[2603.19473] Reinforcement-guided generative protein language models enable de novo design of highly diverse AAV capsids

Quantitative Biology > Biomolecules arXiv:2603.19473 (q-bio) [Submitted on 19 Mar 2026] Title:Reinforcement-guided generative protein language models enable de novo design of highly diverse AAV capsids Authors:Lucas Ferraz, Ana F. Rodrigues, Pedro Giesteira Cotovio, Mafalda Ventura, Gabriela Silva, Ana Sofia Coroadinha, Miguel Machuqueiro, Catia Pesquita View a PDF of the paper titled Reinforcement-guided generative protein language models enable de novo design of highly diverse AAV capsids, by Lucas Ferraz and 7 other authors View PDF Abstract:Adeno-associated viral (AAV) vectors are widely used delivery platforms in gene therapy, and the design of improved capsids is key to expanding their therapeutic potential. A central challenge in AAV bioengineering, as in protein design more broadly, is the vast sequence design space relative to the scale of feasible experimental screening. Machine-guided generative approaches provide a powerful means of navigating this landscape and proposing novel protein sequences that satisfy functional constraints. Here, we develop a generative design framework based on protein language models and reinforcement learning to generate highly novel yet functionally plausible AAV capsids. A pretrained model was fine-tuned on experimentally validated capsid sequences to learn patterns associated with viability. Reinforcement learning was then used to guide sequence generation, with a reward function that jointly promoted predicted viability and seque...