Machine-Learning-Guided Mutagenesis for Directed Evolution of Fluorescent Proteins,ACS Synthetic Biology

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Machine-Learning-Guided Mutagenesis for Directed Evolution of Fluorescent Proteins
ACS Synthetic Biology ( IF 3.7 ) Pub Date : 2018-08-13 00:00:00 , DOI: 10.1021/acssynbio.8b00155
Yutaka Saito _{1,

2} , Misaki Oikawa ₃ , Hikaru Nakazawa ₃ , Teppei Niide ₃ , Tomoshi Kameda ₁ , Koji Tsuda _{4,

5,

6} , Mitsuo Umetsu _{3,

5}

Affiliation

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.

中文翻译：

机器学习指导的诱变，用于荧光蛋白的定向进化

基于诱变的分子进化被广泛应用于蛋白质工程中。然而，由于大的序列空间，通常难以获得最佳的蛋白质。在这里，我们提出了一种将分子进化与机器学习相结合的新颖方法。在这种方法中，我们进行了两轮诱变，其中使用蛋白质变体的初始文库来训练机器学习模型，以指导第二轮文库的诱变。这使我们能够准备一个小型文库，适用于筛选功能蛋白含量高的实验。我们通过更改参考绿色荧光蛋白（GFP）使其荧光变成黄色来证明了我们方法的概念验证。我们成功地获得了许多显示黄色荧光的蛋白质，其中12个具有比参考黄色荧光蛋白（YFP）更长的波长。这些结果表明，我们的方法作为一种有力的荧光蛋白定向进化方法具有潜力。

更新日期：2018-08-13

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