基于Blast-GO的蛋白质亚线粒体定位预测  

Predicting Proteins Submitochondria Locations Using Blast-GO

作　　者：曩毅 梅含雪 赵燕[1] 侯宝妍 赵志远[1] 樊国梁[1] 

机构地区：[1]内蒙古大学物理科学与技术学院,呼和浩特010021

出　　处：《生物化学与生物物理进展》2015年第12期1136-1143,共8页Progress In Biochemistry and Biophysics

基　　金：国家自然科学基金(61461038);内蒙古自治区自然科学基金(2013MS0504);内蒙古自治区高等学校科学研究项目(NJZY13014);内蒙古大学高层次人才引进科研项目(135147);内蒙古大学大学生创新创业训练计划项目(201412155)资助~~

摘　　要：本文建立了一个最新的蛋白质亚线粒体定位数据集,包含4个亚线粒体定位的1 293条序列,结合基因本体(GO)信息和同源信息对线粒体蛋白质进行特征提取,利用支持向量机算法建立分类器,经Jackknife检验,对于4个亚线粒体位置的总体预测准确率为93.27%,其中3个亚线粒体位置的总体预测准确率为94.73%.In this study, a novel protein submitochondia locations dataset was constructed which contained 1 293 proteins classified into four kinds of submitochondria locations. The GO information and homologous information was extracted to combine the feature vectors of proteins and the Supported Vector Machine algorithm was used to construct the classifier. As a result, by using the Jackknife Cross-Validation, an accuracy of 93.27% for four kinds of protein submitochondria locations and that of 94.73% for three kinds of protein submitochondria locations was obtained. Especially, the predictive accuracy for outer membrane of protein submitochondia locations was enhanced than previous methods. The data set of protein submitochondia locations constructed by ours has the intermembrane proteins compared to old ones. The intermembrane proteins have important functions in protein apoptosis. The integrity of data set and the improvement of prediction accuracy can help to understand the cell activity and internal biochemical process.

关 键 词：亚线粒体定位 基因本体 同源信息 支持向量机 

分 类 号：Q61[生物学—生物物理学]