Abstract
BackgroundRecent advances in molecular biology have led to the accumulation of large amounts of data on protein-protein interaction networks in different species. An important challenge for the analysis of these data is to extract functional modules such as protein complexes and biological processes from networks which are characterised by the present of a significant number of false positives. Various computational techniques have been applied in recent years. However, most of them treat protein interaction as binary. Co-complex relations derived from affinity purification/mass spectrometry (AP-MS) experiments have been largely ignored.MethodsThis paper presents a new algorithm for detecting protein complexes from AP-MS data. The algorithm intends to detect groups of prey proteins that are significantly co-associated with the same set of bait proteins. We first construct AP-MS data as a bipartite network, where one set of nodes consists of bait proteins and the other set is composed of prey proteins. We then calculate pair-wise similarities of bait proteins based on the number of their commonly shared neighbours. A hierarchical clustering algorithm is employed to cluster bait proteins based on the similarities and thus a set of 'seed' clusters is obtained. Starting from these 'seed' clusters, an expansion process is developed to identify prey proteins which are significantly associated with the same set of bait proteins. Then, a set of complete protein complexes is derived. In application to two real AP-MS datasets, we validate biological significance of predicted protein complexes by using curated protein complexes and well-characterized cellular component annotation from Gene Ontology (GO). Several statistical metrics have been applied for evaluation.ResultsExperimental results show that, the proposed algorithm achieves significant improvement in detecting protein complexes from AP-MS data. In comparison to the well-known MCL algorithm, our algorithm improves the accuracy rate by about 20% in detecting protein complexes in both networks and increases the F-Measure value by about 50% in Krogan_2006 network. Greater precision and better accuracy have been achieved and the identified complexes are demonstrated to match well with existing curated protein complexes.ConclusionsOur study highlights the significance of taking co-complex relations into account when extracting protein complexes from AP-MS data. The algorithm proposed in this paper can be easily extended to the analysis of other biological networks which can be conveniently represented by bipartite graphs such as drug-target networks.
Highlights
Recent advances in molecular biology have led to the accumulation of large amounts of data on protein-protein interaction networks in different species
Research on Protein-protein interactions (PPIs) in biology and medicine has shown that a protein complex is a typical pattern existing in PPI networks in which a group of proteins interact with each other to play a biological function in a cell, such as anaphase-promoting complex and protein export and transport complexes [7], or bind each other in a series of time in a biological process such as the yeast pheromone response pathway and Mitogenactivated protein (MAP) signalling cascades [7]
To fairly evaluate performance of different methods, we only considered the set of benchmark complexes that contain at least 2 proteins which are in PPI networks
Summary
Recent advances in molecular biology have led to the accumulation of large amounts of data on protein-protein interaction networks in different species. As advance in high throughput experimental methods and computational approaches, such as Yeast two-hybrid (Y2H) screening [2,3] and Affinity purification/ mass spectrometry (AP-MS) [4,5,6], large genome-scale protein interactions have been detected, resulting in increasing size of PPI networks. In 2006, Brohée and Helden [17] evaluated four clustering algorithms for their ability to detect protein complexes, and results highlighted that MCL was remarkably robust to graph alterations. Another well-known clustering algorithm is CFinder [11]. It exploits the topological feature of the network by using the direct link between a pair of nodes
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