Predicting protein-protein interactions based only on sequences information

Protein-protein interactions (PPIs) are central to most biological processes. Although efforts have been devoted to the development of methodology for predicting PPIs and protein interaction networks, the application of most existing methods is limited because they need information about protein hom...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2007-03, Vol.104 (11), p.4337-4341
Main Authors: Shen, Juwen, Zhang, Jian, Luo, Xiaomin, Zhu, Weiliang, Yu, Kunqian, Chen, Kaixian, Li, Yixue, Jiang, Hualiang
Format: Article
Language:English
Subjects:
Algorithms
Amino acids
Amino Acids - chemistry
Base Sequence
Bioinformatics
Biological Sciences
Biophysics
Computational Biology - methods
Crossovers
Databases, Protein
Datasets
Drug interactions
Humans
Kernel functions
Mathematical vectors
Methods
Modeling
Programming Languages
Protein Interaction Mapping - methods
Proteins
Proteomics - methods
Reproducibility of Results
Sensitivity and Specificity
Sequence Analysis, Protein
Software
Vector spaces
Yeasts
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Summary:Protein-protein interactions (PPIs) are central to most biological processes. Although efforts have been devoted to the development of methodology for predicting PPIs and protein interaction networks, the application of most existing methods is limited because they need information about protein homology or the interaction marks of the protein partners. In the present work, we propose a method for PPI prediction using only the information of protein sequences. This method was developed based on a learning algorithm-support vector machine combined with a kernel function and a conjoint triad feature for describing amino acids. More than 16,000 diverse PPI pairs were used to construct the universal model. The prediction ability of our approach is better than that of other sequence-based PPI prediction methods because it is able to predict PPI networks. Different types of PPI networks have been effectively mapped with our method, suggesting that, even with only sequence information, this method could be applied to the exploration of networks for any newly discovered protein with unknown biological relativity. In addition, such supplementary experimental information can enhance the prediction ability of the method.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.0607879104