protpy 1.2.1

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

protpy - Package for generating protein physicochemical, biochemical and structural descriptors using their constituent amino acids.

Table of Contents

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

Introduction

protpy is a Python software package for generating a variety of physicochemical, biochemical and structural descriptors for proteins. All of these descriptors are calculated using sequence-derived or physicochemical 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 available in protpy include:

• Amino Acid Composition (AAComp)
• Dipeptide Composition (DPComp)
• Tripeptide Composition (TPComp)
• Pseudo Amino Acid Composition (PAAComp)
• Amphiphilic Amino Acid Composition (APAAComp)
• Moreaubroto Autocorrelation (MBAuto)
• Moran Autocorrelation (MAuto)
• Geary Autocorrelation (GAuto)
• Conjoint Triad (CTriad)
• CTD (Composition, Transition, Distribution) (CTD)
• Sequence Order Coupling Number (SOCN)
• Quasi Sequence Order (QSO)

This software is aimed at any researcher or developer 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 [1]. protpy is built and developed in Python 3.10.

A demo of the software is available here. A Medium article about protPy and its background etc is available here.

Requirements

• Python >= 3.8
• aaindex >= 1.1.2
• numpy >= 1.16.0
• pandas >= 1.1.0
• varname >= 0.12.0
• biopython >= 1.81 (only required for testing)

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_composition = protpy.amino_acid_composition(protein_seq)
# A      C      D      E      F ...
# 6.693  3.108  5.817  3.347  6.614 ...

Calculate Dipeptide Composition:

dipeptide_composition = protpy.dipeptide_composition(protein_seq)
# AA    AC    AD   AE    AF ...
# 0.72  0.16  0.48  0.4  0.24 ...

Calculate Tripeptide Composition:

tripeptide_composition = protpy.tripeptide_composition(protein_seq)
# AAA  AAC  AAD  AAE  AAF ...
# 1    0    0    2    0 ...

Calculate Pseudo Composition:

pseudo_composition = protpy.pseudo_amino_acid_composition(protein_seq) 
#using default parameters: lamda=30, weight=0.05, properties=[]

# PAAC_1  PAAC_2  PAAC_3  PAAC_4  PAAC_5 ...
# 0.127        0.059        0.111        0.064        0.126 ...

Calculate Amphiphilic Composition:

amphiphilic_composition = protpy.amphiphilic_amino_acid_composition(protein_seq)
#using default parameters: lamda=30, weight=0.5, properties=[hydrophobicity_, hydrophilicity_]

# APAAC_1  APAAC_2  APAAC_3  APAAC_4  APAAC_5 ...
# 6.06    2.814    5.267     3.03    5.988 ...

Autocorrelation Descriptors

Calculate MoreauBroto Autocorrelation:

moreaubroto_autocorrelation = protpy.moreaubroto_autocorrelation(protein_seq)
#using default parameters: lag=30, properties=["CIDH920105", "BHAR880101", "CHAM820101", "CHAM820102", "CHOC760101", "BIGC670101", "CHAM810101", "DAYM780201"], normalize=True

# MBAuto_CIDH920105_1  MBAuto_CIDH920105_2  MBAuto_CIDH920105_3  MBAuto_CIDH920105_4  MBAuto_CIDH920105_5 ...  
# -0.052               -0.104               -0.156               -0.208               0.246 ...

Calculate Moran Autocorrelation:

moran_autocorrelation = protpy.moran_autocorrelation(protein_seq)
#using default parameters: lag=30, properties=["CIDH920105", "BHAR880101", "CHAM820101", "CHAM820102", "CHOC760101", "BIGC670101", "CHAM810101", "DAYM780201"], normalize=True

# MAuto_CIDH920105_1  MAuto_CIDH920105_2  MAuto_CIDH920105_3  MAuto_CIDH920105_4  MAuto_CIDH920105_5 ...
# -0.07786            -0.07879            -0.07906            -0.08001            0.14911 ...

