{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Eid FE"],"funding":["U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)","Apertura Gene Therapy","NIMH NIH HHS","U.S. Department of Health & Human Services | NIH | NIH Office of the Director","U.S. Department of Health & Human Services | NIH | National Institute of Mental Health","U.S. Department of Health & Human Services | NIH | NIH Office of the Director (OD)","Broad Institute Shark Tank Award","Broad Ignite Award","Broad Institute | Stanley Center for Psychiatric Research, Broad Institute","NIDDK NIH HHS","NINDS NIH HHS","Novo Nordisk Fonden","NIH HHS"],"pagination":["6602"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11297966"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["15(1)"],"pubmed_abstract":["Broadening gene therapy applications requires manufacturable vectors that efficiently transduce target cells in humans and preclinical models. Conventional selections of adeno-associated virus (AAV) capsid libraries are inefficient at searching the vast sequence space for the small fraction of vectors possessing multiple traits essential for clinical translation. Here, we present Fit4Function, a generalizable machine learning (ML) approach for systematically engineering multi-trait AAV capsids. By leveraging a capsid library that uniformly samples the manufacturable sequence space, reproducible screening data are generated to train accurate sequence-to-function models. Combining six models, we designed a multi-trait (liver-targeted, manufacturable) capsid library and validated 88% of libra"],"journal":["Nature communications"],"pubmed_title":["Systematic multi-trait AAV capsid engineering for efficient gene delivery."],"pmcid":["PMC11297966"],"funding_grant_id":["U01 MH121499","U42 OD027094","UG3MH120096","UG3 NS111689","R01 DK078616","UG3 MH120096","R01 MH109903","UM1 DK078616","P51-OD011107","UG3NS111689","U01 DK078616","P51 OD011107","NNF21SA0072102"],"pubmed_authors":["Deverman BE","Zhao B","Lage K","Zheng Q","Tarantal AF","Eid FE","Huang Q","Chan YA","Keyes C","Johnston J","Pacouret S","Tobey IG","Chen AT","Chan KY","Brauer PP","Powell M"],"additional_accession":[]},"is_claimable":false,"name":"Systematic multi-trait AAV capsid engineering for efficient gene delivery.","description":"Broadening gene therapy applications requires manufacturable vectors that efficiently transduce target cells in humans and preclinical models. Conventional selections of adeno-associated virus (AAV) capsid libraries are inefficient at searching the vast sequence space for the small fraction of vectors possessing multiple traits essential for clinical translation. Here, we present Fit4Function, a generalizable machine learning (ML) approach for systematically engineering multi-trait AAV capsids. By leveraging a capsid library that uniformly samples the manufacturable sequence space, reproducible screening data are generated to train accurate sequence-to-function models. Combining six models, we designed a multi-trait (liver-targeted, manufacturable) capsid library and validated 88% of libra","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Aug","modification":"2026-06-01T22:18:22.086Z","creation":"2025-02-19T04:39:55.156Z"},"accession":"S-EPMC11297966","cross_references":{"pubmed":["39097583"],"doi":["10.1038/s41467-024-50555-y"]}}