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.
Original language | English |
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Pages (from-to) | 2014-2022 |
Number of pages | 9 |
Journal | ACS Synthetic Biology |
Volume | 7 |
Issue number | 9 |
DOIs | |
Publication status | Published - 2018 Sep 21 |
Keywords
- fluorescent protein
- machine learning
- molecular evolution
- mutagenesis
- protein engineering
ASJC Scopus subject areas
- Biomedical Engineering
- Biochemistry, Genetics and Molecular Biology (miscellaneous)