Directed evolution (DE) is a technique for protein engineering that involves iterative rounds of mutagenesis and screening to search for sequences that optimize a given property, such as binding affinity to a specified target. Unfortunately, the underlying optimization problem is under-determined, and so mutations introduced to improve the specified property may come at the expense of unmeasured, but nevertheless important properties (ex. solubility, thermostability, etc). We address this issue by formulating DE as a regularized Bayesian optimization problem where the regularization term reflects evolutionary or structure-based constraints. We applied our approach to DE to three representative proteins, GB1, BRCA1, and SARS-CoV-2 Spike, and evaluated both evolutionary and structure-based regularization terms. The results of these experiments demonstrate that: (i) structure-based regularization usually leads to better designs (and never hurts), compared to the unregularized setting; (ii) evolutionary-based regularization tends to be least effective; and (iii) regularization leads to better designs because it effectively focuses the search in certain areas of sequence space, making better use of the experimental budget. Additionally, like previous work in Machine learning assisted DE, we find that our approach significantly reduces the experimental burden of DE, relative to model-free methods. Introducing regularization into a Bayesian ML-assisted DE framework alters the exploratory patterns of the underlying optimization routine, and can shift variant selections towards those with a range of targeted and desirable properties. In particular, we find that structure-based regularization often improves variant selection compared to unregularized approaches, and never hurts.

中文翻译：

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用于定向蛋白质进化的具有进化和基于结构的正则化的贝叶斯优化

定向进化 (DE) 是一种蛋白质工程技术，它涉及反复的诱变和筛选，以搜索优化给定特性的序列，例如与特定目标的结合亲和力。不幸的是，潜在的优化问题是不确定的，因此引入突变以改善指定的特性可能会以牺牲不可测量但仍然重要的特性（例如溶解度、热稳定性等）为代价。我们通过将 DE 制定为正则化贝叶斯优化问题来解决这个问题，其中正则化项反映了进化或基于结构的约束。我们将我们的 DE 方法应用于三种代表性蛋白质 GB1、BRCA1 和 SARS-CoV-2 Spike，并评估了进化和基于结构的正则化术语。这些实验的结果表明：(i) 与非正则化设置相比，基于结构的正则化通常会带来更好的设计（并且不会造成伤害）；(ii) 基于进化的正则化往往最不有效；(iii) 正则化导致更好的设计，因为它有效地将搜索集中在序列空间的某些区域，更好地利用实验预算。此外，与之前在机器学习辅助 DE 方面的工作一样，我们发现相对于无模型方法，我们的方法显着减少了 DE 的实验负担。将正则化引入贝叶斯 ML 辅助的 DE 框架会改变底层优化例程的探索模式，并且可以将变体选择转向具有一系列目标和理想属性的变体选择。特别是，