Predictive power of deleterious single amino acid changes to infer on AAV2 and AAV2-13 capsids fitness

Abstract

Adeno-associated virus (AAV) is the most widely used vector for gene transfer. A major limitation of capsid engineering is the incomplete understanding of the consequences of multiple amino acid variations on AAV capsid stability resulting in high frequency of non-viable capsids. In this context, the study of natural AAV variants can provide valuable insights into capsid regions that exhibit greater tolerance to mutations. Here, the characterization of AAV2 variants and the analysis of two public capsid libraries highlighted common features associated with deleterious mutations, suggesting that the impact of mutations on capsid viability is strictly dependent on their 3D location within the capsid structure. We developed a novel prediction method to infer the fitness of AAV2 variants containing multiple amino acid variations with 98% sensitivity, 98% accuracy, and 95% specificity. This novel approach might streamline the development of AAV vector libraries enriched in viable capsids, thus accelerating the identification of therapeutic candidates among engineered capsids.