Combining pairwise-sequence similarity and support vector machines for detecting remote protein evolutionary and structural relationships
Combining pairwise-sequence similarity and support vector machines for detecting remote protein evolutionary and structural relationships
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DOI：
10.1089/106652703322756113
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发表时间：
2003-01-01
影响因子：
1.7
通讯作者：
Noble, WS
中科院分区：
文献类型：
--
作者：
Liao, L;Noble, WS
理解细胞分子机制的一个关键因素是理解基因组中编码的每种蛋白质的结构和功能。一种非常成功的推断先前未注释的蛋白质的结构或功能的方法是通过与一种或多种结构或功能已知的蛋白质的序列相似性。为此，我们提出了一种方法，代表蛋白质成对序列相似性得分。这种表示，结合被称为支持向量机（SVM）的判别分类算法，提供了一个强大的手段来检测蛋白质之间的微妙的结构和进化关系。该算法，称为SVM成对，当测试其识别以前看不见的家庭从SCOP数据库的能力，产生显着更好的性能比SVM-Fisher，配置文件的障碍，和PSI-BLAST。
One key element in understanding the molecular machinery of the cell is to understand the structure and function of each protein encoded in the genome. A very successful means of inferring the structure or function of a previously unannotated protein is via sequence similarity with one or more proteins whose structure or function is already known. Toward this end, we propose a means of representing proteins using pairwise sequence similarity scores. This representation, combined with a discriminative classification algorithm known as the support vector machine (SVM), provides a powerful means of detecting subtle structural and evolutionary relationships among proteins. The algorithm, called SVM-pairwise, when tested on its ability to recognize previously unseen families from the SCOP database, yields significantly better performance than SVM-Fisher, profile HMMs, and PSI-BLAST.