Advances in machine learning for directed evolution
Advances in machine learning for directed evolution
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DOI：
10.1016/j.sbi.2021.01.008
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发表时间：
2021-08-01
影响因子：
6.8
通讯作者：
Arnold, Frances H.
中科院分区：
文献类型：
--
作者：
Wittmann, Bruce J.;Johnston, Kadina E.;Arnold, Frances H.
机器学习(ML)可以通过允许研究人员在Silico中移动昂贵的实验屏幕来加速定向进化。然而，收集序列函数数据来训练ML模型仍然是昂贵的。相比之下，原始蛋白质序列数据却随处可得。ML方法的最新进展是使用蛋白质序列来增加有限的序列功能数据以进行定向进化。我们强调，在使用序列来减少或消除有效的电子筛查所需的序列功能数据量方面，我们做出了越来越大的贡献。我们还重点介绍了使用对序列训练的ML模型来生成新的功能序列多样性的方法，重点介绍了使用这些生成模型来有效探索蛋白质空间的广大区域的策略。
Machine learning (ML) can expedite directed evolution by allowing researchers to move expensive experimental screens in silico. Gathering sequence-function data for training ML models, however, can still be costly. In contrast, raw protein sequence data is widely available. Recent advances in ML approaches use protein sequences to augment limited sequence-function data for directed evolution. We highlight contributions in a growing effort to use sequences to reduce or eliminate the amount of sequence-function data needed for effective in silico screening. We also highlight approaches that use ML models trained on sequences to generate new functional sequence diversity, focusing on strategies that use these generative models to efficiently explore vast regions of protein space.