三肽
肽
工作流程
人类蛋白质组计划
可扩展性
直觉
化学
计算机科学
人工智能
计算生物学
机器学习
生物化学
数据库
生物
蛋白质组学
哲学
认识论
基因
作者
Rohit Batra,Troy D. Loeffler,Henry Chan,Srilok Srinivasan,Honggang Cui,Ivan V. Korendovych,Vikas Nanda,Liam C. Palmer,Lee A. Solomon,H. Christopher Fry,Subramanian K. R. S. Sankaranarayanan
出处
期刊:Nature Chemistry
[Springer Nature]
日期:2022-10-31
卷期号:14 (12): 1427-1435
被引量:35
标识
DOI:10.1038/s41557-022-01055-3
摘要
Peptide materials have a wide array of functions, from tissue engineering and surface coatings to catalysis and sensing. Tuning the sequence of amino acids that comprise the peptide modulates peptide functionality, but a small increase in sequence length leads to a dramatic increase in the number of peptide candidates. Traditionally, peptide design is guided by human expertise and intuition and typically yields fewer than ten peptides per study, but these approaches are not easily scalable and are susceptible to human bias. Here we introduce a machine learning workflow-AI-expert-that combines Monte Carlo tree search and random forest with molecular dynamics simulations to develop a fully autonomous computational search engine to discover peptide sequences with high potential for self-assembly. We demonstrate the efficacy of the AI-expert to efficiently search large spaces of tripeptides and pentapeptides. The predictability of AI-expert performs on par or better than our human experts and suggests several non-intuitive sequences with high self-assembly propensity, outlining its potential to overcome human bias and accelerate peptide discovery.
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