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Convolutional Neural Networks for 3D Protein Classification

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Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 186))

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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.

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Acknowledgements

We would like to acknowledge the support that NVIDIA provided us through the GPU Grant Program. We used a donated TitanX GPU to train CNNs used in this work.

Author information

Authors and Affiliations

  1. Department of Information Engineering, University of Padua, Via Gradenigo 6, 35131, Padova, Italy

    Loris Nanni & Federica Pasquali

  2. Department of Information Technology and Cybersecurity, Missouri State University, Glass Hall, Room 387, 901 S. National, Springfield, MO, 65804, USA

    Sheryl Brahnam

  3. DISI - Department of Computer Science and Engineering, Università di Bologna, Via Sacchi 3, 47521, Cesena, Italy

    Alessandra Lumini

  4. University of Thessaly, Volos, Greece

    Apostolos Axenopoulos

Authors
  1. Loris Nanni
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  2. Federica Pasquali
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  3. Sheryl Brahnam
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  4. Alessandra Lumini
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  5. Apostolos Axenopoulos
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Corresponding author

Correspondence to Loris Nanni .

Editor information

Editors and Affiliations

  1. Department of Information Engineering, University of Padova, Padova, Italy

    Loris Nanni

  2. Computer Information Systems, Missouri State University, Springfield, MO, USA

    Sheryl Brahnam

  3. Department of Information Technology and Cybersecurity, Missouri State University, Springfield, MO, USA

    Rick Brattin

  4. Department of Information Engineering, Intelligent Autonomous Systems Lab, Padova, Padova, Italy

    Stefano Ghidoni

  5. University of Technology, Sydney, Australia

    Lakhmi C. Jain

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Nanni, L., Pasquali, F., Brahnam, S., Lumini, A., Axenopoulos, A. (2020). Convolutional Neural Networks for 3D Protein Classification. In: Nanni, L., Brahnam, S., Brattin, R., Ghidoni, S., Jain, L. (eds) Deep Learners and Deep Learner Descriptors for Medical Applications. Intelligent Systems Reference Library, vol 186. Springer, Cham. https://doi.org/10.1007/978-3-030-42750-4_9

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