EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation.
EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation.
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作者：
Amidi A;Amidi S;Vlachakis D;Megalooikonomou V;Paragios N;Zacharaki EI
在过去的十年中，随着计算能力的显着进步以及数据可用性的不断提高，深度学习技术因其在计算机视觉问题上的出色表现而变得越来越受欢迎。自 1999 年以来，蛋白质数据库 (PDB) 的规模增加了 15 倍以上，这使得旨在通过氨基酸组成预测酶功能的模型得以扩展。然而，氨基酸序列在本质上不如蛋白质结构保守，因此被认为是蛋白质功能不太可靠的预测因子。本文提出了 EnzyNet，一种新型 3D 卷积神经网络分类器，它仅根据基于体素的空间结构来预测酶的酶委员会数量。还检查了生化特性的空间分布作为补充信息。在 PDB 中包含 63,558 种酶的大型数据集上对双层架构进行了研究，仅利用蛋白质形状的二进制表示即可达到 78.4% 的准确率。代码和数据集可在 处获得。
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 .
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DOI：
10.1186/1687-4153-2012
发表时间：
2012-12-01
期刊：
EURASIP JOURNAL ON BIOINFORMATICS AND SYSTEMS BIOLOGY
影响因子：
--
作者：
Kumar, Chetan;Choudhary, Alok
通讯作者：
Choudhary, Alok
Biopython：用于计算分子生物学和生物信息学的免费 Python 工具。
DOI：
10.1093/bioinformatics/btp163
发表时间：
2009-06-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Cock PJ;Antao T;Chang JT;Chapman BA;Cox CJ;Dalke A;Friedberg I;Hamelryck T;Kauff F;Wilczynski B;de Hoon MJ
通讯作者：
de Hoon MJ
影响因子：
2.9
作者：
Illergard, Kristoffer;Ardell, David H.;Elofison, Arne
通讯作者：
Elofison, Arne
影响因子：
5.8
作者：
Di Lena, Pietro;Nagata, Ken;Baldi, Pierre
通讯作者：
Baldi, Pierre
影响因子：
3.7
作者：
Asgari E;Mofrad MR
通讯作者：
Mofrad MR