用蒙特卡洛采样提升蛋白质设计多样性，效果远超传统方法。
Relaxed Sequence Sampling for Diverse Protein Design
- 基于MCMC框架，在连续逻辑空间中结合梯度与语言模型进行采样。
- 生成结构可设计性提升5倍，结构多样性达2-3倍，计算成本不变。
- 适合需要高多样性蛋白质设计的研究者，尤其适用于虚拟筛选场景。
利用AlphaFold2等结构预测模型进行蛋白质设计已取得显著成果，但现有方法如松弛序列优化（RSO）依赖单路径梯度下降，忽略序列空间约束，限制了设计多样性和可设计性。本文提出松弛序列采样（RSS），一种结合结构与进化信息的马尔可夫链蒙特卡洛（MCMC）框架。RSS在连续对数几率空间中运行，融合梯度引导探索与ESM2语言模型驱动的跳跃机制。其能量函数耦合了AlphaFold2生成的结构目标与ESM2生成的序列先验，平衡精度与生物合理性。在一项模拟蛋白质结合剂设计任务中，RSS在同等计算成本下生成的可设计结构比RSO基线多5倍，结构多样性提升2-3倍。结果表明，RSS是一种高效探索蛋白质设计空间的原理性方法。
原文摘要 · Abstract (English)
Protein design using structure prediction models such as AlphaFold2 has shown remarkable success, but existing approaches like relaxed sequence optimization (RSO) rely on single-path gradient descent and ignore sequence-space constraints, limiting diversity and designability. We introduce Relaxed Sequence Sampling (RSS), a Markov chain Monte Carlo (MCMC) framework that integrates structural and evolutionary information for protein design. RSS operates in continuous logit space, combining gradient-guided exploration with protein language model-informed jumps. Its energy function couples AlphaFold2-derived structural objectives with ESM2-derived sequence priors, balancing accuracy and biological plausibility. In an in silico protein binder design task, RSS produces 5$\times$ more designable structures and 2-3$\times$ greater structural diversity than RSO baselines, at equal computational cost. These results highlight RSS as a principled approach for efficiently exploring the protein design landscape.
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