{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T06:48:14Z","timestamp":1778568494662,"version":"3.51.4"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T00:00:00Z","timestamp":1693440000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The accurate prediction of the effect of amino acid mutations for protein\u2013protein interactions (PPI $\\Delta \\Delta G$) is a crucial task in protein engineering, as it provides insight into the relevant biological processes underpinning protein binding and provides a basis for further drug discovery. In this study, we propose MpbPPI, a novel multi-task pre-training-based geometric equivariance-preserving framework to predict PPI \u00a0$\\Delta \\Delta G$. Pre-training on a strictly screened pre-training dataset is employed to address the scarcity of protein\u2013protein complex structures annotated with PPI $\\Delta \\Delta G$ values. MpbPPI employs a multi-task pre-training technique, forcing the framework to learn comprehensive backbone and side chain geometric regulations of protein\u2013protein complexes at different scales. After pre-training, MpbPPI can generate high-quality representations capturing the effective geometric characteristics of labeled protein\u2013protein complexes for downstream $\\Delta \\Delta G$ predictions. MpbPPI serves as a scalable framework supporting different sources of mutant-type (MT) protein\u2013protein complexes for flexible application. Experimental results on four benchmark datasets demonstrate that MpbPPI is a state-of-the-art framework for PPI $\\Delta \\Delta G$ predictions. The data and source code are available at https:\/\/github.com\/arantir123\/MpbPPI.<\/jats:p>","DOI":"10.1093\/bib\/bbad310","type":"journal-article","created":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T19:02:38Z","timestamp":1693508558000},"source":"Crossref","is-referenced-by-count":21,"title":["MpbPPI: a multi-task pre-training-based equivariant approach for the prediction of the effect of amino acid mutations on protein\u2013protein interactions"],"prefix":"10.1093","volume":"24","author":[{"given":"Yang","family":"Yue","sequence":"first","affiliation":[{"name":"School of Computer Science from the University of Birmingham , UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shu","family":"Li","sequence":"additional","affiliation":[{"name":"Centre for Artificial Intelligence Driven Drug Discovery at Macao Polytechnic 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