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

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.