='3eiy.A.3.ARG.CA',\
						 atom2='3eiy.A.12.LYS.CB')

Use Network class to explore interaction network in protein structure

In protein structure, every atom interaction with other atoms and forms a interaction network. I created a Network class to represent this network:

# protein contact network.
network = parser.network

I use Python's NetworkX package to store network data, and also to explore special shapes, for example water tetrahedrons, pentagons, and hexagons within this network:

# entire network of all atom-atom contacts.
allnet = network.allnet

# water network in protein structure.
waternet = network.waternet

# water tetrahedrons.
watertetrahedrons = network.watertetrahedrons

# water pentagons.
waterpentagons = network.waterpentagons

# water hexagons.
waterhexagons = network.waterhexagons

You can also use functions in NetworkX to get graph-related biophysical features:

# Number of water oxygen atoms in water network.
num_atoms = waternet.number_of_nodes()

# Number of hydrogen bonds in water network.
num_hbonds = waternet.number_of_edges()

# See how many hydrogen does one water have with other water atoms.
selected_water_num_hbonds = waternet.degree(['3eiy.A.100.HOH.O'])

Future Improvements

proteinpy was built mainly for geometric analysis for intra-molecular protein interactions. Currently, it is not capable to perform tasks such as calculating the accessible surface area of a residue, or superimposing two structures. Future improvements include:

• Fetching PDB/DSSP/CCP4 files from rcsb.org instead of locally
• Visualization of protein networks
• More strcutural biology calculations

Contact

• Author: Hao Cai
• Email: haocai3@gmail.com
• Google Scholar: https://scholar.google.com/citations?user=WueH2rAAAAAJ&hl=en
• Linkedin: https://www.linkedin.com/in/haocai1992/