https://github.com/paccmann/paccmann_kinase_binding_residues
Comparison of active site and full kinase sequences for drug-target affinity prediction and molecular generation. Full paper: https://pubs.acs.org/doi/10.1021/acs.jcim.1c00889
https://github.com/paccmann/paccmann_kinase_binding_residues
active-sites kinases knn machine-learning protein-ligand-interactions proteochemometrics
Last synced: 2 months ago
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Comparison of active site and full kinase sequences for drug-target affinity prediction and molecular generation. Full paper: https://pubs.acs.org/doi/10.1021/acs.jcim.1c00889
- Host: GitHub
- URL: https://github.com/paccmann/paccmann_kinase_binding_residues
- Owner: PaccMann
- License: mit
- Created: 2021-07-19T14:57:14.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2022-10-03T10:55:10.000Z (over 2 years ago)
- Last Synced: 2025-03-24T16:53:24.756Z (3 months ago)
- Topics: active-sites, kinases, knn, machine-learning, protein-ligand-interactions, proteochemometrics
- Language: Python
- Homepage:
- Size: 7.5 MB
- Stars: 36
- Watchers: 4
- Forks: 6
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Active sites outperform full proteins for modeling kinases
[](https://github.com/PaccMann/paccmann_kinase_binding_residues/actions/workflows/python-package.yml)
[](https://opensource.org/licenses/MIT)
[](https://github.com/psf/black)
[](https://doi.org/10.1021/acs.jcim.1c00889)
[](https://doi.org/10.1021/acs.jcim.2c00840)
## Summary
This repository contains data & code for the JCIM paper: [Active Site Sequence Representations of Human Kinases Outperform Full Sequence Representations for Affinity Prediction and Inhibitor Generation: 3D Effects in a 1D Model](https://pubs.acs.org/doi/10.1021/acs.jcim.1c00889). We study the impact of different protein sequence representations for modeling human kinases. We find that using **active site residues yields superior performance to using full protein sequences for predicting binding affinity**. We also study the difference of active site vs. full sequence on de-novo design tasks. We generate kinase inhibitors directly from protein sequences with our previously developed hybrid-VAE (PaccMannRL) but find no major differences between both kinase representations.
## News
- October 2022: Our report on comparing different definitions of kinase active sites has been published as a *letter* in the ACS [**Journal of Chemical Information & Modeling**](https://pubs.acs.org/doi/full/10.1021/acs.jcim.2c00840).
Therein, we also propose several **novel protein sequence augmentation strategies**.
- March 2022: We added a report with new experiments to compare our original active site sequence representations ([29 residues, see *Sheridan et al. (2012)*](https://pubs.acs.org/doi/abs/10.1021/ci900176y) with the 16 residues identified by [*Martin et al. (2010)](https://pubs.acs.org/doi/10.1021/ci200314j). (The report is now longer available since it has been superseded by the [JCIM letter](https://pubs.acs.org/doi/full/10.1021/acs.jcim.2c00840).)
- January 2022: We are proud to be featured on [**the JCIM cover**](https://pubs.acs.org/toc/jcisd8/62/2) with _an AI artwork_! 👉 
- December 2021: Our work has been published in the ACS [**Journal of Chemical Information & Modeling**](https://pubs.acs.org/doi/10.1021/acs.jcim.1c00889).
- December 2021: The part about binding affinity prediction was presented at the [NeurIPS 2021 workshop on *Machine Learning for Structural Biology*](https://www.mlsb.io) and the [ELLIS Machine Learning for Molecule Discovery workshop](https://moleculediscovery.github.io/workshop2021/) alongside NeurIPS 2021.
- November 2021: Our work has **won** the [🥇 #IOPP best poster award🥇](https://ioppublishing.org/twitter-conference/) in the category *Biomedical engineering*
- July 2021: A preliminary version of our work was presented at the [Twitter #IOPPposter conference](https://ioppublishing.org/twitter-conference/) (see GIF below ⬇️))
## Description
This repository facilitates the reproduction of the experiments conducted in the JCIM paper [`Active Site Sequence Representations of Human Kinases Outperform Full Sequence Representations for Affinity Prediction and Inhibitor Generation: 3D Effects in a 1D Model`](https://pubs.acs.org/doi/10.1021/acs.jcim.1c00889). We provide scripts to:
1. Train and evaluate the BimodalMCA for drug-protein affinity prediction
2. Evaluate the bimodal KNN affinity predictor either in a CV setting or on a plain train/test script
3. Optimize a SMILES- or SELFIES-based molecular generative model to produce molecules with high binding affinities for a protein of interest (affinity is predicted with the KNN model).
### Data
The preprocessed BindingDB data (CV and test data for ligand split and kinase split, data used for pretraining and affinity optimization) can be accessed on this [Box link](https://ibm.biz/active_site_data). We also release the aligned active site sequences (29 residues) for all kinases. If you use the data, please [cite](#citation) our work.
## Installation
The core functionality of this repo is provided in the `pkbr` package which can be installed (in editable mode) by `pip install -e .`.
