Google Research

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

Cell Systems (2021)

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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