Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering.

ABSTRACT: The effective design of combinatorial libraries to balance fitness and diversity facilitates the engineering of useful enzyme functions, particularly those that are poorly characterized or unknown in biology. We introduce MODIFY, a machine learning (ML) algorithm that learns from natural protein sequences to infer evolutionarily plausible mutations and predict enzyme fitness. MODIFY co-optimizes predicted fitness and sequence diversity of starting libraries, prioritizing high-fitness variants while ensuring broad sequence coverage. In silico evaluation shows that MODIFY outperforms state-of-the-art unsupervised methods in zero-shot fitness prediction and enables ML-guided directed evolution with enhanced efficiency. Using MODIFY, we engineer generalist biocatalysts derived from a thermosta

SUBMITTER: Ding K 

PROVIDER: S-EPMC11289365 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature