Discovery of Novel Gain-of-Function Mutations Guided by Structure-Based Deep Learning,ACS Synthetic Biology

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Discovery of Novel Gain-of-Function Mutations Guided by Structure-Based Deep Learning
ACS Synthetic Biology ( IF 3.7 ) Pub Date : 2020-10-16 , DOI: 10.1021/acssynbio.0c00345
Raghav Shroff ₁ , Austin W Cole ₁ , Daniel J Diaz ₂ , Barrett R Morrow ₁ , Isaac Donnell ₁ , Ankur Annapareddy ₃ , Jimmy Gollihar ₃ , Andrew D Ellington ₁ , Ross Thyer ₁

Affiliation

Despite the promise of deep learning accelerated protein engineering, examples of such improved proteins are scarce. Here we report that a 3D convolutional neural network trained to associate amino acids with neighboring chemical microenvironments can guide identification of novel gain-of-function mutations that are not predicted by energetics-based approaches. Amalgamation of these mutations improved protein function in vivo across three diverse proteins by at least 5-fold. Furthermore, this model provides a means to interrogate the chemical space within protein microenvironments and identify specific chemical interactions that contribute to the gain-of-function phenotypes resulting from individual mutations.

中文翻译：

在基于结构的深度学习指导下发现新的功能增益突变

尽管深度学习加速蛋白质工程的前景广阔，但此类改进蛋白质的例子很少。在这里，我们报告了一个 3D 卷积神经网络，它被训练以将氨基酸与邻近的化学微环境相关联，可以指导识别基于能量学的方法无法预测的新的功能获得性突变。这些突变的合并将三种不同蛋白质的体内蛋白质功能提高了至少 5 倍。此外，该模型提供了一种方法来询问蛋白质微环境中的化学空间，并确定有助于由个体突变引起的功能获得表型的特定化学相互作用。

更新日期：2020-11-21

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