Position-specific residue preference features around the ends of helices and strands and a novel strategy for the prediction of secondary structures
Position-specific residue preference features around the ends of helices and strands and a novel strategy for the prediction of secondary structures
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螺旋和链末端周围的位置特异性残基偏好特征以及预测二级结构的新策略
DOI：
10.1110/ps.035691.108
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发表时间：
2008-09-01
期刊：
影响因子：
8
通讯作者：
Zhou, Yanhong
中科院分区：
文献类型：
--
作者：
Duan, Mojie;Huang, Min;Zhou, Yanhong
自从揭示螺旋末端周围的位置特异性残基偏好以来，已经有很多年了。然而，现有的二级结构预测方法都没有利用这种偏好特征，导致二级结构末端预测的准确性较低。在本研究中，我们从PDB中收集了由1860个高分辨率非同源蛋白组成的相对较大的数据集，并进一步分析了规则二级结构末端周围的残基分布。研究发现，不仅螺旋末端存在位置特异性残基偏好（PSRP），链末端也存在位置特异性残基偏好（PSRP）。基于这些独特的特征，我们提出了一种新颖的策略并开发了一种名为E-SSpred的工具，该工具将二级结构视为一个整体，并通过集成相关特征建立模型来直接预测整个二级结构片段。在E-SSpred中，采用支持向量机（SVM）方法根据螺旋和链周围独特的残基分布来建模和预测螺旋和链的末端。通过整合二级结构的末端预测结果、三肽组成和长度分布特征以及最著名的程序 PSIPRED 的预测结果，应用简单的线性判别分析方法对整个二级结构片段进行建模和预测。在广泛使用的数据集上进行五重交叉验证的结果表明，E-SSpred预测二级结构末端的准确度比PSIPRED高约10%，并且E-SSpred的整体预测准确度（Q(3)值）（82.2%）也优于PSIPRED（80.3%）。 E-SSpred Web 服务器位于 http://bioinfo.hust.edu.cn/bio/tools/E-SSpred/index.html。
It has been many years since position-specific residue preference around the ends of a helix was revealed. However, all the existing secondary structure prediction methods did not exploit this preference feature, resulting in low accuracy in predicting the ends of secondary structures. In this study, we collected a relatively large data set consisting of 1860 high-resolution, non-homology proteins from the PDB, and further analyzed the residue distributions around the ends of regular secondary structures. It was found that there exist position-specific residue preferences (PSRP) around the ends of not only helices but also strands. Based on the unique features, we proposed a novel strategy and developed a tool named E-SSpred that treats the secondary structure as a whole and builds models to predict entire secondary structure segments directly by integrating relevant features. In E-SSpred, the support vector machine (SVM) method is adopted to model and predict the ends of helices and strands according to the unique residue distributions around them. A simple linear discriminate analysis method is applied to model and predict entire secondary structure segments by integrating end-prediction results, tri-peptide composition, and length distribution features of secondary structures, as well as the prediction results of the most famous program PSIPRED. The results of fivefold cross-validation on a widely used data set demonstrate that the accuracy of E-SSpred in predicting ends of secondary structures is about 10% higher than PSIPRED, and the overall prediction accuracy (Q(3) value) of E-SSpred (82.2%) is also better than PSIPRED (80.3%). The E-SSpred web server is available at http://bioinfo.hust.edu.cn/bio/tools/E-SSpred/index.html.