Metalloproteins play important roles in many biological processes. Mutations at the metal-binding sites may functionally disrupt metalloproteins, initiating severe diseases; however, there seemed to be no effective approach to predict such mutations until now. Here we develop a deep learning approach to successfully predict disease-associated mutations that occur at the metal-binding sites of metalloproteins. We generate energy-based affinity grid maps and physiochemical features of the metal-binding pockets (obtained from different databases as spatial and sequential features) and subsequently implement these features into a multichannel convolutional neural network. After training the model, the multichannel convolutional neural network can successfully predict disease-associated mutations that occur at the first and second coordination spheres of zinc-binding sites with an area under the curve of 0.90 and an accuracy of 0.82. Our approach stands for the first deep learning approach for the prediction of disease-associated metal-relevant site mutations in metalloproteins, providing a new platform to tackle human diseases.

金属蛋白在许多生物学过程中起重要作用。金属结合位点的突变可能在功能上破坏金属蛋白，引发严重疾病。但是，到目前为止，似乎还没有有效的方法来预测这种突变。在这里，我们开发了一种深度学习方法，可以成功预测在金属蛋白的金属结合位点发生的与疾病相关的突变。我们生成基于能量的亲和力网格图和金属结合袋的理化特征（从不同的数据库中获取为空间和顺序特征），然后将这些特征实现为多通道卷积神经网络。训练模型后，多通道卷积神经网络可以成功预测发生在锌结合位点的第一和第二配位区域的疾病相关突变，其面积在0.90下，准确度为0.82。我们的方法代表了第一个深度学习方法，用于预测金属蛋白中与疾病相关的金属相关位点突变，为应对人类疾病提供了新的平台。