In order to execute the example scripts, we recommend setting up a conda environment:
```console
conda env create -f conda.yml
conda activate pkbr
```
Afterwards you can execute the scripts to run the KNN, the BiMCA or the affinity optimization.
## Code examples
All examples assume that you downloaded the data from [this Box link](https://ibm.biz/active_site_data) and stored it in a folder `data` in the root of this repo.
### Running the KNN affinity predictor in a cross validation
```sh
python3 scripts/knn_cv.py -d data/ligand_split -tr train.csv -te validation.csv \
-f 10 -lp data/ligands.smi -kp data/human_kinases_active_site.smi -r my_cv_results
```
### Running KNN in single train/test split
```sh
python3 scripts/knn_test.py -t data/ligand_split/fold_0/validation.csv -test data/ligand_split/fold_0/test.csv \
-tlp data/ligands.smi -telp data/ligands.smi -kp data/human_kinases_active_site.smi -r my_results.csv
```
### Training the BiMCA model
```sh
python3 scripts/bimca_train.py \
data/ligand_split/fold_0/train.csv data/ligand_split/fold_0/validation.csv data/ligand_split/fold_0/test.csv \
human-kinase-alignment data/human_kinases_active_site.smi data/ligands.smi data/smiles_vocab.json \
models config/active_site.json -n my_as_model
```
### Evaluating the BiMCA model
```sh
python3 scripts/bimca_test.py \
data/ligand_split/fold_0/validation.csv human-kinase-alignment data/human_kinases_sequence.smi data/ligands.smi \
path_to_your_trained_model
```
### Affinity optimization with SMILES generator
To execute this part you need to utilize the pretrained SMILES/SELFIES VAE stored under `data/models`
```sh
python scripts/gp_generation_smiles_knn.py \
data/models/smiles_vae data/affinity_optimization/bindingdb_all_kinase_active_site.csv \
data/affinity_optimization/example_active_site.smi \
smiles_active_site_generator/ -s 42 -t 0.85 -r 10 -n 50 -s 42 -i 40 -c 80
```
### Affinity optimization with SELFIES generator
```sh
python scripts/gp_generation_selfies_knn.py \
data/models/selfies_vae data/affinity_optimization/bindingdb_all_kinase_sequence.csv \
data/affinity_optimization/example_sequence.smi \
selfies_sequence_generator/ -s 42 -t 0.85 -r 10 -n 50 -s 42 -i 40 -c 80
```
## Choosing active-site sequences
See our new letter in [JCIM](https://pubs.acs.org/doi/full/10.1021/acs.jcim.2c00840).
#### Definitions
How to exactly define an "active site" is a critical choice. While we originally relied on
the definition by [Sheridan et al. (2009)](https://pubs.acs.org/doi/10.1021/ci900176y) we have compared now to the active site
definition by [Martin et al. (2012)](https://pubs.acs.org/doi/full/10.1021/ci200314j) and a *Combined* definition that uses a total of
35 residues from either definitions. This improves performance significantly, especially
for allosteric binders.
#### Augmentations
We also devised novel protein sequence augmentation schemes by flipping/swapping
contiguous active-site subsequences that lie discontiguously in the full protein sequence.
To train a model with the new active site definition and the new augmentation, follow the [installation](#installation)
setup and download the data from [Box](https://ibm.biz/active_site_data).
Afterwards run:
```sh
python3 scripts/bimca_train.py \
data/ligand_split/fold_0/train.csv data/ligand_split/fold_0/validation.csv data/ligand_split/fold_0/test.csv \
human-kinase-alignment data/human_kinases_active_site_combined.smi data/ligands.smi data/smiles_vocab.json \
models config/active_site_augment.json -n combined_augment
```
You can modify `config/active_site_augment.json` to play with the hyperparameters and
the probability of the augmentations.
## Citation
If you use this repo, our data, the proposed active site definitions or the sequence augmentation strategies in your projects, please cite the following:
```bib
@article{born2022active,
author = {Born, Jannis and Huynh, Tien and Stroobants, Astrid and Cornell, Wendy D. and Manica, Matteo},
title = {Active Site Sequence Representations of Human Kinases Outperform Full Sequence Representations for Affinity Prediction and Inhibitor Generation: 3D Effects in a 1D Model},
journal = {Journal of Chemical Information and Modeling},
volume = {62},
number = {2},
pages = {240-257},
year = {2022},
doi = {10.1021/acs.jcim.1c00889},
note ={PMID: 34905358},
URL = {https://doi.org/10.1021/acs.jcim.1c00889}
}
@article{born2022on,
author = {Born, Jannis and Shoshan, Yoel and Huynh, Tien and Cornell, Wendy D. and Martin, Eric J. and Manica, Matteo},
title = {On the Choice of Active Site Sequences for Kinase-Ligand Affinity Prediction},
journal = {Journal of Chemical Information and Modeling},
volume = {62},
number = {18},
pages = {4295-4299},
year = {2022},
doi = {10.1021/acs.jcim.2c00840},
URL = {https://doi.org/10.1021/acs.jcim.2c00840}
}
```