Bridging Computational Predictions and Empirical Data: Insights into SARS-CoV-2 Mutational Impacts and Evolution

Bridging Computational Predictions and Empirical Data: Insights into SARS-CoV-2 Mutational Impacts and Evolution | 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 Research Article Bridging Computational Predictions and Empirical Data: Insights into SARS-CoV-2 Mutational Impacts and Evolution Muhammad Arslan Shaukat, Thanh Thi Nguyen, Edbert B. Hsu, Samuel Yang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4019587/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The emergence of SARS-CoV-2 has unleashed a global health crisis, demanding advanced research into its genomic mutations and their consequences. Our study combines computational models and empirical validation to predict the effects of these mutations, aiming to understand their impact on the virus's behaviour, including its transmissibility and immune escape mechanisms. Utilising advanced prediction tools, we analysed mutations across the virus's genome, focusing on changes to both structural and non-structural proteins. This approach identified key mutations likely to influence protein functionality and the virus's evolution. Our findings, integrating computational predictions with real-world data, offer insights into SARS-CoV-2's evolutionary path and its implications for developing vaccines and therapies. We highlight the necessity of ongoing genomic surveillance and the combined use of computational and empirical methods to stay ahead of viral mutations. This study not only deepens our understanding of SARS-CoV-2 but also lays groundwork for future research on viral evolution and pandemic response strategies, emphasizing our approach's potential to inform public health decisions and research priorities. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-4019587","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276787932,"identity":"5325de98-7339-4e36-98d3-47ebfd131ce3","order_by":0,"name":"Muhammad Arslan Shaukat","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYFACHiA+wMDAz5DAwNhgABJJIE6LhGQDyVoMDoC0MBChRbf/7MHPPGfs6oyPJx/8OKPgDgM/e44Bw8823FrMbuQlS/PcSJYwO/MsWXKDwTMGyZ43Boy9eLXwGEjzfGCWMLuRYyD5wOAwgwGQwcCLT8v5M8a/eT7USxjPyP/8E6TFHqiF8S8+LQdyzIAOOyxhIJHDBnQY0BaJHANmvLbcyDGznHPmuOSMM8/MLGcYHOaROPOs4LDMOfwOu/HmWDU/f3vy45s9fw7LARkbH74pw60FA4CiCZQaRsEoGAWjYBRQAgB61FZh2pEfKwAAAABJRU5ErkJggg==","orcid":"","institution":"Deakin University","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"Arslan","lastName":"Shaukat","suffix":""},{"id":276787933,"identity":"71ded0b3-9dde-425d-b3d1-ff35027ecca8","order_by":1,"name":"Thanh Thi Nguyen","email":"","orcid":"","institution":"Monash University","correspondingAuthor":false,"prefix":"","firstName":"Thanh","middleName":"Thi","lastName":"Nguyen","suffix":""},{"id":276787934,"identity":"7349f42a-d546-4a1b-afd2-b85fbe3c5bd9","order_by":2,"name":"Edbert B. 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