蛋白质功能预测：走向相似性度量的整合。
Protein function prediction: towards integration of similarity metrics.
机构信息
Department of Molecular and Human Genetics, 1 Baylor Plaza, Baylor College of Medicine, Houston, TX 77030, USA.
出版信息
Curr Opin Struct Biol. 2011 Apr;21(2):180-8. doi: 10.1016/j.sbi.2011.02.001. Epub 2011 Feb 24.
Abstract
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.
摘要
基因组中心发现了越来越多的蛋白质序列和结构，但不一定能完全确定其生物学功能。因此，目前只有不到百分之一的蛋白质具有经过实验验证的生化活性。为了填补这一空白，功能预测算法应用了蛋白质之间相似性的度量标准，前提是那些在序列或结构上足够相似的蛋白质将执行相同的功能。尽管高灵敏度难以实现，但整合这些度量标准的网络分析有望在功能预测特异性方面取得快速进展。
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