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

Cell Syst

Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK; Google Research, 1600 Amphitheatre Parkway, Mountain View, CA 94043, USA. Electronic address:

Published: November 2021

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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http://dx.doi.org/10.1016/j.cels.2021.10.004DOI Listing

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