Machine-Learning-Guided Mutagenesis for Directed Evolution of Fluorescent Proteins

Molecular evolution
based on mutagenesis is widely used in protein
engineering. However, optimal proteins are often difficult to obtain
due to a large sequence space. Here, we propose a novel approach that
combines molecular evolution with machine learning. In this approach,
we conduct two rounds of mutagenesis where an initial library of protein
variants is used to train a machine-learning model to guide mutagenesis
for the second-round library. This enables us to prepare a small library
suited for screening experiments with high enrichment of functional
proteins. We demonstrated a proof-of-concept of our approach by altering
the reference green fluorescent protein (GFP) so that its fluorescence
is changed into yellow. We successfully obtained a number of proteins
showing yellow fluorescence, 12 of which had longer wavelengths than
the reference yellow fluorescent protein (YFP). These results show
the potential of our approach as a powerful method for directed evolution
of fluorescent proteins.