maranasgroup / CatPred

Machine Learning models for in vitro enzyme kinetic parameter prediction

CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parameters kcat, Km and Ki

Table of Contents

• Google Colab Interface Demo
• Local Demo
  • System Requirements
  • Installing using pip
  • Run demo
• Acknowledgements
• License

Google Colab Interface Demo (easy)

For ease of use without any hardware requirements, a Google Colab interface is available here: tiny.cc/catpred. It contains sample data, instructions and installation all in the Colab notebook.

Local Demo

If you would like to install the package on a local machine, please follow the following instructions.

System Requirements

For using pre-trained models to predict, any machine running a Linux based operating system is recommended. For training, we recommend using a Linux based operating system on a GPU-enabled machine.

Both training and prediction have been tested on Ubuntu 20.04.5 LTS with NVIDIA A10 and CUDA Version: 12.0

To train with GPUs, you will need:

• cuda >= 11.7
• cuDNN

Installation

Both options require conda, so first install Miniconda from https://conda.io/miniconda.html.

Then proceed to either option below to complete the installation. If installing the environment with conda seems to be taking too long, you can also try running conda install -c conda-forge mamba and then replacing conda with mamba in each of the steps below.

Note for machines with GPUs: You may need to manually install a GPU-enabled version of PyTorch by following the instructions here. If you're encountering issues with not using a GPU on your system after following the instructions below, check which version of PyTorch you have installed in your environment using conda list | grep torch or similar. If the PyTorch line includes cpu, please uninstall it using conda remove pytorch and reinstall a GPU-enabled version using the instructions at the link above.

Installing and downloading pre-trained models (~5 mins)

1. git clone https://github.com/maranasgroup/catpred.git
2. cd catpred
3. conda env create -f environment.yml
4. conda activate catpred
5. pip install -e .
6. pip install ipdb fair-esm rotary_embedding_torch egnn_pytorch -q
7. wget https://catpred.s3.amazonaws.com/production_models.tar.gz -q
8. wget https://catpred.s3.amazonaws.com/processed_databases.tar.gz -q
9. tar -xzf production_models.tar.gz
10. tar -xzf processed_databases.tar.gz

Run a demo (~2 mins)

Use the demo.ipynb jupyter notebook to run the demo.

Reproducing publication training/results

In order to train publication models, you must download and extract training datasets using

wget https://https://catpred.s3.amazonaws.com/publication_training_datasets.tar.gz -q
tar -xzf publication_training_datasets.tar.gz

Training

TODO: Will be made available upon publication

Acknowledgements

Majority of the functionality in this codebase has been adapted from the chemprop library. We thank the authors of chemprop for open-sourcing it. Chemprop

License

This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree.

Citations

If you find the models useful in your research, we ask that you cite the relevant paper:

@article {Boorla2024.03.10.584340,
	author = {Veda Sheersh Boorla and Costas D. Maranas},
	title = {CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parameters kcat, Km and Ki},
	elocation-id = {2024.03.10.584340},
	year = {2024},
	doi = {10.1101/2024.03.10.584340},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2024/03/26/2024.03.10.584340},
	eprint = {https://www.biorxiv.org/content/early/2024/03/26/2024.03.10.584340.full.pdf},
	journal = {bioRxiv}
}