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- W2885204620 endingPage "2022" @default.
- W2885204620 startingPage "2014" @default.
- W2885204620 abstract "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." @default.
- W2885204620 created "2018-08-22" @default.
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- W2885204620 date "2018-08-13" @default.
- W2885204620 modified "2023-10-18" @default.
- W2885204620 title "Machine-Learning-Guided Mutagenesis for Directed Evolution of Fluorescent Proteins" @default.
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- W2885204620 doi "https://doi.org/10.1021/acssynbio.8b00155" @default.
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