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Directed molecular evolution by machine learning and the influence of nonlinear interactions

机译：通过机器学习指导分子进化以及非线性相互作用的影响

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Alternative search strategies for the directed evolution of proteins are presented and compared with each other. In particular, two different machine learning strategies based on partial least-squares regression are developed: the first contains only linear terms that represent a given residue's independent contribution to fitness, the second contains additional nonlinear terms to account for potential epistatic coupling between residues. The nonlinear modeling strategy is further divided into two types, one that contains all possible nonlinear terms and another that makes use of a genetic algorithm to select a subset of important interaction terms. The performance of each modeling type as a function of training set size is analysed. Simulated molecular evolution on a synthetic protein landscape shows the use of machine learning techniques to guide library design can be a powerful addition to library generation methods such as DNA shuffling. (c) 2004 Elsevier Ltd. All rights reserved.
机译：提出了蛋白质定向进化的替代搜索策略，并将其相互比较。特别地，开发了两种基于偏最小二乘回归的不同机器学习策略：第一种仅包含表示给定残基对适应性的独立贡献的线性项，第二种包含其他非线性项以说明残基之间的潜在上位耦合。非线性建模策略又分为两种类型，一种包含所有可能的非线性项，另一种利用遗传算法选择重要交互项的子集。分析了每种建模类型的性能与训练集大小的关系。在合成蛋白质环境中模拟的分子进化表明，使用机器学习技术来指导文库设计可以是对库生成方法（例如DNA改组）的有力补充。 （c）2004 Elsevier Ltd.保留所有权利。

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