Deep diversification of an AAV capsid protein by machine learning

• Published in: Nature biotechnology Vol. 39; no. 6; pp. 691 - 696
• Main Authors: Bryant, Drew H., Bashir, Ali, Sinai, Sam, Jain, Nina K., Ogden, Pierce J., Riley, Patrick F., Church, George M., Colwell, Lucy J., Kelsic, Eric D.
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.06.2021; Nature Publishing Group
• Subjects: 631/114; 631/114/1305; 631/114/2397; 631/114/469; Agriculture; Amino acid sequence; Amino acids; Artificial neural networks; Bioinformatics; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Biomedicine; Biotechnology; Capsid protein; Capsids; Deep learning; Learning algorithms; Letter; Life Sciences; Machine learning; Methods; Mutation; Neural networks; Packaging; Physiological aspects; Protein engineering; Protein families; Proteins; Viral proteins; United States
• ISSN: 1087-0156; 1546-1696
• DOI: 10.1038/s41587-020-00793-4

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. Viable AAV capsids are designed with a machine learning approach.