Identifying protein complexes in AP-MS data with negative evidence via soft Markov clustering

Authors:
Yu-Keng Shih;Srinivasan Parthasarathy
Affiliations:
Dept. of Computer Science and Engineering, The Ohio State University;Dept. of Computer Science and Engineering, The Ohio State University
Venue:
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Year:
2013

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Abstract

Protein complexes are key units to discover protein mechanism. Traditional protein complex identification methods adopt a soft (overlapping) network clustering algorithm on protein-protein interaction network and predict the clusters as protein complexes. Recently, the AP-MS technique and the scoring method can measure the co-complex relationship among proteins. Unlike traditional PPI networks, AP-MS can provide negative evidence which indicates which proteins are unlikely to be in the same protein complex. However, most of existing network clustering algorithms cannot utilize this negative similarity score. In this paper, we propose a soft network clustering algorithm, SR-MCL-N, which can take into account negative similarity scores. SR-MCL-N is a variation of a previous algorithm, SR-MCL, which is a network clustering algorithm based on the transition flow. Additionally, since the scoring approach we use produces a dense similarity matrix, a sparsification technique is adopted on the similarity matrix. Based on the gold standard CYC2008 and GO terms, we first show that the sparsification can not only speed up SR-MCL-N, but also let SR-MCL-N generate more accurate clusters. SR-MCL-N is then compared against SR-MCL and a hierarchical algorithm which also considers negative similarity score. The results indicate that our algorithm outperforms others since SR-MCL-N not only generates overlapped clusters but also additionally takes negative similarity score into account.