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