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Advances in machine learning for directed evolution

Wittmann, Bruce J. and Johnston, Kadina E and Wu, Zachary and Arnold, Frances H. (2021) Advances in machine learning for directed evolution. Current Opinion in Structural Biology, 69 . pp. 11-18. ISSN 0959-440X. doi:10.1016/j.sbi.2021.01.008. https://resolver.caltech.edu/CaltechAUTHORS:20210301-153151041

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Abstract

Machine learning (ML) can expedite directed evolution by allowing researchers to move expensive experimental screens in silico. Gathering sequence-function data for training ML models, however, can still be costly. In contrast, raw protein sequence data is widely available. Recent advances in ML approaches use protein sequences to augment limited sequence-function data for directed evolution. We highlight contributions in a growing effort to use sequences to reduce or eliminate the amount of sequence-function data needed for effective in silico screening. We also highlight approaches that use ML models trained on sequences to generate new functional sequence diversity, focusing on strategies that use these generative models to efficiently explore vast regions of protein space.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.sbi.2021.01.008DOIArticle
ORCID:
AuthorORCID
Wittmann, Bruce J.0000-0001-8144-9157
Johnston, Kadina E0000-0002-2214-3534
Wu, Zachary0000-0003-2429-9812
Arnold, Frances H.0000-0002-4027-364X
Additional Information:© 2021 Elsevier Ltd. Available online 26 February 2021. This work was supported by the Amgen Chem-Bio-Engineering Award (CBEA), the NSF Division of Chemical, Bioengineering, Environmental and Transport Systems (1937902), the Camille and Henry Dreyfus Foundation (ML-20-194), and the Caltech Carver Mead New Adventure Seed Fund. CRediT authorship contribution statement: Bruce J Wittmann: Conceptualization, Writing - original draft, Writing - review & editing, Visualization. Kadina E Johnston: Conceptualization, Writing - original draft, Writing - review & editing, Visualization. Zachary Wu: Conceptualization, Writing - original draft, Writing - review & editing. Frances H Arnold: Conceptualization, Writing - original draft, Writing - review & editing. Conflict of interest statement: Nothing declared.
Funders:
Funding AgencyGrant Number
Amgen FoundationUNSPECIFIED
NSFCBET-1937902
Camille and Henry Dreyfus FoundationML-20-194
Carver Mead New Adventures FundUNSPECIFIED
DOI:10.1016/j.sbi.2021.01.008
Record Number:CaltechAUTHORS:20210301-153151041
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210301-153151041
Official Citation:Bruce J Wittmann, Kadina E Johnston, Zachary Wu, Frances H Arnold, Advances in machine learning for directed evolution, Current Opinion in Structural Biology, Volume 69, 2021, Pages 11-18, ISSN 0959-440X, https://doi.org/10.1016/j.sbi.2021.01.008.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108258
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:01 Mar 2021 23:40
Last Modified:16 Nov 2021 19:10

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