{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T19:04:51Z","timestamp":1781118291626,"version":"3.54.1"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"W1","license":[{"start":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T00:00:00Z","timestamp":1686096000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Australian Government Research Training Program Scholarship"},{"DOI":"10.13039\/501100000925","name":"National Health and Medical Research Council","doi-asserted-by":"publisher","award":["GNT1174405"],"award-info":[{"award-number":["GNT1174405"]}],"id":[{"id":"10.13039\/501100000925","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,7,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Understanding the effects of mutations on protein stability is crucial for variant interpretation and prioritisation, protein engineering, and biotechnology. Despite significant efforts, community assessments of predictive tools have highlighted ongoing limitations, including computational time, low predictive power, and biased predictions towards destabilising mutations. To fill this gap, we developed DDMut, a fast and accurate siamese network to predict changes in Gibbs Free Energy upon single and multiple point mutations, leveraging both forward and hypothetical reverse mutations to account for model anti-symmetry. Deep learning models were built by integrating graph-based representations of the localised 3D environment, with convolutional layers and transformer encoders. This combination better captured the distance patterns between atoms by extracting both short-range and long-range interactions. DDMut achieved Pearson's correlations of up to 0.70 (RMSE: 1.37\u2009kcal\/mol) on single point mutations, and 0.70 (RMSE: 1.84\u2009kcal\/mol) on double\/triple mutants, outperforming most available methods across non-redundant blind test sets. Importantly, DDMut was highly scalable and demonstrated anti-symmetric performance on both destabilising and stabilising mutations. We believe DDMut will be a useful platform to better understand the functional consequences of mutations, and guide rational protein engineering. DDMut is freely available as a web server and API at https:\/\/biosig.lab.uq.edu.au\/ddmut.<\/jats:p>","DOI":"10.1093\/nar\/gkad472","type":"journal-article","created":{"date-parts":[[2023,6,8]],"date-time":"2023-06-08T00:35:37Z","timestamp":1686184537000},"page":"W122-W128","source":"Crossref","is-referenced-by-count":244,"title":["DDMut: predicting effects of mutations on protein stability using deep learning"],"prefix":"10.1093","volume":"51","author":[{"given":"Yunzhuo","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Chemistry and Molecular Biosciences, The University of Queensland , Brisbane , Australia"},{"name":"Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute , Melbourne , Victoria , Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qisheng","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Chemistry and Molecular Biosciences, The University of Queensland , Brisbane , Australia"},{"name":"Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute , Melbourne , Victoria , Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3004-2119","authenticated-orcid":false,"given":"Douglas E V","family":"Pires","sequence":"additional","affiliation":[{"name":"School of Computing and Information Systems, University of Melbourne , Melbourne , Victoria , Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlos\u00a0H M","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"School of Chemistry and Molecular Biosciences, The University of Queensland , Brisbane , Australia"},{"name":"Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute , Melbourne , Victoria , Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2948-2413","authenticated-orcid":false,"given":"David\u00a0B","family":"Ascher","sequence":"additional","affiliation":[{"name":"School of Chemistry and Molecular Biosciences, The University of Queensland , Brisbane , Australia"},{"name":"Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute , Melbourne , Victoria , Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,6,7]]},"reference":[{"key":"2023070504564746000_B1","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.ygeno.2007.12.007","article-title":"A protein-protein interaction network of transcription factors acting during liver cell proliferation","volume":"91","author":"Gao","year":"2008","journal-title":"Genomics"},{"key":"2023070504564746000_B2","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1385\/MB:29:1:57","article-title":"Protein-protein interactions in the regulation of the extracellular signal-regulated kinase","volume":"29","author":"Chuderland","year":"2005","journal-title":"Mol. 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