期刊
ACS SYNTHETIC BIOLOGY
卷 7, 期 9, 页码 2014-2022出版社
AMER CHEMICAL SOC
DOI: 10.1021/acssynbio.8b00155
关键词
protein engineering; machine learning; molecular evolution; mutagenesis; fluorescent protein
资金
- Materials Research by Information Integration Initiative (MI2I) from Japan Science and Technology Agency (JST)
- Core Research for Evolutional Science and Technology (CREST) from Japan Science and Technology Agency (JST) [JPMJCR1502]
- Ministry of Education, Culture, Sports, Science and Technology (MEXT)
- MEXT [16H04570, 16K14483]
- JSPS KAKENHI [17H06410]
- Grants-in-Aid for Scientific Research [16H04570, 17H06410, 16K14483] Funding Source: KAKEN
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.
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