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Status: Unchecked ×Keyword: enzyme activity ×Clear all

2 claims from 1 paper

1. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

   Evaluation of Machine Learning-Assisted Directed Evolution Across Diverse Combinatorial Landscapes

   Li, Yang, Johnston, Gürsoy, Yue and Arnold · Cell Systems · 2024

   The authors compared several machine learning-assisted directed evolution strategies across 16 protein fitness landscapes to see what influences performance, and offer practical guidelines for choosing a strategy.

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   1. UncheckedAcross 16 protein fitness landscapes, machine learning-assisted directed evolution helped most on landscapes that were harder for ordinary directed evolution.“By quantifying landscape navigability with six attributes, we found that MLDE offers a greater advantage on landscapes that are more challenging for directed evolution, especially when focused training is combined with active learning.”
   2. UncheckedTraining on variants chosen by zero-shot predictors beat random sampling on binding and enzyme-activity protein landscapes, though the size of the gain varied.“Despite varying levels of advantage across landscapes, focused training with zero-shot predictors leveraging distinct evolutionary, structural, and stability knowledge sources consistently outperforms random sampling for both binding interactions and enzyme activities.”