https://github.com/amckenna41/protpy
Calculating a range of protein descriptors using their physicochemical, biological and structural properties 🔬.
https://github.com/amckenna41/protpy
bioinformatics biological-data-analysis protein-sequences protein-structure protein-structure-prediction pypi pysar python
Last synced: about 2 months ago
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Calculating a range of protein descriptors using their physicochemical, biological and structural properties 🔬.
- Host: GitHub
- URL: https://github.com/amckenna41/protpy
- Owner: amckenna41
- License: mit
- Created: 2023-01-12T17:29:07.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-13T16:13:12.000Z (almost 2 years ago)
- Last Synced: 2025-04-07T01:52:53.994Z (6 months ago)
- Topics: bioinformatics, biological-data-analysis, protein-sequences, protein-structure, protein-structure-prediction, pypi, pysar, python
- Language: Python
- Homepage:
- Size: 255 KB
- Stars: 13
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# protpy - Package for generating protein physicochemical, biochemical and structural descriptors using their constituent amino acids. #
[](https://pypi.org/project/protpy/)
[](https://github.com/amckenna41/protpy/actions?query=workflowBuilding%20and%20Testing)
[](https://dl.circleci.com/status-badge/redirect/gh/amckenna41/protPy/tree/main)
[](https://pypi.org/project/protpy/)
[](https://pypi.org/project/protpy/)
[](https://codecov.io/gh/amckenna41/protPy)
[](https://opensource.org/licenses/MIT)
[](https://github.com/amckenna41/protpy/issues)
* A demo of the software is available [here][demo]
* A Medium article about `protPy` and its background etc is available [here][article]
Table of Contents
-----------------
* [Introduction](#introduction)
* [Requirements](#requirements)
* [Installation](#installation)
* [Usage](#usage)
* [Directories](#directories)
* [Tests](#tests)
* [Issues](#Issues)
* [Contact](#contact)
* [References](#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`](https://github.com/amckenna41/pySAR) which uses protein sequence data to identify Sequence Activity Relationships (SAR) using Machine Learning [[1]](#references). `protpy` is built and developed in Python 3.10.
Requirements
------------
* [Python][python] >= 3.8
* [aaindex][aaindex] >= 1.1.2
* [numpy][numpy] >= 1.16.0
* [pandas][pandas] >= 1.1.0
* [varname][varname] >= 0.12.0
* [biopython][biopython] >= 1.81 (only required for testing)
Installation
------------
Install the latest version of `protpy` using pip:
```bash
pip3 install protpy --upgrade
```
Install by cloning repository:
```bash
git clone https://github.com/amckenna41/protpy.git
python3 setup.py install
```
Usage
-----
### Import `protpy` after installation:
```python
import protpy as protpy
```
### Import protein sequence from fasta:
```python
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:
```python
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:
```python
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:
```python
tripeptide_composition = protpy.tripeptide_composition(protein_seq)
# AAA AAC AAD AAE AAF ...
# 1 0 0 2 0 ...
```
Calculate Pseudo Composition:
```python
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:
```python
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:
```python
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:
```python
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:
```python
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:
```python
conjoint_triad = protpy.conjoint_triad(protein_seq)
# 111 112 113 114 115 ...
# 7 17 11 3 6 ...
```
### CTD Descriptors
Calculate CTD:
```python
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):
```python
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:
```python
#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):
```python
#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 amckenna41@qub.ac.uk or raise an issue on the [Issues][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](#TOP)
[python]: https://www.python.org/downloads/release/python-360/
[protpy]: https://github.com/amckenna41/protpy
[aaindex]: https://github.com/amckenna41/aaindex
[varname]: https://pypi.org/project/varname/
[biopython]: https://biopython.org/
[numpy]: https://numpy.org/
[pandas]: https://pandas.pydata.org/
[PyPi]: https://pypi.org/project/protpy/
[demo]: https://colab.research.google.com/drive/12E3ayovpZOf6Gv-8ILwkN1zReKslEksB?usp=sharing
[article]: https://medium.com/@ajmckenna69/protpy-05f02f821baa
[Issues]: https://github.com/amckenna41/protpy/issues