Deconvolution of targeted protein-protein interaction maps.
Current proteomic techniques allow researchers to analyze chosen biological pathways or an ensemble of related protein complexes at a global level via the measure of physical protein-protein interactions by affinity purification mass spectrometry (AP-MS). Such experiments yield information-rich but complex interaction maps whose unbiased interpretation is challenging. Guided by current knowledge on the modular structure of protein complexes, we propose a novel statistical approach, named BI-MAP, complemented by software tools and a visual grammar to present the inferred modules. We show that the BI-MAP tools can be applied from small and very detailed maps to large, sparse, and much noisier data sets. The BI-MAP tool implementation and test data are made freely available.
CeMM-Center for Molecular Medicine of the Austrian Academy of Sciences, AKH-BT 25.3, Lazarettgasse 14, A-1090 Vienna, Austria.
Alexey
A
Stukalov
Giulio
G
Superti-Furga
Jacques
J
Colinge
D000465:Algorithms; D001343:Autophagy; D001499:Bayes Theorem; D016000:Cluster Analysis; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D008954:Models, Biological; D009010:Monte Carlo Method; D046912:Multiprotein Complexes / Q000502:physiology; D011485:Protein Binding; D060066:Protein Interaction Maps; D040901:Proteomics; D012984:Software
D046912:Multiprotein Complexes