{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T15:59:43Z","timestamp":1768406383092,"version":"3.49.0"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T00:00:00Z","timestamp":1715126400000},"content-version":"vor","delay-in-days":7,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of Biotechnology, Government of India","award":["BT\/PR40309\/BTIS\/137\/57\/2023"],"award-info":[{"award-number":["BT\/PR40309\/BTIS\/137\/57\/2023"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Protein\u2013protein interactions underpin many cellular processes and their disruption due to mutations can lead to diseases. With the evolution of protein structure prediction methods like AlphaFold2 and the availability of extensive experimental affinity data, there is a pressing need for updated computational tools that can efficiently predict changes in binding affinity caused by mutations in protein\u2013protein complexes.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We developed a deep ensemble model that leverages protein sequences, predicted structure-based features, and protein functional classes to accurately predict the change in binding affinity due to mutations. The model achieved a correlation of 0.97 and a mean absolute error (MAE) of 0.35\u2009kcal\/mol on the training dataset, and maintained robust performance on the test set with a correlation of 0.72 and a MAE of 0.83\u2009kcal\/mol. Further validation using Leave-One-Out Complex (LOOC) cross-validation exhibited a correlation of 0.83 and a MAE of 0.51\u2009kcal\/mol, indicating consistent performance.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/web.iitm.ac.in\/bioinfo2\/DeepPPAPredMut\/index.html.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae309","type":"journal-article","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T18:21:48Z","timestamp":1715192508000},"source":"Crossref","is-referenced-by-count":3,"title":["DeepPPAPredMut: deep ensemble method for predicting the binding affinity change in protein\u2013protein complexes upon mutation"],"prefix":"10.1093","volume":"40","author":[{"given":"Rahul","family":"Nikam","sequence":"first","affiliation":[{"name":"Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras , Chennai 600036, India"}]},{"given":"Sherlyn","family":"Jemimah","sequence":"additional","affiliation":[{"name":"Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras , Chennai 600036, India"},{"name":"Department of Biomedical Engineering, Khalifa University , P.O. Box: 127788 , Abu Dhabi, United Arab Emirates"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1776-4096","authenticated-orcid":false,"given":"M Michael","family":"Gromiha","sequence":"additional","affiliation":[{"name":"Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras , Chennai 600036, India"},{"name":"Department of Computer Science, Tokyo Tech World Research Hub Initiative (WRHI) , Institute of Innovative Research, Tokyo Institute of Technology , 4259 Nagatsutacho, Midori-ku, Yokohama, Kanagawa 226-8501, Japan"}]}],"member":"286","published-online":{"date-parts":[[2024,5,8]]},"reference":[{"key":"2024052304535732300_btae309-B1","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1126\/science.abj8754","article-title":"Accurate prediction of protein structures and interactions using a three-track neural 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