Here we are proposing to extend our ongoing project on the development of computational methods and tools for analyzing and modeling protein-protein interaction (PPI) data using affinity-purification mass spectrometry technology (AP-MS). The recent technological advances have made AP/MS a widely used technique for studying PPIs in vivo. At the same time, the development of computational methods and tools for these data has lagged behind. There is a great need for accurate and robust methods for statistical assessment of PPI data. Furthermore, AP/MS data is presently used to reconstruct protein networks that are purely qualitative in nature. Such important questions as partition of proteins into multiple complexes and changes in the complex composition upon perturbation in the cellular environment remain to be fully addressed. As a part of our ongoing R01 project, we are working to add a new dimension to MS-based analysis of PPI networks and complexes by taking advantage of the quantitative information encoded in MS data. At the moment, we are focusing on commonly used label-free quantification strategies such as spectral counting and peptide parent ion intensities. Using these data, we have already demonstrated that our methods enable more accurate reconstruction of protein complexes and interaction networks. We have also developed and continue improving a statistical framework, Significance Analysis of Interactome (SAINT), which utilizes spectral counts or peptide intensities for assigning a confidence measure to individual interactions. SAINT and other tools we are developing for statistical analysis and data mining of PPI data are already being used by an increasing number of laboratories worldwide. Very recently, however, several new strategies have emerged that are all based on the concept of targeted MS-based protein quantification. In particular, the new MS approach called SWATH-MS offers an exciting opportunity for developing a much faster, more accurate, and highly sensitive technology for monitoring PPIs. Coupling SWATH-MS approach with affinity purification, AP/SWATH-MS, should enable us to determine - with very high accuracy - the relative abundance of every identified interacting protein in a given AP/MS dataset. This, in term, will create a technological platform for the analysis of PPIs and networks in a dynamic fashion. It will also improve our ability to monitor PPIs involving specific forms of proteins, including post-translationally modified (e.g. phosphorylated) proteins. Following the overall theme of our ongoing research collaboration, the AP/SWATH-MS approach will be optimized and evaluated in the context of several biological studies linked through their significance for fundamental understanding of cell signaling. The ultimate goal of our collaborative research is to enable generation of high quality PPI networks and complexes using AP/MS and their subsequent biological interpretation. Extending this work to include the emerging SWATH-MS technology has a potential to significantly improve the accuracy and throughput of PPI network reconstruction, and should enable effective monitoring of changes in the interaction networks in a dynamic fashion.

Public Health Relevance

This is a proposal to extend our on-going project on the development of computational methods for analyzing and modeling protein interaction data. We will optimize and apply the new AP/SWATH-MS technology for accurate and highly sensitive monitoring of dynamic changes in the interactomes in response to changes in the cellular environment. The tools and methods developed as a part of this work should be of great utility for both large collaborative interactome projects and small scale studies.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZGM1-CBB-0 (MI))
Program Officer
Brazhnik, Paul
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Michigan Ann Arbor
Schools of Medicine
Ann Arbor
United States
Zip Code
Navarro, Pedro; Kuharev, Jörg; Gillet, Ludovic C et al. (2016) A multicenter study benchmarks software tools for label-free proteome quantification. Nat Biotechnol 34:1130-1136
Kochen, Michael A; Chambers, Matthew C; Holman, Jay D et al. (2016) Greazy: Open-Source Software for Automated Phospholipid Tandem Mass Spectrometry Identification. Anal Chem 88:5733-41
Zhang, Hui; Gayen, Srimonta; Xiong, Jie et al. (2016) MLL1 Inhibition Reprograms Epiblast Stem Cells to Naive Pluripotency. Cell Stem Cell 18:481-94
Liu, Guomin; Knight, James D R; Zhang, Jian Ping et al. (2016) Data Independent Acquisition analysis in ProHits 4.0. J Proteomics 149:64-68
Malik, Rohit; Khan, Amjad P; Asangani, Irfan A et al. (2015) Targeting the MLL complex in castration-resistant prostate cancer. Nat Med 21:344-52
Omenn, Gilbert S; Lane, Lydie; Lundberg, Emma K et al. (2015) Metrics for the Human Proteome Project 2015: Progress on the Human Proteome and Guidelines for High-Confidence Protein Identification. J Proteome Res 14:3452-60
Kitata, Reta Birhanu; Dimayacyac-Esleta, Baby Rorielyn T; Choong, Wai-Kok et al. (2015) Mining Missing Membrane Proteins by High-pH Reverse-Phase StageTip Fractionation and Multiple Reaction Monitoring Mass Spectrometry. J Proteome Res 14:3658-69
Shively, Christian A; Kweon, Hye Kyong; Norman, Kaitlyn L et al. (2015) Large-Scale Analysis of Kinase Signaling in Yeast Pseudohyphal Development Identifies Regulation of Ribonucleoprotein Granules. PLoS Genet 11:e1005564
Tsou, Chih-Chiang; Avtonomov, Dmitry; Larsen, Brett et al. (2015) DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics. Nat Methods 12:258-64, 7 p following 264
Fermin, Damian; Avtonomov, Dmitry; Choi, Hyungwon et al. (2015) LuciPHOr2: site localization of generic post-translational modifications from tandem mass spectrometry data. Bioinformatics 31:1141-3

Showing the most recent 10 out of 54 publications