Cluster learning-assisted directed evolution-Reference-Cited by-同舟云学术

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Cluster learning-assisted directed evolution

Published:2021-12 Issue:12 Volume:1 Page:809-818
ISSN:2662-8457
Container-title:Nature Computational Science
language:en
Short-container-title:Nat Comput Sci

Author:

Qiu Yuchi^ORCID,Hu Jian,Wei Guo-Wei^ORCID

Funder

U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences

NSF | Directorate for Mathematical & Physical Sciences | Division of Mathematical Sciences

NSF | Directorate for Computer & Information Science & Engineering | Division of Information and Intelligent Systems

MSU Foundation; Pfizer; Bristol-Myers Squibb 65109

Publisher

Springer Science and Business Media LLC

Subject

General Medicine

Link

https://www.nature.com/articles/s43588-021-00168-y.pdf

Reference60 articles.

1. Tawfik, O. K. & S, D. Enzyme promiscuity: a mechanistic and evolutionary perspective. Annu. Rev. Biochem. 79, 471–505 (2010).

2. Wittmann, B. J., Yue, Y. & Arnold, F. H. Informed training set design enables efficient machine learning-assisted directed protein evolution. Cell Syst. 12, 1026–1045.e7 (2021).

3. Wu, Z., Kan, S. J., Lewis, R. D., Wittmann, B. J. & Arnold, F. H. Machine learning-assisted directed protein evolution with combinatorial libraries. Proc. Natl Acad. Sci. USA 116, 8852–8858 (2019).

4. Wu, N. C., Dai, L., Olson, C. A., Lloyd-Smith, J. O. & Sun, R. Adaptation in protein fitness landscapes is facilitated by indirect paths. eLife 5, e16965 (2016).

5. Yang, K. K., Wu, Z. & Arnold, F. H. Machine-learning-guided directed evolution for protein engineering. Nat. Methods 16, 687–694 (2019).

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