A structural alignment kernel for protein structures
A structural alignment kernel for protein structures
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DOI：
10.1093/bioinformatics/btl642
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发表时间：
2007-05-01
期刊：
影响因子：
5.8
通讯作者：
Noble, William Stafford
中科院分区：
文献类型：
--
作者：
Qiu, Jian;Hue, Martial;Noble, William Stafford
动机：这项工作旨在开发计算方法以自动方式注释蛋白质结构。我们采用支持向量机（SVM）分类器将给定的结构类别映射到其相应的结构（SCOP）或功能（基因本体）注释。特别是，我们以最近描述蛋白质结构的各种内核的工作为基础，其中内核是分类器用来比较结构对的相似性函数。结果：我们描述了一个以直接方式从现有结构比对程序 MAMMOTH 派生的内核。我们在基准实验中发现，该内核的性能显着优于各种其他内核，包括之前描述的几个内核。此外，在这两个基准测试中，单独使用 MAMMOTH 对结构进行分类的效果不如使用带有 MAMMOTH 内核的 SVM。
Motivation: This work aims to develop computational methods to annotate protein structures in an automated fashion. We employ a support vector machine (SVM) classifier to map from a given class of structures to their corresponding structural (SCOP) or functional (Gene Ontology) annotation. In particular, we build upon recent work describing various kernels for protein structures, where a kernel is a similarity function that the classifier uses to compare pairs of structures.Results: We describe a kernel that is derived in a straightforward fashion from an existing structural alignment program, MAMMOTH. We find in our benchmark experiments that this kernel significantly out-performs a variety of other kernels, including several previously described kernels. Furthermore, in both benchmarks, classifying structures using MAMMOTH alone does not work as well as using an SVM with the MAMMOTH kernel.