{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T20:13:02Z","timestamp":1757621582688,"version":"3.44.0"},"reference-count":10,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T00:00:00Z","timestamp":1757289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioinform."],"abstract":"<jats:p>This dataset presents a structure-enriched resource of theoretical and empirical SARS-CoV-2 spike receptor-binding domain (RBD) variants, developed under the STAYAHEAD project for pandemic preparedness. It integrates large-scale <jats:italic>in silico<\/jats:italic> structure predictions with empirical biophysical measurements. The dataset includes 3,705 single-point Wuhan-Hu-1 RBD variants and 100 higher-order Omicron BA.1\/BA.2 variants, annotated with AlphaFold2 and ESMFold metrics and Bio2Byte sequence-based predictors. Structural descriptors\u2014RMSD, TM-score, plDDT, solvent accessibility, hydrophobicity, aggregation propensity\u2014are linked to ACE2 binding and expression data from deep mutational scanning. Provided as a FAIR<jats:sup>2<\/jats:sup> Data Package, it supports structure\u2013function analysis, variant modeling, and responsible reuse in virology, structural biology, and computational protein science. This collaboration was co-funded by the PPP Allowance from Health \u223c Holland, Top Sector Life Sciences and Health, to stimulate public\u2013private partnerships.<\/jats:p>","DOI":"10.3389\/fbinf.2025.1634111","type":"journal-article","created":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T14:39:11Z","timestamp":1757342351000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Structure-based prediction of SARS-CoV-2 variant properties using machine learning on mutational neighborhoods"],"prefix":"10.3389","volume":"5","author":[{"given":"Max","family":"van den Boom","sequence":"first","affiliation":[]},{"given":"Erik","family":"Schultes","sequence":"additional","affiliation":[]},{"given":"Thomas","family":"Hankemeier","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,9,8]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1093\/bioinformatics\/btt024","article-title":"DynaMine: predicting protein dynamics from sequence","volume":"29","author":"Cilia","year":"2013","journal-title":"Bioinform."},{"key":"B2","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1038\/s41579-021-00573-0","article-title":"SARS-CoV-2 variants, spike mutations and immune escape","volume":"19","author":"Harvey","year":"2021","journal-title":"Nat. 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Data"},{"key":"B10","unstructured":"Tracking SARS-CoV-2 variants\n          \n          \n          2023"}],"container-title":["Frontiers in Bioinformatics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fbinf.2025.1634111\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T14:39:12Z","timestamp":1757342352000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fbinf.2025.1634111\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,8]]},"references-count":10,"alternative-id":["10.3389\/fbinf.2025.1634111"],"URL":"https:\/\/doi.org\/10.3389\/fbinf.2025.1634111","relation":{},"ISSN":["2673-7647"],"issn-type":[{"type":"electronic","value":"2673-7647"}],"subject":[],"published":{"date-parts":[[2025,9,8]]},"article-number":"1634111"}}