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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. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20210413-080510593

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Abstract

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:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2104.04457arXivDiscussion Paper
ORCID:
AuthorORCID
Wu, Zachary0000-0003-2429-9812
Arnold, Frances H.0000-0002-4027-364X
Yang, Kevin K.0000-0001-9045-6826
Additional Information:Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0). 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).
Funders:
Funding AgencyGrant Number
Camille and Henry Dreyfus FoundationML-20-194
NSFCBET-1937902
Record Number:CaltechAUTHORS:20210413-080510593
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210413-080510593
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108709
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:13 Apr 2021 21:45
Last Modified:19 Apr 2021 22:44

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