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A Deep Learning Network Approach to <italic>ab initio</italic> Protein Secondary Structure Prediction

深度学习 人工智能 库达 人工神经网络 工作流程 计算机科学 机器学习 并行计算 数据库
作者
Matt Spencer,Jesse Eickholt,Jianlin Cheng
出处
期刊:IEEE/ACM Transactions on Computational Biology and Bioinformatics [Institute of Electrical and Electronics Engineers]
卷期号:12 (1): 103-112 被引量:289
标识
DOI:10.1109/tcbb.2014.2343960
摘要

Ab initio 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 Q3 accuracy of 80.7 percent and a Sov accuracy of 74.2 percent.

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