Predicting disease-associated mutation of metal-binding sites in proteins using a deep learning approach

Abstract

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. Metals can bind to proteins to fulfil important biological functions. Predicting the features of mutated binding sites can thus help us understand the connection between specific mutations and their role in diseases.