Active learning-assisted directed evolution.

Abstract excerpt

Directed evolution (DE) is a powerful tool to optimize protein fitness for a specific application. However, DE can be inefficient when mutations exhibit non-additive, or epistatic, behavior. Here, we present Active Learning-assisted Directed Evolution (ALDE), an iterative machine learning-assisted DE workflow that leverages uncertainty quantification to explore the search space of proteins more efficiently than...

Topics

• Directed Molecular Evolution
• Protein Engineering
• Problem-Based Learning
• Mutation
• Machine Learning
• Catalytic Domain