Prediction of RNA-binding sites from evolutionary information of protein sequences

摘要：

Protein-RNA interactions play significant roles in a number of biological activities, such as protein synthesis, regulation of gene expression. A reliable identification of RNA-binding sites in proteins is important to understand the molecular details of protein-RNA interaction. In this work, we have developed a machine learning approach, support vector machine (SVM), to predict RNA-binding sites in proteins based on the profile of evolutionary conservation of sequence positions, which only needs protein primary sequence as input of classifier. Using evolutionary information in terms of a position specific scoring matrix (PSSM) of each residue and 6 of its closest neighboring residues, our results indicated that RNA-binding residue can be predicted at 67.6％ sensitivity, 75.0％ specificity and a net prediction (an average of sensitivity and specificity) of 71.3％. This method outperforms previous protein RNA-binding site prediction methods.

关键词： Protein RNA-binding site Position specific scoring matrix Evolutionary conservation Support vector machine

作者: Jing Tong Peng Jiang Zu-hong Lu

作者单位: State Key Laboratory of BioelectronicsDepartment of Biological Science and Medical Engineering,Southeast University,Nanjing, China,210096

会议类型: 国际会议

会议名称: The 5th International Forum on Post-genome Technologies(5IFPT)(第五届国际后基因组生命科学技术学术论坛)

会议地点: 苏州

会议语种:英文

在线出版日期: 2007-09-10（万方平台首次上网日期，不代表论文的发表时间）