基于功能域组分的蛋白质折叠类型识别  

Protein Fold Recognition by Functional Domain Composition

作　　者：闫金丽[1] 陈治伟[1] 徐海松[1] 李晓琴[1] 

机构地区：[1]北京工业大学生命科学与生物工程学院,北京100124

出　　处：《生物化学与生物物理进展》2011年第2期166-172,共7页Progress In Biochemistry and Biophysics

基　　金：国家自然科学基金(30570427);北京市自然科学基金(4092008)资助项目~~

摘　　要：蛋白质空间结构研究是分子生物学、细胞生物学、生物化学以及药物设计等领域的重要课题.折叠类型反映了蛋白质核心结构的拓扑模式,对折叠类型的识别是蛋白质序列与结构关系研究的重要内容.选取LIFCA数据库中样本量较大的53种折叠类型,应用功能域组分方法进行折叠识别.将Astral 1.65中序列一致性小于95%的样本作为检验集,全库检验结果中平均敏感性为96.42%,特异性为99.91%,马修相关系数(MCC)为0.91,各项统计结果表明:功能域组分方法可以很好地应用在蛋白质折叠识别中,LIFCA相对简单的分类规则可以很好地集中蛋白质的大部分功能特性,反映了结构与功能的对应关系.Research of protein 3D structures plays a key role in molecular biology, cell biology, biomedicine, and drug design. The protein fold type reflects the topological pattern of the structure＇s core. Fold recognition is an important method in protein sequence-structure research. On the 53 fold types which have more than 10 samples in LIFCA were selected. The functional domain composition is introduced to predict the fold types of a protein or a domain. After testing 9 211 proteins with less than 95% sequence identity from the Astral 1.65 database, the average sensitivity, specificity and Matthew＇s correlation coefficient （MCC） of the 53 fold types were found to be 96.42%, 99.91% and 0.91, respectively. The result indicates that using the functional domain composition to represent a protein is very promising for protein fold recognition. And though based on simple classification rules, LIFCA can concentrate the functional features of proteins, reflecting the corresponding relation between structure and function.

关 键 词：折叠类型 折叠识别 功能域 LIFCA数据库 

分 类 号：Q615[生物学—生物物理学] Q518