sb8b00155_si_002.xlsx (3.14 MB)
Machine-Learning-Guided Mutagenesis for Directed Evolution of Fluorescent Proteins
dataset
posted on 2018-08-13, 00:00 authored by Yutaka Saito, Misaki Oikawa, Hikaru Nakazawa, Teppei Niide, Tomoshi Kameda, Koji Tsuda, Mitsuo UmetsuMolecular 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.