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https://github.com/ohuelab/QEPPI
Quantitative Estimate Index for Early-Stage Screening of Compounds Targeting Protein-Protein Interactions
https://github.com/ohuelab/QEPPI
Last synced: 3 months ago
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Quantitative Estimate Index for Early-Stage Screening of Compounds Targeting Protein-Protein Interactions
- Host: GitHub
- URL: https://github.com/ohuelab/QEPPI
- Owner: ohuelab
- License: mit
- Created: 2021-09-12T12:27:01.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-11-28T08:02:05.000Z (12 months ago)
- Last Synced: 2024-07-19T18:42:23.644Z (4 months ago)
- Language: Python
- Homepage:
- Size: 1.53 MB
- Stars: 17
- Watchers: 4
- Forks: 7
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# QEPPI
**Quantitative estimate index for early-stage screening of compounds targeting protein-protein interactions**
[](LICENSE)
[](https://github.com/ohuelab/QEPPI/)
[](https://hits.seeyoufarm.com/)
[](https://pypi.org/project/QEPPI/)
[](https://github.com/ohuelab/QEPPI)
## Calculation QEPPI with using Google Colab
We have made it so that you can use Google Colab to calculate QEPPI from SMILES without creating your own environment.
If you have a lot of SMILES to calculate, please convert the SMILES to SDF files.
[](http://colab.research.google.com/github/ohuelab/QEPPI/blob/main/notebook/QEPPI.ipynb)
## Mininal environment setup (Git clone)
We setup it on a Mac (macOS10.15.7), but I'm sure it will run fine on other platforms such as Linux.
```bash
# Python 3.7 | 3.8
# dependencies
pip install rdkit-pypi # >= 2021.3.1.5
pip install numpy # >= 1.19.5
pip install pandas # >= 1.1.5
```
We also confirmed that QEPPI works with Colab. (see [notebook](https://github.com/ohuelab/QEPPI/blob/main/notebook/QEPPI.ipynb))
### Clone QEPPI
Clone QEPPI repository when you are done with the setup.
```bash
git clone https://github.com/ohuelab/QEPPI.git
```
### Test
Test it after git clone the QEPPI repository. If the test passes, the QEPPI calculation has been successfully performed. (We used pytest version is 6.2.2)
```bash
cd QEPPI
pytest -v
```
## QEPPI calculation example
```bash
# for .sdf
python calc_QEPPI.py --sdf PATH_TO_YOUR_COMPOUND.sdf --out PATH_TO_OUTPUT.csv
```
```bash
# for .csv ("A column name of "SMILES" is required.")
python calc_QEPPI.py --csv PATH_TO_YOUR_COMPOUND.csv --out PATH_TO_OUTPUT.csv
```
## Instalation using pip install
You can also install QEPPI with ```pip install QEPPI```. The following sample code is available as an implementation example.
Note: some dependancies will also be installed with QEPPI module, so a clean environment is preferred!
```bash
# QEPPI
pip install QEPPI
```
```python
import QEPPI as ppi
from rdkit import Chem
from rdkit.Chem import SDMolSupplier
q = ppi.QEPPI_Calculator()
q.read()
# SMILES
smiles = "COC1=CC(=CC=C1NC(=O)[C@@H]1N[C@@H](CC(C)(C)C)[C@@](C#N)([C@H]1C1=CC=CC(Cl)=C1F)C1=CC=C(Cl)C=C1F)C(O)=O"
mol = Chem.MolFromSmiles(smiles)
print(q.qeppi(mol))
# 0.7862842663145835
# SDF
ppi_s = SDMolSupplier("PATH_TO_SDF/YOUR_COMPOUND.sdf")
ppi_mols = [mol for mol in ppi_s if mol is not None]
result = list(map(q.qeppi, ppi_mols))
```
## Reference
If you find QEPPI useful, please consider citing this publication;
- Kosugi T, Ohue M. [**Quantitative estimate index for early-stage screening of compounds targeting protein-protein interactions**](https://www.mdpi.com/1422-0067/22/20/10925). _International Journal of Molecular Sciences_, 22(20): 10925, 2021. doi: 10.3390/ijms222010925
Another QEPPI publication (conference paper)
- Kosugi T, Ohue M. [**Quantitative estimate of protein-protein interaction targeting drug-likeness**](https://ieeexplore.ieee.org/abstract/document/9562931/). In _Proceedings of The 18th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2021)_, 2021. doi: 10.1109/CIBCB49929.2021.9562931 [(PDF)](https://www.li.c.titech.ac.jp/paper/Kosugi-CIBCB2021.pdf) * © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.