Abstract
Protein surface shape plays an essential role in various function of proteins. In order to efficiently investigate protein function and evolutionary history, we introduce a global protein surface shape representation called EMNets. EMNets provides an effective and accurate way of protein surface representation and similarity search, and thus contributes to biomedical research. The method uses a Convolutional Autoencoder (CAE) neural network to learn the geometric information of three-dimensional (3D) density maps in a data-driven manner. Our method effectively represents a 3D cryo-electron microscopy density map by using a descriptor consists of only 256 numeric variables which is called EMNets descriptor. Based on EMNets descriptor, we are able to retrieve similar protein surfaces using k-nearest-neighbor algorithm in real-time. The search results of protein surface represented with the EMNets descriptor has shown high agreement with the existing Combinatorial Extension (CE) algorithm of sequence and structure similarity search. Overall, EMNets is a powerful tool in comparing 3D protein structures obtained by cryo-electron microscopy.
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