Predicting protein-protein interactions via multivariate mutual information of protein sequences

BMC Bioinformatics

Research Article

Yijie Ding¹  Fei Guo¹  Jijun Tang² 

[1] School of Computer Science and Technology, Tianjin University, No.135, Yaguan Road, Tianjin Haihe Education Park, Tianjin, People’s Republic of China;School of Computer Science and Technology, Tianjin University, No.135, Yaguan Road, Tianjin Haihe Education Park, Tianjin, People’s Republic of China;Department of Computer Science and Engineering, University of South Carolina, Columbia, USA;

关键词: Protein-protein interactions;    Protein sequence;    Feature extraction;    Conjoint amino acids;    Multivariate mutual information;   

DOI  :  10.1186/s12859-016-1253-9

 received in 2016-01-21, accepted in 2016-09-08,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundProtein-protein interactions (PPIs) are central to a lot of biological processes. Many algorithms and methods have been developed to predict PPIs and protein interaction networks. However, the application of most existing methods is limited since they are difficult to compute and rely on a large number of homologous proteins and interaction marks of protein partners. In this paper, we propose a novel sequence-based approach with multivariate mutual information (MMI) of protein feature representation, for predicting PPIs via Random Forest (RF).MethodsOur method constructs a 638-dimentional vector to represent each pair of proteins. First, we cluster twenty standard amino acids into seven function groups and transform protein sequences into encoding sequences. Then, we use a novel multivariate mutual information feature representation scheme, combined with normalized Moreau-Broto Autocorrelation, to extract features from protein sequence information. Finally, we feed the feature vectors into a Random Forest model to distinguish interaction pairs from non-interaction pairs.ResultsTo evaluate the performance of our new method, we conduct several comprehensive tests for predicting PPIs. Experiments show that our method achieves better results than other outstanding methods for sequence-based PPIs prediction. Our method is applied to the S.cerevisiae PPIs dataset, and achieves 95.01 % accuracy and 92.67 % sensitivity repectively. For the H.pylori PPIs dataset, our method achieves 87.59 % accuracy and 86.81 % sensitivity respectively. In addition, we test our method on other three important PPIs networks: the one-core network, the multiple-core network, and the crossover network.ConclusionsCompared to the Conjoint Triad method, accuracies of our method are increased by 6.25,2.06 and 18.75 %, respectively. Our proposed method is a useful tool for future proteomics studies.

【 授权许可】

CC BY   
© The Author(s) 2016