Saito, Yutaka Oikawa, Misaki Nakazawa, Hikaru Niide, Teppei Kameda, Tomoshi Tsuda, Koji Umetsu, Mitsuo 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. novel approach;Machine-Learning-Guided Mutagenesis;machine-learning model;sequence space;YFP;screening experiments;protein engineering;protein variants;Fluorescent Proteins Molecular evolution;guide mutagenesis;results show;GFP 2018-08-13
    https://acs.figshare.com/articles/dataset/Machine-Learning-Guided_Mutagenesis_for_Directed_Evolution_of_Fluorescent_Proteins/6985241
10.1021/acssynbio.8b00155.s002