Discover protein complexes in protein-protein interaction networks using parametric local modularity.
BACKGROUND
Recent advances in proteomic technologies have enabled us to create detailed protein-protein interaction maps in multiple species and in both normal and diseased cells. As the size of the interaction dataset increases, powerful computational methods are required in order to effectively distil network models from large-scale interactome data.


RESULTS
We present an algorithm, miPALM (Module Inference by Parametric Local Modularity), to infer protein complexes in a protein-protein interaction network. The algorithm uses a novel graph theoretic measure, parametric local modularity, to identify highly connected sub-networks as candidate protein complexes. Using gold standard sets of protein complexes and protein function and localization annotations, we show our algorithm achieved an overall improvement over previous algorithms in terms of precision, recall, and biological relevance of the predicted complexes. We applied our algorithm to predict and characterize a set of 138 novel protein complexes in S. cerevisiae.


CONCLUSIONS
miPALM is a novel algorithm for detecting protein complexes from large protein-protein interaction networks with improved accuracy than previous methods. The software is implemented in Matlab and is freely available at http://www.medicine.uiowa.edu/Labs/tan/software.html.
Department of Internal Medicine, The University of Iowa, 2294 CBRB, Iowa City, IA 52242, USA.
Jongkwang
J
Kim
Kai
K
Tan
D000465:Algorithms; D046912:Multiprotein Complexes / Q000737:chemistry* / Q000378:metabolism*; D025941:Protein Interaction Mapping / Q000379:methods*; D040901:Proteomics / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry / Q000378:metabolism
D046912:Multiprotein Complexes; D029701:Saccharomyces cerevisiae Proteins