Protein function prediction: towards integration of similarity metrics

被引：33

作者：

Erdin, Serkan ^{[1
]}

Lisewski, Andreas Martin ^{[1
]}

Lichtarge, Olivier ^{[1
]}

机构：

[1] Baylor Coll Med, Dept Mol & Human Genet, Houston, TX 77030 USA

来源：

CURRENT OPINION IN STRUCTURAL BIOLOGY | 2011年 / 21卷 / 02期

基金：

美国国家科学基金会;

关键词：

EVOLUTIONARY IMPORTANCE; STRUCTURAL GENOMICS; GENE ONTOLOGY; DATABASE; ANNOTATION; BINDING; CLASSIFICATION; DISCOVERY; ENZYMES; SERVER;

D O I：

10.1016/j.sbi.2011.02.001

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

Genomic centers discover increasingly many protein sequences and structures, but not necessarily their full biological functions. Thus, currently, less than one percent of proteins have experimentally verified biochemical activities. To fill this gap, function prediction algorithms apply metrics of similarity between proteins on the premise that those sufficiently alike in sequence, or structure, will perform identical functions. Although high sensitivity is elusive, network analyses that integrate these metrics together hold the promise of rapid gains in function prediction specificity.

引用

页码：180 / 188

页数：9

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