Exploiting the past and the future in protein secondary structure prediction
Exploiting the past and the future in protein secondary structure prediction
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DOI：
10.1093/bioinformatics/15.11.937
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发表时间：
1999-11-01
期刊：
影响因子：
5.8
通讯作者：
Pollastri, G
中科院分区：
文献类型：
--
作者：
Baldi, P;Brunak, S;Pollastri, G
动机：预测蛋白质的二级结构（α -螺旋，β -片，线圈）是阐明其三维结构及其功能的重要一步。目前，最好的预测器是基于机器学习方法，特别是具有固定且相对较短的以预测位点为中心的氨基酸输入窗口的神经网络架构。虽然固定的小窗口避免了过度拟合问题，但它不允许捕获可变的远程信息。结果：我们引入了一系列新的架构，它们可以学习基于依赖关系的可变范围进行预测。这些架构扩展了循环神经网络，引入了非因果双向动态来捕获上游和下游信息。预测算法是通过使用混合的估计器来完成的，这些估计器利用了在输入和输出水平上以多种排列方式表示的进化信息。虽然我们的系统目前实现了接近76%的预测正确率，至少与现有最好的系统相当，但这里的主要重点是开发新的算法思想。可用性：预测蛋白质二级结构的可执行程序可从作者那里免费获得。联系方式：pfbaldi@ics.uci.edu, gpollast@ics.uci.edu, brunak@cbs.dtu.dk, paolo@dsi.unifi.it。
Motivation: Predicting the secondary structure of a protein (alpha-helix, beta-sheet, coil) is an important step towards elucidating its three-dimensional structure, as well as its function. Presently, the best predictors are based on machine learning approaches, in particular neural network architectures with a fixed and relatively short, input window of amino acids, centered at the prediction site. Although a fixed small window avoids overfitting problems, it does not permit capturing variable long-rang information.Results: We introduce a family of novel architectures which can learn to make predictions based on variable ranges of dependencies. These architectures extend recurrent neural networks, introducing non-causal bidirectional dynamics to capture both upstream and downstream information. The prediction algorithm is completed by the use of mixtures of estimators that leverage evolutionary information, expressed in terms of multiple alignments, both at the input and output levels. While our system currently achieves an overall performance close to 76% correct prediction - at least comparable to the best existing systems - the main emphasis here is on the development of new algorithmic ideas.Availability: The executable program for predicting protein secondary structure is available from the authors free of charge.Contact: pfbaldi@ics.uci.edu, gpollast@ics.uci.edu, brunak@cbs.dtu.dk, paolo@dsi.unifi.it.