Protein-protein interaction based on pairwise similarity

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