SCREEN: A Graph-based Contrastive Learning Tool to Infer Catalytic Residues and Assess Enzyme Mutations
Files
(Published version)
Date
2024
Authors
Pan, T.
Bi, Y.
Wang, X.
Zhang, Y.
Webb, G.
Gasser, R.B.
Kurgan, L.
Song, J.
Editors
Zhang, Z.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
Genomics, Proteomics & Bioinformatics, 2024; 22(6):qzae094-1-qzae094-14
Statement of Responsibility
Tong Pan, Yue Bi, Xiaoyu Wang, Ying Zhang, Geoffrey I Webb, Robin B Gasser, Lukasz Kurgan, Jiangning Song
Conference Name
Abstract
The accurate identification of catalytic residues contributes to our understanding of enzyme functions in biological processes and pathways. The increasing number of protein sequences necessitates computational tools for the automated prediction of catalytic residues in enzymes. Here, we introduce SCREEN, a graph neural network for the high-throughput prediction of catalytic residues via the integration of enzyme functional and structural information. SCREEN constructs residue representations based on spatial arrangements and incorporates enzyme function priors into such representations through contrastive learning. We demonstrate that SCREEN (1) consistently outperforms currently-available predictors; (2) provides accurate results when applied to inferred enzyme structures; and (3) generalizes well to enzymes dissimilar from those in the training set. We also show that the putative catalytic residues predicted by SCREEN mimic key structural and biophysical characteristics of native catalytic residues. Moreover, using experimental datasets, we show that SCREEN’s predictions can be used to distinguish residues with a high mutation tolerance from those likely to cause functional loss when mutated, indicating that this tool might be used to infer disease-associated mutations. SCREEN is publicly available at https://github.com/BioColLab/SCREEN and https://ngdc.cncb.ac.cn/biocode/tool/7580.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
© The Author(s) 2024. Published by Oxford University Press and Science Press on behalf of the Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation and Genetics Society of China. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.