  In this paper we address the problem of protein classification starting from
a multi-view 2D representation of proteins. From each 3D protein structure, a
large set of 2D projections is generated using the protein visualization
software Jmol. This set of multi-view 2D representations includes 13 different
types of protein visualizations that emphasize specific properties of protein
structure (e.g., a backbone visualization that displays the backbone structure
of the protein as a trace of the C{\alpha} atom). Each type of representation
is used to train a different Convolutional Neural Network (CNN), and the fusion
of these CNNs is shown to be able to exploit the diversity of different types
of representations to improve classification performance. In addition, several
multi-view projections are obtained by uniformly rotating the protein structure
around its central X, Y, and Z viewing axes to produce 125 images. This
approach can be considered a data augmentation method for improving the
performance of the classifier and can be used in both the training and the
testing phases. Experimental evaluation of the proposed approach on two
datasets demonstrates the strength of the proposed method with respect to the
other state-of-the-art approaches. The MATLAB code used in this paper is
available at https://github.com/LorisNanni.
