EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation

PeerJ

article

Afshine Amidi¹  Shervine Amidi²  Dimitrios Vlachakis³  Vasileios Megalooikonomou³  Nikos Paragios²  Evangelia I. Zacharaki² 

[1] Massachusetts Institute of Technology;Center for Visual Computing, Department of Applied Mathematics, Ecole Centrale de Paris ,(CentraleSupélec), Châtenay-Malabry;MDAKM Group, Department of Computer Engineering and Informatics, University of Patras

关键词: Deep learning;    3D convolutional neural networks;    EnzyNet;    Enzyme classification;   

DOI  :  10.7717/peerj.4750

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

During the past decade, with the significant progress of computational power as well as ever-rising data availability, deep learning techniques became increasingly popular due to their excellent performance on computer vision problems. The size of the Protein Data Bank (PDB) has increased more than 15-fold since 1999, which enabled the expansion of models that aim at predicting enzymatic function via their amino acid composition. Amino acid sequence, however, is less conserved in nature than protein structure and therefore considered a less reliable predictor of protein function. This paper presents EnzyNet, a novel 3D convolutional neural networks classifier that predicts the Enzyme Commission number of enzymes based only on their voxel-based spatial structure. The spatial distribution of biochemical properties was also examined as complementary information. The two-layer architecture was investigated on a large dataset of 63,558 enzymes from the PDB and achieved an accuracy of 78.4% by exploiting only the binary representation of the protein shape. Code and datasets are available at https://github.com/shervinea/enzynet.

【 授权许可】

CC BY