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Protein sequence design with deep generative models

Wu, Zachary and Johnston, Kadina E. and Arnold, Frances H. and Yang, Kevin K. (2021) Protein sequence design with deep generative models. Current Opinion in Chemical Biology, 65 . pp. 18-27. ISSN 1367-5931. doi:10.1016/j.cbpa.2021.04.004.

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Protein engineering seeks to identify protein sequences with optimized properties. When guided by machine learning, protein sequence generation methods can draw on prior knowledge and experimental efforts to improve this process. In this review, we highlight recent applications of machine learning to generate protein sequences, focusing on the emerging field of deep generative methods.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Wu, Zachary0000-0003-2429-9812
Arnold, Frances H.0000-0002-4027-364X
Yang, Kevin K.0000-0001-9045-6826
Additional Information:© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( Available online 26 May 2021. The authors wish to thank members Lucas Schaus and Sabine Brinkmann-Chen for feedback on early drafts. This work is supported by the Camille and Henry Dreyfus Foundation (ML-20-194) and the NSF Division of Chemical, Bioengineering, Environmental, and Transport Systems (1937902). The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Funding AgencyGrant Number
Camille and Henry Dreyfus FoundationML-20-194
Record Number:CaltechAUTHORS:20210413-080510593
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Official Citation:Zachary Wu, Kadina E. Johnston, Frances H. Arnold, Kevin K. Yang, Protein sequence design with deep generative models, Current Opinion in Chemical Biology, Volume 65, 2021, Pages 18-27, ISSN 1367-5931,
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108709
Deposited By: Tony Diaz
Deposited On:13 Apr 2021 21:45
Last Modified:27 May 2021 20:46

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