Affiliation:
1. Research Center for Industries of the Future Westlake University Hangzhou 310030 China
2. School of Engineering Westlake University Hangzhou 310030 China
3. AI Lab Research Center for Industries of the Future Westlake University Hangzhou 310030 China
4. Department of Chemistry School of Science Westlake University Hangzhou 310030 China
5. Institute of Natural Sciences Westlake Institute for Advanced Study 18 Shilongshan Road Hangzhou Zhejiang Province 310024 China
Abstract
AbstractSelf‐assembling of peptides is essential for a variety of biological and medical applications. However, it is challenging to investigate the self‐assembling properties of peptides within the complete sequence space due to the enormous sequence quantities. Here, it is demonstrated that a transformer‐based deep learning model is effective in predicting the aggregation propensity (AP) of peptide systems, even for decapeptide and mixed‐pentapeptide systems with over 10 trillion sequence quantities. Based on the predicted AP values, not only the aggregation laws for designing self‐assembling peptides are derived, but the transferability relation among the APs of pentapeptides, decapeptides, and mixed pentapeptides is also revealed, leading to discoveries of self‐assembling peptides by concatenating or mixing, as consolidated by experiments. This deep learning approach enables speedy, accurate, and thorough search and design of self‐assembling peptides within the complete sequence space of oligopeptides, advancing peptide science by inspiring new biological and medical applications.
Funder
National Natural Science Foundation of China
Ministry of Science and Technology of the People's Republic of China
Subject
General Physics and Astronomy,General Engineering,Biochemistry, Genetics and Molecular Biology (miscellaneous),General Materials Science,General Chemical Engineering,Medicine (miscellaneous)
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献