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. 2009 May 17:10:150.
doi: 10.1186/1471-2105-10-150.

Protein-protein interaction based on pairwise similarity

Nazar Zaki  1 , Sanja Lazarova-Molnar, Wassim El-Hajj, Piers Campbell
Affiliations

Protein-protein interaction based on pairwise similarity

Nazar Zaki et al. BMC Bioinformatics. .

Abstract

Background: Protein-protein interaction (PPI) is essential to most biological processes. Abnormal interactions may have implications in a number of neurological syndromes. Given that the association and dissociation of protein molecules is crucial, computational tools capable of effectively identifying PPI are desirable. In this paper, we propose a simple yet effective method to detect PPI based on pairwise similarity and using only the primary structure of the protein. The PPI based on Pairwise Similarity (PPI-PS) method consists of a representation of each protein sequence by a vector of pairwise similarities against large subsequences of amino acids created by a shifting window which passes over concatenated protein training sequences. Each coordinate of this vector is typically the E-value of the Smith-Waterman score. These vectors are then used to compute the kernel matrix which will be exploited in conjunction with support vector machines.

Results: To assess the ability of the proposed method to recognize the difference between "interacted" and "non-interacted" proteins pairs, we applied it on different datasets from the available yeast saccharomyces cerevisiae protein interaction. The proposed method achieved reasonable improvement over the existing state-of-the-art methods for PPI prediction.

Conclusion: Pairwise similarity score provides a relevant measure of similarity between protein sequences. This similarity incorporates biological knowledge about proteins and it is extremely powerful when combined with support vector machine to predict PPI.

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Figures

Figure 1
Figure 1
Similarity score of each protein sequence in the testing dataset against the three generated subsequences.
Figure 2
Figure 2
Comparing PPI-PS performance with MLE and Domain-based random forest of decision trees methods.
Figure 3
Figure 3
Illustration of the feature extraction algorithm.
Figure 4
Figure 4
Overview of the feature extraction step.
See this image and copyright information in PMC

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