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.
Nanni L., Pasquali F., Brahnam S., Lumini A., Axenopoulos A. (2020). Convolutional neural networks for 3d protein classification. Cham : Springer [10.1007/978-3-030-42750-4_9].
Convolutional neural networks for 3d protein classification
Lumini A.;
2020
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
