PRED-PPI: A server for predicting protein-protein interactions based on sequence data with probability assignment

被引：45

作者：

Guo Y. ^{[1
]}

Li M. ^{[1
]}

Pu X. ^{[1
]}

Li G. ^{[1
]}

Guang X. ^{[1
]}

Xiong W. ^{[1
]}

Li J. ^{[1
]}

机构：

[1] College of Chemistry, Sichuan University

来源：

BMC Research Notes | / 3卷 / 1期

基金：

高等学校博士学科点专项科研基金; 中国国家自然科学基金;

关键词：

Support Vector Machine; Probability Threshold; Probability Assignment; Query Protein; Interaction Probability;

D O I：

10.1186/1756-0500-3-145

中图分类号：

学科分类号：

摘要：

Background: Protein-protein interactions (PPIs) are crucial for almost all cellular processes, including metabolic cycles, DNA transcription and replication, and signaling cascades. Given the importance of PPIs, several methods have been developed to detect them. Since the experimental methods are time-consuming and expensive, developing computational methods for effectively identifying PPIs is of great practical significance. Findings: Most previous methods were developed for predicting PPIs in only one species, and do not account for probability estimations. In this work, a relatively comprehensive prediction system was developed, based on a support vector machine (SVM), for predicting PPIs in five organisms, specifically humans, yeast, Drosophila, Escherichia coli, and Caenorhabditis elegans. This PPI predictor includes the probability of its prediction in the output, so it can be used to assess the confidence of each SVM prediction by the probability assignment. Using a probability of 0.5 as the threshold for assigning class labels, the method had an average accuracy for detecting protein interactions of 90.67% for humans, 88.99% for yeast, 90.09% for Drosophila, 92.73% for E. coli, and 97.51% for C. elegans. Moreover, among the correctly predicted pairs, more than 80% were predicted with a high probability of 0.8, indicating that this tool could predict novel PPIs with high confidence. Conclusions. Based on this work, a web-based system, Pred-PPI, was constructed for predicting PPIs from the five organisms. Users can predict novel PPIs and obtain a probability value about the prediction using this tool. Pred-PPI is freely available at http://cic.scu.edu.cn/bioinformatics/predict-ppi/default.html. © 2010 Li et al; licensee BioMed Central Ltd.

引用

共 13 条

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