Machine-Learning-Guided Mutagenesis for Directed Evolution of Fluorescent Proteins
| Autor: | Misaki Oikawa, Yutaka Saito, Teppei Niide, Hikaru Nakazawa, Tomoshi Kameda, Mitsuo Umetsu, Koji Tsuda |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Yellow fluorescent protein Green Fluorescent Proteins Biomedical Engineering Mutagenesis (molecular biology technique) Machine learning computer.software_genre Biochemistry Genetics and Molecular Biology (miscellaneous) Green fluorescent protein Machine Learning 03 medical and health sciences Molecular evolution Escherichia coli biology business.industry Chemistry General Medicine Protein engineering Directed evolution Fluorescence Luminescent Proteins 030104 developmental biology Mutagenesis biology.protein Sequence space (evolution) Artificial intelligence Directed Molecular Evolution business computer |
| Zdroj: | ACS synthetic biology. 7(9) |
| ISSN: | 2161-5063 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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