Convolutional neural networks for 3d protein classification
The main goal of this chapter is to develop a system for automatic protein classification. Proteins are classified using CNNs trained on ImageNet, which are tuned using a set of multiview 2D images of 3D protein structures generated by Jmol, which is a 3D molecular graphics program. Jmol generates different types of protein visualizations that emphasize specific properties of a protein’s structure, such as a visualization that displays the backbone structure of the protein as a trace of the Cα atom. Different multiview protein visualizations are generated by uniformly rotating the protein structure around its central X, Y, and Z viewing axes to produce 125 images for each protein. This set of images is then used to fine-tune the pretrained CNNs. The proposed system is tested on two datasets with excellent results. The MATLAB code used in this chapter is available at https://github.com/LorisNanni.
Information Technology and Cybersecurity
Nanni, Loris; Pasquali, Federica; Brahnam, Sheryl; Lumini, Alessandra; and Axenopoulos, Apostolos, "Convolutional neural networks for 3d protein classification" (2020). Articles by College of Business Faculty. 545.
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