标题： 通过嵌入空间中的贝叶斯优化对蛋白质进行定向进化 显示英文标题

标题： Directed Evolution of Proteins via Bayesian Optimization in Embedding Space

摘要： 定向进化是一种通过迭代合成新的蛋白质变体并使用昂贵且耗时的生化筛选来评估其所需特性的实验室过程，以设计功能更优的蛋白质。 机器学习方法可以帮助选择有信息量或有前景的变体进行筛选，从而提高其质量并减少所需的筛选数量。 在本文中，我们提出了一种用于蛋白质定向进化的机器学习辅助新方法，该方法结合了贝叶斯优化与从预训练蛋白质语言模型中提取的信息表示。 我们证明基于序列嵌入的新表示显著提高了贝叶斯优化的性能，在总筛选次数相同的情况下取得了更好的结果。 同时，我们的方法在回归目标下优于最先进的机器学习辅助定向进化方法。 显示英文摘要

摘要： Directed evolution is an iterative laboratory process of designing proteins with improved function by iteratively synthesizing new protein variants and evaluating their desired property with expensive and time-consuming biochemical screening. Machine learning methods can help select informative or promising variants for screening to increase their quality and reduce the amount of necessary screening. In this paper, we present a novel method for machine-learning-assisted directed evolution of proteins which combines Bayesian optimization with informative representation of protein variants extracted from a pre-trained protein language model. We demonstrate that the new representation based on the sequence embeddings significantly improves the performance of Bayesian optimization yielding better results with the same number of conducted screening in total. At the same time, our method outperforms the state-of-the-art machine-learning-assisted directed evolution methods with regression objective.