Proteome-wide Prediction of the Functional Impact of Missense Variants with ProteoCast

Proteome-wide Prediction of the Functional Impact of Missense Variants with ProteoCast | 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 Biological Sciences - Article Proteome-wide Prediction of the Functional Impact of Missense Variants with ProteoCast Michael Rera, Marina Abakarova, Maria-Inès Freiberger, Arnaud Lierhmann, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6128805/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Apr, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Background: Dissecting the functional impact of genetic mutations is essential to advancing our understanding of genotype-phenotype relationships and identifying new therapeutic targets. Despite the progress in sequencing and CRISPR technologies, proteome-wide mutation effect prediction remains challenging. Here, we introduce ProteoCast, a scalable and interpretable computational method for proteome-wide classification of genetic variants and functional protein site identification. It relies solely on evolutionary information, leveraging protein sequence data across organisms. Results: Using ProteoCast, we generated mutational landscapes for 22,169 Drosophila melanogaster protein isoforms, categorising over 293 million amino acid substitutions as functionally neutral, uncertain, or impactful. We validated our predictions with over 380 thousand natural polymorphisms observed in the Drosophila Genetic Reference Panel (DGRP) and Drosophila Evolution over Space and Time (DEST) datasets and with FlyBase's developmentally lethal mutations. About 86% of known lethal mutations were classified as impactful or uncertain, versus only 13% and 18% of DGRP and DEST mutations. Moreover, we performed ProteoCastguided genome editing experiments, providing a proof-of-concept of the validity of this strategy. Beyond variant effect prediction, ProteoCast detected evolutionary conservation signals in about one-third of 40.5K annotated post-translational modification sites and 83% of ~90 known short linear motifs. These results support its usefulness for uncovering interaction and regulatory sites in unstructured protein regions. Conclusions: Our results demonstrate ProteoCast applicability for model organisms, contributing to basic genetic research and translational studies. This work provides a publicly available dataset, userfriendly interactive web services, and a locally deployable pipeline tool for further research into gene function and mutation effects in any organism. Biological sciences/Computational biology and bioinformatics/Protein function predictions Biological sciences/Computational biology and bioinformatics/Proteome informatics Protein mutation variant effect prediction developmental lethality protein function Drosophila melanogaster functional sites discovery Full Text Additional Declarations There is NO Competing Interest. The authors have no competing interests to declare. Supplementary Files ProteomewidepredictionSupMat.pdf Supplementary figures and tables ProteomewidepredictionExtendedData.pdf Extended data Cite Share Download PDF Status: Published Journal Publication published 27 Apr, 2026 Read the published version in Nature Communications → 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-6128805","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Biological Sciences - Article","associatedPublications":[],"authors":[{"id":438608526,"identity":"292bcfe9-450f-4e6d-9fbe-7fdbe8239cd5","order_by":0,"name":"Michael 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