Prediction of problematic complexes from PPI networks: sparse, embedded, and small complexes.

backgroundThe prediction of protein complexes from high-throughput protein-protein interaction (PPI) data remains an important challenge in bioinformatics. Three groups of complexes have been identified as problematic to discover. First, many complexes are sparsely connected in the PPI network, and do not form dense clusters that can be derived by clustering algorithms. Second, many complexes are embedded within highly-connected regions of the PPI network, which makes it difficult to accurately delimit their boundaries. Third, many complexes are small (composed of two or three distinct proteins), so that traditional topological markers such as density are ineffective.

resultsWe have previously proposed three approaches to address these challenges. First, Supervised Weighting of Composite Networks (SWC) integrates diverse data sources with supervised weighting, and successfully fills in missing co-complex edges in sparse complexes to allow them to be predicted. Second, network decomposition (DECOMP) splits the PPI network into spatially- and temporally-coherent subnetworks, allowing complexes embedded within highly-connected regions to be more clearly demarcated. Finally, Size-Specific Supervised Weighting (SSS) integrates diverse data sources with supervised learning to weight edges in a size-specific manner-of being in a small complex versus a large complex-and improves the prediction of small complexes. Here we integrate these three approaches into a single system. We test the integrated approach on the prediction of yeast and human complexes, and show that it outperforms SWC, DECOMP, or SSS when run individually, achieving the highest precision and recall levels.

conclusionThree groups of protein complexes remain challenging to predict from PPI data: sparse complexes, embedded complexes, and small complexes. Our previous approaches have addressed each of these challenges individually, through data integration, PPI-network decomposition, and supervised learning. Here we integrate these approaches into a single complex-discovery system, which improves the prediction of all three types of challenging complexes. With our approach, protein complexes can be more accurately and comprehensively predicted, allowing a clearer elucidation of the modular machinery of the cell.