Calculate Geary Autocorrelation:

geary_autocorrelation = protpy.geary_autocorrelation(protein_seq)
#using default parameters: lag=30, properties=["CIDH920105", "BHAR880101", "CHAM820101", "CHAM820102", "CHOC760101", "BIGC670101", "CHAM810101", "DAYM780201"], normalize=True

# GAuto_CIDH920105_1  GAuto_CIDH920105_2  GAuto_CIDH920105_3  GAuto_CIDH920105_4  GAuto_CIDH920105_5 ...
# 1.057               1.077               1.04                1.02                1.013 ...

Conjoint Triad Descriptors

Calculate Conjoint Triad:

conjoint_triad = protpy.conjoint_triad(protein_seq)
# 111  112  113  114  115 ...
# 7    17   11   3    6 ...

CTD Descriptors

Calculate CTD:

ctd = protpy.ctd(protein_seq)
#using default parameters: property="hydrophobicity", all_ctd=True

# hydrophobicity_CTD_C_01  hydrophobicity_CTD_C_02  hydrophobicity_CTD_C_03  normalized_vdwv_CTD_C_01 ...
# 0.279                    0.386                    0.335                    0.389 ...                   

Sequence Order Descriptors

Calculate Sequence Order Coupling Number (SOCN):

socn = protpy.sequence_order_coupling_number_(protein_seq)
#using default parameters: d=1, distance_matrix="schneider-wrede"

#401.387        

Calculate all SOCN's per distance matrix:

#using default parameters: lag=30, distance_matrix="schneider-wrede"
socn_all = protpy.sequence_order_coupling_number(protein_seq)

# SOCN_SW1  SOCN_SW2  SOCN_SW3  SOCN_SW4  SOCN_SW5 ...
# 401.387    409.243    376.946    393.042    396.196 ...  

#using custom parameters: lag=10, distance_matrix="grantham"
socn_all = protpy.sequence_order_coupling_number(protein_seq, lag=10, distance_matrix="grantham")      

# SOCN_Grant1  SOCN_Grant_2  SOCN_Grant_3  SOCN_Grant_4  SOCN_Grant_5 ...
# 399.125    402.153    387.820    393.111    409.096 ...  

Calculate Quasi Sequence Order (QSO):

#using default parameters: lag=30, weight=0.1, distance_matrix="schneider-wrede"
qso = protpy.quasi_sequence_order(protein_seq)

# QSO_SW1   QSO_SW2   QSO_SW3   QSO_SW4   QSO_SW5 ...
# 0.005692  0.002643  0.004947  0.002846  0.005625 ...  

#using custom parameters: lag=10, weight=0.2, distance_matrix="grantham"
qso = protpy.quasi_sequence_order(protein_seq, lag=10, weight=0.2, distance_matrix="grantham")

# QSO_Grant1   QSO_Grant2   QSO_Grant3   QSO_Grant4   QSO_Grant5 ...
# 0.123287  0.079967  0.04332  0.039983  0.013332 ...  

Directories

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

Tests

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

python3 -m unittest discover tests -v
-v: verbose output flag

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]: Reczko, M. and Bohr, H. (1994) The DEF data base of sequence based protein fold class predictions. Nucleic Acids Res, 22, 3616-3619.
[5]: Hua, S. and Sun, Z. (2001) Support vector machine approach for protein subcellular localization prediction. Bioinformatics, 17, 721-728.
[6]: Broto P, Moreau G, Vandicke C: Molecular structures: perception, autocorrelation descriptor and SAR studies. Eur J Med Chem 1984, 19: 71–78.
[7]: 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
[8]: 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.
[9]: 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.
[10]: Kuo-Chen Chou. Prediction of Protein Subcellar Locations by Incorporating Quasi-Sequence-Order Effect. Biochemical and Biophysical Research Communications 2000, 278, 477-483.
[11]: Kuo-Chen Chou. Prediction of Protein Cellular Attributes Using Pseudo-Amino Acid Composition. PROTEINS: Structure, Function, and Genetics, 2001, 43: 246-255.
[12]: Kuo-Chen Chou. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics, 2005,21,10-19.

Support

Back to top