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Machine learning-assisted directed protein evolution with combinatorial libraries
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DOI：10.1073/pnas.1901979116.png)
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摘要
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To reduce experimental effort associated with directed protein evolution and to explore the sequence space encoded by mutating multiple positions simultaneously, we incorporate machine learning into the directed evolution workflow. Combinatorial sequence space can be quite expensive to sample experimentally, but machine-learning models trained on tested variants provide a fast method for testing sequence space computationally. We validated this approach on a large published empirical fitness landscape for human GB1 binding protein, demonstrating that machine learning-guided directed evolution finds variants with higher fitness than those found by other directed evolution approaches. We then provide an example application in evolving an enzyme to produce each of the two possible product enantiomers (i.e., stereodivergence) of a new-to-nature carbene Si-H insertion reaction. The approach predicted libraries enriched in functional enzymes and fixed seven mutations in two rounds of evolution to identify variants for selective catalysis with 93% and 79% ee (enantiomeric excess). By greatly increasing throughput with in silico modeling, machine learning enhances the quality and diversity of sequence solutions for a protein engineering problem.
Keyword:
protein engineering
machine learning
directed evolution
enzyme
catalysis
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期刊
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IF:
9.1
论文数:
10.8W
被引数:
73.5W
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Deep generative models of genetic variation capture the effects of mutations遗传变异的深度生成模型捕捉了突变的影响.png)
NATURE METHODSOAAI
NATURE METHODSIF32.1
OAAI分享收藏One contact for every twelve residues allows robust and accurate topology-level protein structure modeling每十二个残基一个触点可实现稳健且准确的拓扑级蛋白质结构建模OAAI
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