This repository contains the datasets and scripts pertaining to the publication
CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parameters kcat, Km and Ki
Citation
CatPred biorxiv pre-print:@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}
}Table of contents
Datasets referenced in the publication can be found in these directories
./datasets/
├── processed # processed datasets one each for kcat, Km and Ki
├── splits # training/validation/test dataset splits for kcat, Km and Ki
├── all_natural_metabolite_names.json # compiled names of natural metabolites from BRENDA
├── metabolite_inchi_smiles_brenda_pubchem.tsv # compiled list of inchi and smiles strings for brenda molecules
Custom scripts used for processing, splitting and analysis can be found in these directories
./scripts/
├── processing # for processing brenda/sabio datasets and merging them
├── pdb # for creating files required for datasets including structural information (pdb)
├── add_pdb_paths.py # adds pdb paths to a dataset file
├── count_natural.py # counts natural vs. non-natural entries
├── create_pdbrecords.py # create pdb records files to go along with datasets required for training
├── make_splits.py # create train/valid/test splits from processed datasets
This source code is licensed under the MIT license found in the LICENSE file
in the root directory of this source tree.