Recovering Protein-Protein and Domain-Domain Interactions from Aggregation of IP-MS Proteomics of Coregulator Complexes

Coregulator proteins (CoRegs) are part of multi-protein complexes that transiently assemble with transcription factors and chromatin modifiers to regulate gene expression. In this study we analyzed data from 3,290 immuno-precipitations (IP) followed by mass spectrometry (MS) applied to human cell lines aimed at identifying CoRegs complexes. Using the semi-quantitative spectral counts, we scored binary protein-protein and domain-domain associations with several equations. Unlike previous applications, our methods scored prey-prey protein-protein interactions regardless of the baits used. We also predicted domain-domain interactions underlying predicted protein-protein interactions. The quality of predicted protein-protein and domain-domain interactions was evaluated using known binary interactions from the literature, whereas one protein-protein interaction, between STRN and CTTNBP2NL, was validated experimentally; and one domain-domain interaction, between the HEAT domain of PPP2R1A and the Pkinase domain of STK25, was validated using molecular docking simulations. The scoring schemes presented here recovered known, and predicted many new, complexes, protein-protein, and domain-domain interactions. The networks that resulted from the predictions are provided as a web-based interactive application at http://maayanlab.net/HT-IP-MS-2-PPI-DDI/. Author Summary: In response to various extracellular stimuli, protein complexes are transiently assembled within the nucleus of cells to regulate gene transcription in a context dependent manner. Here we analyzed data from 3,290 proteomics experiments that used as bait different member proteins from regulatory complexes with different antibodies. Such proteomics experiments attempt to characterize complex membership for other proteins that associate with bait proteins. However, the experiments are noisy and aggregation of the data from many pull-down experiments is computationally challenging. To this end we developed and evaluated several equations that score pair-wise interactions based on co-occurrence in different but related pull-down experiments. We compared and evaluated the scoring methods and combined them to recover known, and discover new, complexes and protein-protein interactions. We also applied the same equations to predict domain-domain interactions that might underlie the protein interactions and complex formation. As a proof of concept, we experimentally validated one predicted protein-protein interaction and one predicted domain-domain interaction using different methods. Such rich information about binary interactions between proteins and domains should advance our knowledge of transcriptional regulation by CoRegs in normal and diseased human cells.