PRJNA673640
Deep diversification of AAV capsid proteins by machine learning
We apply deep learning to design novel adeno-associated virus (AAV) capsid proteins, a challenging target of great utility for gene therapy. Focusing on a 28-amino acid segment spanning buried and exposed regions, we generated 201,426 highly diverse variants of the AAV2 wildtype (WT) sequence, yielding 110,689 viable synthetic capsids, 57,348 of which surpass the average diversity of natural AAV serotype sequences with 12-29 mutations across this region.