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

• URL: http://arxiv.org/abs/2603.19473v1
• Date: Thu, 19 Mar 2026 21:11:17 GMT
• Title: Reinforcement-guided generative protein language models enable de novo design of highly diverse AAV capsids
• Abstract summary: Adeno-associated viral (AAV) vectors are widely used in gene therapy.<n>A central challenge in AAV bioengineering is the vast sequence design space.<n>We develop a generative design framework based on protein language models and reinforcement learning.
• Score: 0.3372751145910977
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 sequence novelty, thereby enabling exploration beyond regions represented in the training data. Comparative analyses showed that fine-tuning alone produces sequences with high predicted viability but remains biased toward the training distribution, whereas reinforcement learining-guided generation reaches more distant regions of sequence space while maintaining high predicted viability. Finally, we propose a candidate selection strategy that integrates predicted viability, sequence novelty, and biophysical properties to prioritize variants for downstream evaluation. This work establishes a framework for the generative exploration of protein sequence space and advances the application of generative protein language models to AAV bioengineering.

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