H-DROP: an SVM based helical domain linker predictor trained with features optimized by combining random forest and stepwise selection
H-DROP: an SVM based helical domain linker predictor trained with features optimized by combining random forest and stepwise selection
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DOI：
10.1007/s10822-014-9763-x
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发表时间：
2014-08-01
影响因子：
3.5
通讯作者：
Kuroda，Yutaka
中科院分区：
文献类型：
--
作者：
Ebina，Teppei;Suzuki，Ryosuke;Kuroda，Yutaka
结构域连接子预测是蛋白质组学研究的热点之一，它可以帮助识别适合于高通量蛋白质组学分析的新结构域。在这里，我们报告H-DROP，一个基于SVM的螺旋结构域连接器pRediction使用OPTIMAL功能。据我们所知，H-DROP是特异性和有效地鉴定螺旋接头的第一个预测因子。这之所以成为可能，首先是因为IS-Dom提供了一个大型的训练数据集，其次是因为我们从大量的潜在特征中选择了少量的最佳特征。训练的螺旋接头数据集，其中包括261个螺旋接头，通过检测在我们以前报道的IS-Dom数据集中列出的两个独立的结构域的边界区域的螺旋残基构建。通过随机森林从3，000个特征中选择45个最佳特征候选，通过逐步选择将其进一步减少到26个最佳特征。H-DROP的预测灵敏度和准确度分别为35.2%和38.8%。这些值比对照方法的值高出10.7%以上，所述对照方法包括我们先前开发的DROP（其是卷曲接头预测物）和PPRODO（其用未分化的结构域边界序列训练）。总体而言，这些结果表明，螺旋接头可以通过使用严格策划的螺旋接头训练数据集和精心选择的最佳特征集，仅从序列信息预测。 H-DROP可在 http://domserv.lab.tuat.ac.jp
Domain linker prediction is attracting much interest as it can help identifying novel domains suitable for high throughput proteomics analysis. Here, we report H-DROP, an SVM-based Helical Domain linker pRediction using OPtimal features. H-DROP is, to the best of our knowledge, the first predictor for specifically and effectively identifying helical linkers. This was made possible first because a large training dataset became available from IS-Dom, and second because we selected a small number of optimal features from a huge number of potential ones. The training helical linker dataset, which included 261 helical linkers, was constructed by detecting helical residues at the boundary regions of two independent structural domains listed in our previously reported IS-Dom dataset. 45 optimal feature candidates were selected from 3,000 features by random forest, which were further reduced to 26 optimal features by stepwise selection. The prediction sensitivity and precision of H-DROP were 35.2 and 38.8 %, respectively. These values were over 10.7 % higher than those of control methods including our previously developed DROP, which is a coil linker predictor, and PPRODO, which is trained with un-differentiated domain boundary sequences. Overall, these results indicated that helical linkers can be predicted from sequence information alone by using a strictly curated training data set for helical linkers and carefully selected set of optimal features. H-DROP is available at http://domserv.lab.tuat.ac.jp