Minding the gaps: The importance of navigating holes in protein fitness landscapes

Neil Thomas; Lucy J Colwell

doi:10.1016/j.cels.2021.10.004

Minding the gaps: The importance of navigating holes in protein fitness landscapes

Cell Syst. 2021 Nov 17;12(11):1019-1020. doi: 10.1016/j.cels.2021.10.004.

Authors

Neil Thomas¹, Lucy J Colwell²

Affiliations

¹ Computer Science Division, University of California, Berkeley, Berkeley, CA 94720, USA.
² Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK; Google Research, 1600 Amphitheatre Parkway, Mountain View, CA 94043, USA. Electronic address: ljc37@cam.ac.uk.

PMID: 34793698
DOI: 10.1016/j.cels.2021.10.004

Abstract

Machine-learning-guided protein design is rapidly emerging as a strategy to find high-fitness multi-mutant variants. In this issue of Cell Systems, Wittman et al. analyze the impact of design decisions for machine-learning-assisted directed evolution (MLDE) on its ability to navigate a fitness landscape and reliably find global optima.

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MeSH terms

Machine Learning*
Proteins*

Substances

Proteins