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Protein Interaction Network Analysis

Protein interaction networkWe have a growing interest in protein interaction network analyses. As a result of ongoing collaborations with other principal investigators at the Stowers Institute, we analyze a large amount of diverse affinity purifications from organisms like S. cerevisiae and human tissue culture. We used a portion of this data to develop a novel approach for assembling probabilistic local protein interaction networks using vector algebra and statistical methods, and applied this to the human Tip49a/Tip49b protein interaction network (Sardiu et al., 2008). We defined four protein complexes, URI/Prefoldin, hINO80, SRCAP, and TRRAP/TIP60 and we identified new components of these complexes. Most importantly, we determined the probabilities of protein-protein interactions within and between complexes. We used NSAF values to determine the probability of each protein protein interaction in the dataset. In a limited follow up analysis, higher probabilities corresponded to positive coIPs and low probabilities corresponded to negative coIPs (Sardiu et al., 2008). These results raise the intriguing possibility that the probabilities that are calculated from this technology may provide insight into the architectural organization of a protein complex. We have further analyzed the Tip49a/Tip49b dataset to evaluate different clustering algorithms to gain insight into the potential value of different clustering approaches for future computational assembly of unknown protein complexes (Sardiu et al., 2009). We are currently developing a large dataset protein protein interactions with a large number of affinity purifications of proteins involved in transcription. Future studies will be to assemble and analyze these datasets into probabilistic protein interaction networks.

Banks CA, Lee ZT, Boanca G, Lakshminarasimhan M, Groppe BD, Wen Z, Hattem GL, Seidel CW, Florens L, Washburn MP. (2014) Controlling for gene expression changes in transcription factor protein networks. Mol Cell Proteomics. Jun;13(6):1510-22. doi: 10.1074/mcp.M113.033902. Epub 2014 Apr 10. Abstract

Sardiu M, Washburn M. (2011) Building Protein-Protein Interaction Networks with Proteomic and Informatics Tools. J Biol Chem, 286(27):23645-51. Abstract

Ramisetty SR, Washburn MP. (2011) Unraveling the dynamics of protein interactions with quantitative mass spectrometry. Crit Rev Biochem Mol Biol, 46(3):216-28. Abstract

Sardiu ME, Gilmore JM, Carrozza MJ, Li B, Workman JL, Florens L, Washburn MP. (2009) Determining protein complex connectivity using a probabilistic deletion network derived from quantitative proteomics. PloS ONE, 4(10):e7310. Abstract

Sardiu ME, Florens L, Washburn MP. (2009) Evaluation of Clustering Algorithms for Protein Complex and Protein Interaction Network Assembly. J. Proteome Res., 8(6):2944-52. Abstract

Sardiu ME, Cai Y, Jin J, Swanson SK, Conaway RC, Conaway JW, Florens L, Washburn MP. (2008) Probabilistic Assembly of Human Protein Interaction Networks from Label Free Quantitative Proteomics. Proc. Natl. Acad. Sci. USA, 105(5):1454-9. Abstract