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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction
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A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction

机译：一种深度学习网络方法，用于从头算起蛋白二级结构预测

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摘要

protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80 percent and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test dataset of 198 proteins, achieving a Q accuracy of 80.7 percent and a Sov accuracy of 74.2 percent.
机译：利用蛋白质二级结构（SS）预测来生成三级结构预测，由于蛋白质的快速发现，对三级结构的预测越来越高。尽管最近的发展已经稍微超过了以前的SS预测方法，但准确性却停滞在80％左右，许多人都怀疑预测是否不能超出此上限。传统上使用神经网络的学科正在尝试新颖的深度学习技术，以刺激进步。由于神经网络在SS预测中一直起着重要作用，因此我们想确定深度学习是否也可以为该领域的发展做出贡献。我们开发了一种SS预测器，该预测器利用了PSI-BLAST和深度学习网络体系结构（我们称为DNSS）生成的特定位置评分矩阵。图形处理单元和CUDA软件可优化深度网络体系结构并有效地训练深度网络。确定了训练过程的最佳参数，并构建了包含三个单独训练的深度网络的工作流程，以便进行精确的预测。这种深度学习网络方法用于预测198种蛋白质的完全独立测试数据集的SS，Q准确度为80.7％，Sov准确度为74.2％。

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