protpy 1.2.1
Python package for generating various biochemical, physicochemical and structural descriptors/features of protein sequences.
License: MIT
Requires Python: >=3.6
Keywords: bioinformatics,protein engineering,python,pypi,machine learning,aaindex,protein descriptors,physicochemical descriptors,biochemical descriptorsstructural descriptors,pySAR


# protpy - Package for generating protein physicochemical, biochemical and structural descriptors using their constituent amino acids. #
[![PyPI](https://img.shields.io/pypi/v/protpy)](https://pypi.org/project/protpy/)
[![pytest](https://github.com/amckenna41/protpy/workflows/Building%20and%20Testing/badge.svg)](https://github.com/amckenna41/protpy/actions?query=workflowBuilding%20and%20Testing)
[![CircleCI](https://dl.circleci.com/status-badge/img/gh/amckenna41/protPy/tree/main.svg?style=svg)](https://dl.circleci.com/status-badge/redirect/gh/amckenna41/protPy/tree/main)
[![Platforms](https://img.shields.io/badge/platforms-linux%2C%20macOS%2C%20Windows-green)](https://pypi.org/project/protpy/)
[![PythonV](https://img.shields.io/pypi/pyversions/protpy?logo=2)](https://pypi.org/project/protpy/)
[![codecov](https://codecov.io/gh/amckenna41/protPy/graph/badge.svg?token=H508M9J13W)](https://codecov.io/gh/amckenna41/protPy)
[![License: MIT](https://img.shields.io/badge/License-MIT-red.svg)](https://opensource.org/licenses/MIT)
[![Issues](https://img.shields.io/github/issues/amckenna41/protpy)](https://github.com/amckenna41/protpy/issues)
<!-- [![Size](https://img.shields.io/github/repo-size/amckenna41/protpy)](https://github.com/amckenna41/protpy)
[![Commits](https://img.shields.io/github/commit-activity/w/amckenna41/protpy)](https://github.com/amckenna41/protpy) -->
<!-- <p align="center">
<img src="https://images.newscientist.com/wp-content/uploads/2021/07/22155326/22-july_deepmind-proteome.jpg?width=300" alt="protpyLogo" height="200"/>
</p> -->

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.

<strong>A demo of the software is available [here][demo].</strong>
<strong>A Medium article about protPy and its background etc is available [here][article].</strong>

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 <br>
\[2\]: 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 <br>
\[3\]: 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 <br>
\[4\]: Reczko, M. and Bohr, H. (1994) The DEF data base of sequence based protein
fold class predictions. Nucleic Acids Res, 22, 3616-3619. <br>
\[5\]: Hua, S. and Sun, Z. (2001) Support vector machine approach for protein
subcellular localization prediction. Bioinformatics, 17, 721-728. <br>
\[6\]: Broto P, Moreau G, Vandicke C: Molecular structures: perception,
autocorrelation descriptor and SAR studies. Eur J Med Chem 1984, 19: 71–78. <br>
\[7\]: 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 <br>
\[8\]: 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. <br>
\[9\]: 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. <br>
\[10\]: Kuo-Chen Chou. Prediction of Protein Subcellar Locations by Incorporating
Quasi-Sequence-Order Effect. Biochemical and Biophysical Research
Communications 2000, 278, 477-483. <br>
\[11\]: Kuo-Chen Chou. Prediction of Protein Cellular Attributes Using
Pseudo-Amino Acid Composition. PROTEINS: Structure, Function, and
Genetics, 2001, 43: 246-255. <br>
\[12\]: Kuo-Chen Chou. Using amphiphilic pseudo amino acid composition to predict enzyme
subfamily classes. Bioinformatics, 2005,21,10-19.

Support
-------
<a href="https://www.buymeacoffee.com/amckenna41" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy Me A Coffee" height="41" width="174"></a>

[Back to top](#TOP)

<!-- Links -->
[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/
[article]: https://www.sciencedirect.com/science/article/abs/pii/S1532046422000326
[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