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Optimal trade-off control in machine learning-based library design, with application to adeno-associated virus (AAV) for gene therapy

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Sci Adv. 2024 Jan 26;10(4):eadj3786. doi: 10.1126/sciadv.adj3786. Epub 2024 Jan 24.

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.

PMID:38266077 | DOI:10.1126/sciadv.adj3786

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