SCREEN: A Graph-based Contrastive Learning Tool to Infer Catalytic Residues and Assess Enzyme Mutations

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dc.contributor.authorPan, T.
dc.contributor.authorBi, Y.
dc.contributor.authorWang, X.
dc.contributor.authorZhang, Y.
dc.contributor.authorWebb, G.
dc.contributor.authorGasser, R.B.
dc.contributor.authorKurgan, L.
dc.contributor.authorSong, J.
dc.contributor.editorZhang, Z.
dc.date.issued2024
dc.description.abstractThe 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.
dc.description.statementofresponsibilityTong Pan, Yue Bi, Xiaoyu Wang, Ying Zhang, Geoffrey I Webb, Robin B Gasser, Lukasz Kurgan, Jiangning Song
dc.identifier.citationGenomics, Proteomics & Bioinformatics, 2024; 22(6):qzae094-1-qzae094-14
dc.identifier.doi10.1093/gpbjnl/qzae094
dc.identifier.issn1672-0229
dc.identifier.issn2210-3244
dc.identifier.orcidPan, T. [0009-0003-6676-5727]
dc.identifier.urihttps://hdl.handle.net/2440/148433
dc.language.isoen
dc.publisherOxford University Press
dc.relation.granthttp://purl.org/au-research/grants/arc/LP220200614
dc.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.
dc.source.urihttps://doi.org/10.1093/gpbjnl/qzae094
dc.subjectcatalytic residue; enzyme structure; evolutionary conservation; graph neural network; contrastive learning.
dc.subject.meshHumans
dc.subject.meshEnzymes
dc.subject.meshComputational Biology
dc.subject.meshCatalytic Domain
dc.subject.meshMutation
dc.subject.meshSoftware
dc.subject.meshMachine Learning
dc.subject.meshNeural Networks, Computer
dc.titleSCREEN: A Graph-based Contrastive Learning Tool to Infer Catalytic Residues and Assess Enzyme Mutations
dc.typeJournal article
pubs.publication-statusPublished

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