EL_PSSM-RT: DNA-binding residue prediction by integrating ensemble learning with PSSM Relation Transformation

BMC Bioinformatics

Research Article

Qin Lu¹  Hongpeng Wang²  Jiyun Zhou³  Ruifeng Xu⁴  Yulan He⁵ 

[1] Department of Computing, the Hong Kong Polytechnic University, Kowloon, Hong Kong;School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, 518055, Shenzhen, Guangdong, China;School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, 518055, Shenzhen, Guangdong, China;Department of Computing, the Hong Kong Polytechnic University, Kowloon, Hong Kong;School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, 518055, Shenzhen, Guangdong, China;Shenzhen Engineering Laboratory of Performance Robots at Digital Stage, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China;School of Engineering and Applied Science, Aston University, Birmingham, UK;

关键词: DNA-protein interaction;    DNA-binding residue;    PSSM;    Ensemble learning;    SVM;    Random forest;    Relation transformation;   

DOI  :  10.1186/s12859-017-1792-8

 received in 2017-03-07, accepted in 2017-08-15,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundPrediction of DNA-binding residue is important for understanding the protein-DNA recognition mechanism. Many computational methods have been proposed for the prediction, but most of them do not consider the relationships of evolutionary information between residues.ResultsIn this paper, we first propose a novel residue encoding method, referred to as the Position Specific Score Matrix (PSSM) Relation Transformation (PSSM-RT), to encode residues by utilizing the relationships of evolutionary information between residues. PDNA-62 and PDNA-224 are used to evaluate PSSM-RT and two existing PSSM encoding methods by five-fold cross-validation. Performance evaluations indicate that PSSM-RT is more effective than previous methods. This validates the point that the relationship of evolutionary information between residues is indeed useful in DNA-binding residue prediction. An ensemble learning classifier (EL_PSSM-RT) is also proposed by combining ensemble learning model and PSSM-RT to better handle the imbalance between binding and non-binding residues in datasets. EL_PSSM-RT is evaluated by five-fold cross-validation using PDNA-62 and PDNA-224 as well as two independent datasets TS-72 and TS-61. Performance comparisons with existing predictors on the four datasets demonstrate that EL_PSSM-RT is the best-performing method among all the predicting methods with improvement between 0.02–0.07 for MCC, 4.18–21.47% for ST and 0.013–0.131 for AUC. Furthermore, we analyze the importance of the pair-relationships extracted by PSSM-RT and the results validates the usefulness of PSSM-RT for encoding DNA-binding residues.ConclusionsWe propose a novel prediction method for the prediction of DNA-binding residue with the inclusion of relationship of evolutionary information and ensemble learning. Performance evaluation shows that the relationship of evolutionary information between residues is indeed useful in DNA-binding residue prediction and ensemble learning can be used to address the data imbalance issue between binding and non-binding residues. A web service of EL_PSSM-RT (http://hlt.hitsz.edu.cn:8080/PSSM-RT_SVM/) is provided for free access to the biological research community.

【 授权许可】

CC BY   
© The Author(s). 2017