protpy 0.0.3

Python package for generating various biochemical, physiochemical and structural descriptors/features of protein sequences.

protpy

Table of Contents

• Introduction
• Requirements
• Installation
• Usage
• Directories
• Tests
• Issues
• Contact
• References

Introduction

protpy is a Python software package for generating a variety of physiochemical, biochemical and structural descriptors for proteins. All of these descriptors are calculated using sequence-derived or physiochemical features of the amino acids that make up the proteins. These descriptors have been highly studied and used in a series of Bioinformatic applications including protein engineering, SAR (sequence-activity-relationships), predicting protein structure & function, subcellular localization, protein-protein interactions, drug-target interactions etc. The descriptors that are available in protpy include:

• Moreaubroto Autocorrelation
• Moran Autocorrelation
• Geary Autocorrelation
• Amino Acid Composition
• Dipeptide Composition
• Tripeptide Composition
• Pseudo Amino Acid Composition
• Amphiphilic Amino Acid Composition
• Sequence Order Correlation Factor
• Conjoint Triad
• CTD (Composition, Transition, Distribution)
• Sequence Order Coupling Number
• Quasi Sequence Order

This software is aimed at any researcher using protein sequence/structural data and was mainly created to use in my own project pySAR which uses protein sequence data to identify Sequence Activity Relationships (SAR) using Machine Learning. protpy is built solely in Python3 and specifically developed in Python 3.10.

Requirements

• Python >= 3.6
• numpy >= 1.16.0
• pandas >= 1.1.0
• requests >= 2.24.0

Installation

Install the latest version of protpy using pip:

pip3 install protpy --upgrade

Install by cloning repository:

git clone https://github.com/amckenna41/protpy.git
python3 setup.py install

Usage

Import protpy after installation:

import protpy as protpy

Import protein sequence from fasta:

from Bio import SeqIO

with open("test_fasta.fasta") as pro:
    protein_seq = str(next(SeqIO.parse(pro,'fasta')).seq)

Composition Descriptors

Calculate Amino Acid Composition:

amino_acid_comp = protpy.amino_acid_composition(protein_seq)
#

Calculate Dipeptide Composition:

dipeptide_comp = protpy.dipeptide_composition(protein_seq)
#

Calculate Tripeptide Composition:

tripeptide_comp = protpy.tripeptide_composition(protein_seq)
#

Calculate Pseudo Composition:

pseudo_comp = protpy.pseudo_amino_acid_composition(protein_seq)
#

Calculate Amphiphilic Composition:

amphiphilic_comp = protpy.amphiphilic_amino_acid_composition(protein_seq)
#

Autocorrelation Descriptors

Calculate MoreauBroto Autocorrelation:

moreaubroto_autocorrelation = protpy.moreaubroto_autocorrelation(protein_seq)
#

Calculate Moran Autocorrelation:

moran_autocorrelation = protpy.moran_autocorrelation(protein_seq)
#

Calculate Geary Autocorrelation:

geary_autocorrelation = protpy.geary_autocorrelation(protein_seq)
#

Conjoint Triad Descriptors

CTD

Sequence Order Correlation Factor

Sequence Order Coupling Number

Quasi Sequence Order

Directories

• /tests - unit and integration tests for protpy package.
• /protpy - source code and all required external data files for package.
• /images - images used throughout README.
• /docs - protpy documentation.

Tests

To run all tests, from the main protpy folder run:

python3 -m unittest discover test

Contact

If you have any questions or comments, please contact [e-mail] or raise an issue on the Issues tab.

References

[1]: Mckenna, A., & Dubey, S. (2022). Machine learning based predictive model for the analysis of sequence activity relationships using protein spectra and protein descriptors. Journal of Biomedical Informatics, 128(104016), 104016. https://doi.org/10.1016/j.jbi.2022.104016 [2]: Shuichi Kawashima, Minoru Kanehisa, AAindex: Amino Acid index database, Nucleic Acids Research, Volume 28, Issue 1, 1 January 2000, Page 374, https://doi.org/10.1093/nar/28.1.374 [3]: Dong, J., Yao, ZJ., Zhang, L. et al. PyBioMed: a python library for various molecular representations of chemicals, proteins and DNAs and their interactions. J Cheminform 10, 16 (2018). https://doi.org/10.1186/s13321-018-0270-2 [4]: Dong, J., Yao, ZJ., Zhang, L. et al. PyBioMed: a python library for various molecular representations of chemicals, proteins and DNAs and their interactions. J Cheminform 10, 16 (2018). https://doi.org/10.1186/s13321-018-0270-2 [5]: Reczko, M. and Bohr, H. (1994) The DEF data base of sequence based protein fold class predictions. Nucleic Acids Res, 22, 3616-3619. [6]: Hua, S. and Sun, Z. (2001) Support vector machine approach for protein subcellular localization prediction. Bioinformatics, 17, 721-728. [7]: Broto P, Moreau G, Vandicke C: Molecular structures: perception, autocorrelation descriptor and SAR studies. Eur J Med Chem 1984, 19: 71–78. [8]: Ong, S.A., Lin, H.H., Chen, Y.Z. et al. Efficacy of different protein descriptors in predicting protein functional families. BMC Bioinformatics 8, 300 (2007). https://doi.org/10.1186/1471-2105-8-300 [9]: Inna Dubchak, Ilya Muchink, Stephen R.Holbrook and Sung-Hou Kim. Prediction of protein folding class using global description of amino acid sequence. Proc.Natl. Acad.Sci.USA, 1995, 92, 8700-8704. [10]: Juwen Shen, Jian Zhang, Xiaomin Luo, Weiliang Zhu, Kunqian Yu, Kaixian Chen, Yixue Li, Huanliang Jiang. Predicting proten-protein interactions based only on sequences inforamtion. PNAS. 2007 (104) 4337-4341. [11]: Kuo-Chen Chou. Prediction of Protein Subcellar Locations by Incorporating Quasi-Sequence-Order Effect. Biochemical and Biophysical Research Communications 2000, 278, 477-483.<- quasi-seq-order refernece [12]: Kuo-Chen Chou. Prediction of Protein Cellular Attributes Using Pseudo-Amino Acid Composition. PROTEINS: Structure, Function, and Genetics, 2001, 43: 246-255. [13]: Kuo-Chen Chou. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics, 2005,21,10-19.