Protein sequencing

Gene

Machine learning

Banach space

Mutant

Protein engineering

Artificial intelligence

Peptide sequence

Workflow

Enzyme

Pure mathematics

In silico

Sequence (biology)

Directed Molecular Evolution

Sequence space

Mathematics

Computer science

Genetics

Biochemistry

Computational biology

Database

Biology

Directed evolution

Proceedings of the National Academy of Sciences

To reduce experimental effort associated with directed protein evolution and to explore the sequence space encoded by mutating multiple positions simultaneously, we incorporate machine learning in the directed evolution workflow. Combinatorial sequence space can be quite expensive to sample experimentally, but machine learning models trained on tested variants provide a fast method for testing sequence space computationally. We validate this approach on a large published empirical fitness landscape for human GB1 binding protein, demonstrating that machine learning-guided directed evolution finds variants with higher fitness than those found by other directed evolution approaches. We then provide an example application in evolving an enzyme to produce each of the two possible product enantiomers (stereodivergence) of a new-to-nature carbene Si-H insertion reaction. The approach predicted libraries enriched in functional enzymes and fixed seven mutations in two rounds of evolution to identify variants for selective catalysis with 93% and 79% ee. By greatly increasing throughput with in silico modeling, machine learning enhances the quality and diversity of sequence solutions for a protein engineering problem.

Machine learning-assisted directed protein evolution with combinatorial libraries