Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks
Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks
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DOI：
10.1093/bioinformatics/btw678
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发表时间：
2017-03-01
期刊：
影响因子：
5.8
通讯作者：
Zhou, Yaoqi
中科院分区：
文献类型：
--
作者：
Hanson, Jack;Yang, Yuedong;Zhou, Yaoqi
动机：捕捉蛋白质结构而不是序列邻居之间的远程相互作用是生物信息学中一个长期具有挑战性的问题。最近，长短期记忆(LSTM)网络通过在长序列事件中记忆有用的过去信息，显著提高了语音和图像分类问题的准确性。结果：与传统的基于窗口的神经网络(SPIN-D)相比，这种新的方法在所有测试数据集上都有稳步的改进，不需要对短无序区域和长无序区域进行单独的训练。对其他四个数据集的独立测试，包括来自结构预测关键评估(CASP)技术的数据集和来自MobiDB的>；10000注释蛋白质集，证实Spot-Disorder是最好的无序预测方法之一。此外，初步研究表明，该方法在预测无序区域的功能位点方面更准确。这些结果突出了将LSTM和深度双向递归神经网络相结合在捕捉生物信息学应用中的非局部、远程交互方面的有效性。
Motivation: Capturing long-range interactions between structural but not sequence neighbors of proteins is a long-standing challenging problem in bioinformatics. Recently, long short-term memory (LSTM) networks have significantly improved the accuracy of speech and image classification problems by remembering useful past information in long sequential events. Here, we have implemented deep bidirectional LSTM recurrent neural networks in the problem of protein intrinsic disorder prediction.Results: The new method, named SPOT-Disorder, has steadily improved over a similar method using a traditional, window-based neural network (SPINE-D) in all datasets tested without separate training on short and long disordered regions. Independent tests on four other datasets including the datasets from critical assessment of structure prediction (CASP) techniques and > 10 000 annotated proteins from MobiDB, confirmed SPOT-Disorder as one of the best methods in disorder prediction. Moreover, initial studies indicate that the method is more accurate in predicting functional sites in disordered regions. These results highlight the usefulness combining LSTM with deep bidirectional recurrent neural networks in capturing non-local, long-range interactions for bioinformatics applications.