强化引导的生成性蛋白质语言模型实现高多样性的AAV衣壳从头设计
Reinforcement-guided generative protein language models enable de novo design of highly diverse AAV capsids
AI总结：
本文提出基于蛋白质语言模型和强化学习的生成设计框架，用于从头设计高多样性的AAV衣壳，通过强化学习引导序列生成，提升序列空间探索能力。
摘要：
腺相关病毒（AAV）载体广泛用于基因治疗，改进其衣壳设计对扩大治疗潜力至关重要。AAV生物工程中的核心挑战在于序列设计空间广阔，而可行的实验筛选规模有限。机器引导的生成方法为导航此空间并提出满足功能约束的新型蛋白质序列提供了强大手段。本文开发了一种基于蛋白质语言模型和强化学习的生成设计框架，用于生成新颖且功能上合理的AAV衣壳。预训练模型在实验验证的衣壳序列上进行微调以学习与存活相关的模式。随后使用强化学习引导序列生成，奖励函数联合促进预测存活性和序列新颖性，从而探索训练数据未覆盖的区域。比较分析显示，仅微调生成的序列具有高预测存活性但偏向训练分布，而强化学习引导的生成能探索更远的序列空间并保持高预测存活性。最后，提出一种候选选择策略，整合预测存活性、序列新颖性和生物物理性质以优先选择下游评估的变体。本工作建立了一种蛋白质序列空间生成探索的框架，并推进了生成性蛋白质语言模型在AAV生物工程中的应用。
英文摘要：
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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