{"doi":"10.1126/sciadv.adj3786","title":"Optimal trade-off control in machine learning–based library design, with application to adeno-associated virus (AAV) for gene therapy","abstract":"Adeno-associated viruses (AAVs) hold tremendous promise as delivery vectors for gene therapies. AAVs have been successfully engineered-for instance, for more efficient and/or cell-specific delivery to numerous tissues-by creating large, diverse starting libraries and selecting for desired properties. However, these starting libraries often contain a high proportion of variants unable to assemble or package their genomes, a prerequisite for any gene delivery goal. Here, we present and showcase a machine learning (ML) method for designing AAV peptide insertion libraries that achieve fivefold higher packaging fitness than the standard NNK library with negligible reduction in diversity. To demonstrate our ML-designed library's utility for downstream engineering goals, we show that it yields approximately 10-fold more successful variants than the NNK library after selection for infection of human brain tissue, leading to a promising glial-specific variant. Moreover, our design approach can be applied to other types of libraries for AAV and beyond.","journal":"Science Advances","year":2024,"id":418592,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":48,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9534,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":565822,"name":"David H. Brookes","orcid":"0000-0002-5049-5799","position":1,"is_corresponding":false},{"id":805781,"name":"Akosua Busia","orcid":"0000-0002-2225-3389","position":2,"is_corresponding":false},{"id":806249,"name":"A. Carneiro","orcid":null,"position":3,"is_corresponding":false},{"id":805782,"name":"Clara Fannjiang","orcid":"0000-0002-0060-2082","position":4,"is_corresponding":false},{"id":558603,"name":"Galina Popova","orcid":"0000-0001-8249-219X","position":5,"is_corresponding":false},{"id":307691,"name":"David Shin","orcid":"0000-0001-7029-089X","position":6,"is_corresponding":false},{"id":636929,"name":"Kevin C. Donohue","orcid":"0000-0002-4237-5957","position":7,"is_corresponding":false},{"id":242614,"name":"Li Lin","orcid":"0009-0005-6010-4586","position":8,"is_corresponding":false},{"id":400493,"name":"Zachary M. Miller","orcid":"0000-0002-6737-7625","position":9,"is_corresponding":false},{"id":522244,"name":"Evan R. Williams","orcid":"0000-0002-1733-3018","position":10,"is_corresponding":false},{"id":233106,"name":"Edward F. Chang","orcid":"0000-0003-2480-4700","position":11,"is_corresponding":false},{"id":21360,"name":"Tomasz J. Nowakowski","orcid":"0000-0003-2345-4964","position":12,"is_corresponding":false},{"id":565825,"name":"Jennifer Listgarten","orcid":"0000-0002-6600-1431","position":13,"is_corresponding":false},{"id":235840,"name":"David V. Schaffer","orcid":"0000-0002-9625-0121","position":14,"is_corresponding":false},{"id":492105,"name":"Danqing Zhu","orcid":"0000-0003-3916-3192","position":0,"is_corresponding":true}],"reference_count":63,"raw_metadata":null,"created_at":"2026-07-19T01:57:09.067930Z","pmid":"38266077","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}