Modality Laboratories Sohyaku Innovative Research Division, Mitsubishi Tanabe Pharma Co., LTD., 1000, Kamoshida-cho, Aoba-ku, Yokohama, 227-0033, Japan.
RIKEN Medical Sciences Innovation Hub Program, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama, 230-0045, Japan.
Sci Rep. 2021 Dec 8;11(1):23599. doi: 10.1038/s41598-021-02948-y.
Low-resolution electron density maps can pose a major obstacle in the determination and use of protein structures. Herein, we describe a novel method, called quality assessment based on an electron density map (QAEmap), which evaluates local protein structures determined by X-ray crystallography and could be applied to correct structural errors using low-resolution maps. QAEmap uses a three-dimensional deep convolutional neural network with electron density maps and their corresponding coordinates as input and predicts the correlation between the local structure and putative high-resolution experimental electron density map. This correlation could be used as a metric to modify the structure. Further, we propose that this method may be applied to evaluate ligand binding, which can be difficult to determine at low resolution.
低分辨率电子密度图可能会成为确定和使用蛋白质结构的主要障碍。在此，我们描述了一种新方法，称为基于电子密度图的质量评估（QAEmap），该方法可评估 X 射线晶体学确定的局部蛋白质结构，并可应用于使用低分辨率图校正结构错误。QAEmap 使用三维深度卷积神经网络，将电子密度图及其相应坐标作为输入，并预测局部结构与假定的高分辨率实验电子密度图之间的相关性。这种相关性可用作修改结构的指标。此外，我们提出该方法可用于评估配体结合，这在低分辨率下可能难以确定。
