ArticleNature biotechnology2021
Deep diversification of an AAV capsid protein by machine learning.
Article in Nature biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 161 papers.
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Who cites it
161 citing papers in PubMed, 306 citations in OpenAlex.
- AI-engineered AAV capsid enables intravitreal delivery for the treatment of diverse retinal degenerations.Molecular therapy. Advances · 2026Article
- Recent advancements in improving cross-species applicability of bioengineered AAV capsids.Gene therapy · 2026Review
- Epigenetic editing approaches maturity: AI-driven precision design, delivery innovation, and the road to clinical translation.Clinical epigenetics · 2026Review
- Artificial intelligence in biologic drug discovery: A review of methodological evolution and therapeutic applications.Acta pharmaceutica Sinica. B · 2026Review
- Inference of fitness landscapes with heterogeneous patterns of epistasis across sites.bioRxiv : the preprint server for biology · 2026Article
- Machine learning-driven optimization of specific, compact, and efficient base editors via single-round diversification.Nucleic acids research · 2026Article
- Engineering challenges and translational opportunities in emerging gene delivery platforms.Nature biomedical engineering · 2026Review
- Scalable and cost-efficient custom gene library assembly from oligopools.Science advances · 2026Article
- Rapid directed evolution guided by protein language models and epistatic interactions.Science (New York, N.Y.) · 2026Article
- Local gene editing of fibroblasts in tumors reveals a new cancer-associated fibroblast state.The Journal of experimental medicine · 2026Article
- Tissue-specific gene delivery approaches.Bioengineering & translational medicine · 2026Review
- Recent advances in lipid and biomimetic nanocarriers for nucleic acid delivery in glioblastoma.Discover oncology · 2026Review
- Descent from a common ancestor restricts exploration of protein sequence space.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Exploring the limits of pre-trained embeddings in machine-guided protein design: a case study on predicting AAV vector viability.Scientific reports · 2026Article
- Review
- Inference and Visualization of Complex Genotype-Phenotype Maps.Molecular biology and evolution · 2026Article
- Advances and challenges of precision epigenetic therapy in treating genomic imprinting diseases.Translational pediatrics · 2026Review
- AAV Capsid Modification and Its Influence on Viral Protein Stoichiometry and Packaging Fitness: Current Understandings and Future Direction.Molecular biotechnology · 2026Review
- From bench to bedside: the promise and roadblocks of extracellular vesicle therapeutics.Theranostics · 2026Review
- Steering Protein Generative Models at Test-Time for Guided AAV2 Capsid Design.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
101 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Modern experimental technologies can assay large numbers of biological sequences, but engineered protein libraries rarely exceed the sequence diversity of natural protein families. Machine learning (ML) models trained directly on experimental data without biophysical modeling provide one route to accessing the full potential diversity of engineered proteins. Here we apply deep learning to design highly diverse adeno-associated virus 2 (AAV2) capsid protein variants that remain viable for packaging of a DNA payload. Focusing on a 28-amino acid segment, we generated 201,426 variants of the AAV2 wild-type (WT) sequence yielding 110,689 viable engineered capsids, 57,348 of which surpass the average diversity of natural AAV serotype sequences, with 12-29 mutations across this region. Even when trained on limited data, deep neural network models accurately predict capsid viability across diverse variants. This approach unlocks vast areas of functional but previously unreachable sequence space, with many potential applications for the generation of improved viral vectors and protein therapeutics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.