基于间隔二肽组分和递归特征消除法的DNA结合蛋白的鉴定  

Identification of DNA-binding Proteins Using Gapped-dipeptide Composition and Recursive Feature Elimination Algorithm

作　　者：汤亚东 刘潇 刘太岗[2] 谢鹭[3] 陈兰明[1] TANG Ya-Dong;LIU Xiao;LIU Tai-Gang;XIE Lu;CHEN Lan-Ming(Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation（Shanghai）, Ministry of Agriculture, College of Food Science and Technology, Shanghai 201306, China;College of Information Technology, Shanghai Ocean University, Shanghai 201306, China;Shanghai Center for Bioinformation Technology, Shanghai 201203, China)

机构地区：[1]农业部水产品贮藏保鲜质量安全风险评估重点实验室(上海),上海海洋大学食品科学与技术学院,上海201306 [2]上海海洋大学信息学院,上海201306 [3]上海生物信息技术研究中心,上海201203

出　　处：《生物化学与生物物理进展》2018年第4期453-459,共7页Progress In Biochemistry and Biophysics

基　　金：国家自然科学基金(31671946,11601324); 上海市科委基金(17050502200)资助项目

摘　　要：DNA结合蛋白(DNA-binding proteins,DBPs)的鉴定在原核和真核生物的基因和蛋白质功能注释研究中具有十分重要的意义.本研究首次运用间隔二肽组分(gapped-dipeptide composition,Gap DPC)结合递归特征消除法(recursive feature elimination,RFE)鉴定DBPs.首先获得待测蛋白质氨基酸序列的位置特异性得分矩阵(position specific scoring matrix,PSSM),在此基础上提取蛋白质的Gap DPC特征,通过RFE法选择最优特征,然后利用支持向量机(support vector machine,SVM)作为分类器,在蛋白质序列数据集PDB396和LB1068中进行夹克刀交叉验证(jackknife cross validation test).研究结果显示,基于PDB396和LB1068数据集,DBPs预测的准确率、Matthews相关系数、敏感性和特异性分别达到93.43%、0.86、89.04%和96.00%,以及86.33%、0.73、86.49%和86.18%,明显优于文献报道中的相关方法,为DBPs的鉴定提供了新的模型.The identification of DNA-binding proteins（DBPs） plays an important role in functional annotation of genes and proteins of prokaryote and eukaryote organisms. This study, for the first time, combined the gapped-dipeptide composition（Gap DPC） and recursive feature elimination（RFE） to identify DBPs. The position specific scoring matrix（PSSM） of each tested amino acid sequence was obtained. Based on the PSSM, their Gap DPC features of the amino acid sequences were extracted, and then the optimal features were selected using the RFE method. Subsequently, the support vector machine（SVM） was chosen as a classifier and the datasets PDB396 and LB1068 were tested using the jackknife cross validation test. The result showed that the values of accuracy, Matthews correlation coefficient, sensitivity, and specificity for the identification of DBPs were 93.43%,0.86, 89.04% and 96%, and 86.33%, 0.73, 86.49% and 86.18% for the datasets PDB396 and LB1068, respectively,which were obviously superior to the methods reported previously in the literature. The new model established in this study improved the identification methods of DBPs.

关 键 词：DNA结合蛋白 间隔二肽组分 位置特异性得分矩阵 递归特征消除法 支持向量机分类器 

分 类 号：Q71[生物学—分子生物学] Q81