研究蛋白结合剂设计中风险对贝叶斯优化的影响
Why risk matters for protein binder design
- 从投资组合优化借鉴指标，量化优化初始阶段表现和风险
- 发现不同蛋白质景观下存在风险-性能权衡的最优模型组合
- 强调模型选择需大量计算与统计分析，不能仅看平均表现
贝叶斯优化（BO）在蛋白质工程中应用日益广泛，成为基准测试的重要对象。然而，现有比较常忽视现实中的风险与成本约束。本文在11个蛋白结合剂适应度景观上，对比了72种编码、代理模型与获取函数的组合，从这一视角出发。借鉴投资组合优化理论，采用指标量化冷启动性能、优化风险及达到适应度阈值所需的总预算。结果表明：在风险-性能轴上存在帕累托最优模型组合；偏好随探索景观变化；景观特性如上位性与模型平均及最差表现有强相关性。研究还指出，严格的模型选择需投入大量计算与统计工作。
原文摘要 · Abstract (English)
Bayesian optimization (BO) has recently become more prevalent in protein engineering applications and hence has become a fruitful target of benchmarks. However, current BO comparisons often overlook real-world considerations like risk and cost constraints. In this work, we compare 72 model combinations of encodings, surrogate models, and acquisition functions on 11 protein binder fitness landscapes, specifically from this perspective. Drawing from the portfolio optimization literature, we adopt metrics to quantify the cold-start performance relative to a random baseline, to assess the risk of an optimization campaign, and to calculate the overall budget required to reach a fitness threshold. Our results suggest the existence of Pareto-optimal models on the risk-performance axis, the shift of this preference depending on the landscape explored, and the robust correlation between landscape properties such as epistasis with the average and worst-case model performance. They also highlight that rigorous model selection requires substantial computational and statistical efforts.
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