Machine-Learning-Guided Mutagenesis for Directed Evolution of Fluorescent Proteins Yutaka Saito Misaki Oikawa Hikaru Nakazawa Teppei Niide Tomoshi Kameda Koji Tsuda Mitsuo Umetsu 10.1021/acssynbio.8b00155.s002 https://acs.figshare.com/articles/dataset/Machine-Learning-Guided_Mutagenesis_for_Directed_Evolution_of_Fluorescent_Proteins/6985241 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. 2018-08-13 00:00:00 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