Incorporating semantic similarity into clustering process for identifying protein complexes from affinity purification/mass spectrometry data

This paper presents a framework for incorporating semantic similarities in the detection of protein complexes from Affinity Purification/Mass Spectrometry (AP-MS) data. AP-MS data is modeled as a bipartite network, where one set of nodes consist of bait proteins and the other set are prey proteins. Pair-wise similarities of bait proteins are computed by combining similarities based on topological features and functional semantic similarities. A hierarchical clustering algorithm is then applied to obtain 'seed clusters' consisting of bait proteins. Starting from these 'seed' clusters, an expansion process is developed to recruit prey proteins which are significantly associated with bait proteins, to produce final sets of identified protein complexes. In the application to real AP-MS datasets, we validate biological significance of predicted protein complexes by using curated protein complexes. Six statistical metrics have been applied. Results show that by integrating semantic similarities into the clustering process, the accuracy of identifying complexes has been greatly improved. Meanwhile, clustering results obtained by the proposed framework are better than those from several existent clustering methods.