Can Machine Learning Guide Directed Evolution Of Functional Proteins
BIOPHYSICAL JOURNAL(2020)
摘要
Molecular evolution based on mutagenesis is widely used in protein engineering, where critical amino acid residues of a target protein are identified based on available structural information and mutated for function alteration and maturation. In iterative saturation mutagenesis (ISM), one of the principal molecular evolution methods, mutagenesis proceeds in a step-wise manner: however, ISM does not always lead to the optimal sequence, because the effects of mutations on function are often synergistic or antagonistic. Here, we propose a novel approach that combines molecular evolution with machine learning. In this approach, we iterated mutagenesis, following machine-learning guidance: a Gaussian process is trained with a last small library to propose the next-round mutagenesis library. This enables to prepare a small library suited for screening experiments with high enrichment of functional proteins. A first library of variants was generated, and the sequence and functional data acquired from the variants in the library were used for training a machine-learning model to create the second-round library. The library containing the positive candidate variants predicted by machine-learning are analyzed, and the data are used for training a machine-learning model again. We show the potential of our approach as a powerful platform for accelerating the discovery of functional proteins fluorescent in the case of fluorescence protein and enzyme.
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关键词
proteins,machine learning,evolution
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