用生物启发搜索策略，在有限测试次数内高效优化蛋白质序列。
Self-Improvement Imitation with Biologically Guided Search for Protein Design Under Oracle Budgets
- 分步决策：先选位置再选氨基酸，提升突变精准度。
- 8个蛋白景观中全部最优，且早期改进更快。
- 适合资源受限的蛋白质设计，尤其抗噪声和小样本场景。
在严格评估预算下进行蛋白质序列优化，需在巨大组合空间中探索并确保每次评估有价值。现有强化学习与离线生成方法易受代理噪声影响，且不区分位置的突变可能破坏关键残基功能。我们提出SILO，一种基于轨迹级自提升模仿学习的预算受限蛋白质设计框架。SILO采用分层编辑策略，将每个突变分解为位置选择与残基选择。每轮主动学习中，策略通过无放回的随机束搜索（SBS）采样候选路径，并结合基于UCB的代理集成与丙氨酸扫描评分（AFS），筛选出功能相关突变用于体外评估。策略通过本轮最优标注轨迹的下一步交叉熵模仿进行更新，避免价值函数估计。在八组复现的蛋白质适应度景观及五种强基线对比中，SILO在全部8个景观上均取得最高最大值与前100名平均适应度，且早期进步更快。在低数据与噪声代理的压力测试中（每种设置两个景观），当多个基线性能下降时，SILO仍保持竞争力或最优。消融实验表明，SBS结合AFS贡献主要提升，迭代模仿带来额外改进。代码已公开：https://github.com/grimmlab/SILO.git
原文摘要 · Abstract (English)
Protein sequence optimization under tight oracle budgets requires methods that explore vast combinatorial spaces while making each evaluation informative. Existing reinforcement learning and off-policy generative approaches often degrade under surrogate noise, and position-agnostic mutation proposals risk disrupting functionally critical residues. We introduce SILO, a trajectory-level self-improvement imitation framework for oracle-budgeted protein design. SILO uses a hierarchical edit policy that decomposes each mutation into a position choice followed by a residue choice. In each active-learning round, the policy samples candidate trajectories via incremental stochastic beam search without replacement (SBS), and a UCB-based proxy ensemble, combined with an alanine-scan fitness score (AFS), selects candidates with functionally relevant edits for in silico oracle evaluation. The policy is then updated by next-action cross-entropy imitation on the round's best oracle-labeled trajectories, avoiding value-function estimation. Across eight reproduced protein fitness landscapes and five strong baselines from prior work, SILO achieves the highest maximum and top-100 mean fitness on 8 of 8 landscapes within our evaluations, often exhibiting faster early-stage improvement. In low-data and noisy-proxy stress tests on two landscapes per setting, SILO remains competitive or best when several baselines degrade. Ablations show that SBS with AFS account for much of the gains, with iterative imitation providing additional improvement. Code is available at: https://github.com/grimmlab/SILO.git
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