HSEpred: predict half-sphere exposure from protein sequences
HSEpred: predict half-sphere exposure from protein sequences
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DOI：
10.1093/bioinformatics/btn222
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发表时间：
2008-07-01
期刊：
影响因子：
5.8
通讯作者：
Akutsu, Tatsuya
中科院分区：
文献类型：
--
作者：
Song, Jiangning;Tan, Hao;Akutsu, Tatsuya
动机：半球形暴露（HSE）是一个新开发的二维溶剂暴露措施。通过概念上将蛋白质结构中的氨基酸球体分成两个半球，这两个半球代表其在向上和向下方向上的不同空间邻域，HSE-up和HSE-down度量与其他度量（如可及表面积、残基深度和接触数）相比显示出上级性能。然而，目前还没有现有的方法来预测HSE措施从sequence data.Results：在这篇文章中，我们提出了一种新的方法来预测HSE措施和推断残基接触数使用预测的HSE值，基于一个精心准备的非同源蛋白质结构数据集。特别是，我们采用支持向量回归（SVR）来量化HSE指标与蛋白质序列之间的关系并评估其预测性能。我们广泛地探讨了五个序列编码方案，以研究它们对预测性能的影响。我们的方法可以实现预测和观测的HSE上升和HSE下降措施之间的相关系数分别为0.72和0.68。此外，通过将预测的HSE-up和HSE-down值相加，可以准确地预测接触次数，进一步扩大了该方法的应用范围。支持向量回归方法在本研究中的成功应用表明，它在定量蛋白质序列-结构关系和预测蛋白质序列的结构特性方面将更加有用。
Motivation: Half-sphere exposure (HSE) is a newly developed two-dimensional solvent exposure measure. By conceptually separating an amino acids sphere in a protein structure into two half spheres which represent its distinct spatial neighborhoods in the upward and downward directions, the HSE-up and HSE-down measures show superior performance compared with other measures such as accessible surface area, residue depth and contact number. However, currently there is no existing method for the prediction of HSE measures from sequence data.Results: In this article, we propose a novel approach to predict the HSE measures and infer residue contact numbers using the predicted HSE values, based on a well-prepared non-homologous protein structure dataset. In particular, we employ support vector regression (SVR) to quantify the relationship between HSE measures and protein sequences and evaluate its prediction performance. We extensively explore five sequence-encoding schemes to examine their effects on the prediction performance. Our method could achieve the correlation coefficients of 0.72 and 0.68 between the predicted and observed HSE-up and HSE-down measures, respectively. Moreover, contact number can be accurately predicted by the summation of the predicted HSE-up and HSE-down values, which has further enlarged the application of this method. The successful application of SVR approach in this study suggests that it should be more useful in quantifying the protein sequencestructure relationship and predicting the structural property profiles from protein sequences.