GL-E2EATP: improving protein-ATP binding residue prediction using global and local embedding of protein language model

GL-E2EATP: improving protein-ATP binding residue prediction using global and local embedding of protein language model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article GL-E2EATP: improving protein-ATP binding residue prediction using global and local embedding of protein language model Bing Rao, Jie Bai, Maha A. Thafar, Somayah Albaradei, Kamran Arshad, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9088034/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 17 You are reading this latest preprint version Abstract Identification of ATP-binding residues in proteins is of paramount importance for elucidating the mechanisms underlying protein functions and advancing drug discovery efforts. Several computational approaches have been developed for predicting ATP binding sites, yet their predictive performance remains suboptimal, primarily due to inadequate features descriptors and learning models. In this study, we developed a novel end-to-end deep learning (DL) model called GL-E2EATP, for predicting ATP-binding residues with improved accuracy. The proposed model use self-supervise learning strategy by extracting both global and local embeddings from protein language model generated by protein sequences. Specifically, we leverage a pre-trained DL-based biological language model, ESM2, to autonomously generate biologically relevant features. Building upon ESM2, two different neural network modules, i.e., convolutional layers and multi-head attention layers, are employed to separately extract global information for whole protein sequences and local information for the potential ATP-binding residues. Empirical evaluations conducted on two independent test datasets reveal that GL-E2EATP outperforms existing ATP-based prediction methods, achieving superior Matthews correlation coefficient (MCC), area under the ROC curve (AUC), and area under the Precision-Recall curve (AUCPR) metrics. Comprehensive analyses anticipate that GL-E2EATP will serve an efficient solution for characterizing large-scale prediction of ATP-binding sites from protein sequences. The standalone package for GL-E2EATP is downloadable at https://github.com/Robin8990/gl-e2eatp for academic use. Biological sciences/Biochemistry Biological sciences/Computational biology and bioinformatics Protein-ATP binding residue prediction Bioinformatics Protein language model Multi-head attention Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 08 May, 2026 Reviews received at journal 18 Apr, 2026 Reviewers agreed at journal 18 Apr, 2026 Reviews received at journal 18 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviews received at journal 13 Apr, 2026 Reviews received at journal 05 Apr, 2026 Reviewers agreed at journal 26 Mar, 2026 Reviewers agreed at journal 24 Mar, 2026 Reviewers agreed at journal 24 Mar, 2026 Reviewers agreed at journal 24 Mar, 2026 Reviewers invited by journal 24 Mar, 2026 Editor invited by journal 17 Mar, 2026 Editor assigned by journal 12 Mar, 2026 Submission checks completed at journal 12 Mar, 2026 First submitted to journal 10 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9088034","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":612065214,"identity":"1235a3c3-e6a5-432f-9983-f9cf23794358","order_by":0,"name":"Bing Rao","email":"","orcid":"","institution":"Hangzhou City University","correspondingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Rao","suffix":""},{"id":612065215,"identity":"565a0431-aa4a-47b1-ae3f-8824c7c12a54","order_by":1,"name":"Jie Bai","email":"","orcid":"","institution":"Hangzhou City University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Bai","suffix":""},{"id":612065216,"identity":"6d853cbd-1f40-4b49-b17d-02bc681c9cfa","order_by":2,"name":"Maha A. 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Several computational approaches have been developed for predicting ATP binding sites, yet their predictive performance remains suboptimal, primarily due to inadequate features descriptors and learning models. In this study, we developed a novel end-to-end deep learning (DL) model called GL-E2EATP, for predicting ATP-binding residues with improved accuracy. The proposed model use self-supervise learning strategy by extracting both global and local embeddings from protein language model generated by protein sequences. Specifically, we leverage a pre-trained DL-based biological language model, ESM2, to autonomously generate biologically relevant features. Building upon ESM2, two different neural network modules, i.e., convolutional layers and multi-head attention layers, are employed to separately extract global information for whole protein sequences and local information for the potential ATP-binding residues. Empirical evaluations conducted on two independent test datasets reveal that GL-E2EATP outperforms existing ATP-based prediction methods, achieving superior Matthews correlation coefficient (MCC), area under the ROC curve (AUC), and area under the Precision-Recall curve (AUCPR) metrics. Comprehensive analyses anticipate that GL-E2EATP will serve an efficient solution for characterizing large-scale prediction of ATP-binding sites from protein sequences. 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