One of the best strategies to understand how a particular protein drives a biological process is to first find its interacting proteins. Current methods to identify the repertoire of protein interactors suffer from multiple limitations. Most methods to identify protein-protein interactions are biased in favor of high affinity interactions between abundant partners. They also lack an easy way to compare the interactome between two different proteins or versions of a particular protein. The goal here is to further develop a new method, termed DEEPN (Dynamic Enrichment for Evaluation of Protein Networks) as a means to define stable and transient conformationally sensitive binding interactions. We believe that DEEPN circumvents many of the shortcomings associated with other methods. DEEPN is innovative in that it utilizes next-generation, high- throughput DNA sequencing approaches to quantitatively follow the evolution of a population of plasmids that encode interacting partners in a yeast 2-hybrid (Y2H) assay format. Our preliminary studies with DEEPN showed it to be robust and information rich when applied to finding factors that interact with particular proteins f interest. An important strength of DEEPN is that it can discover differential interactomes, that being the repertoire of proteins that bind one conformation of a protein vs another. We propose to develop DEEPN into an exportable workflow that can be used by novice labs needing a method for comprehensive identification of low- and high-affinity interactions. DEEPN would be a boon for any research program focused on finding out how activated proteins (e.g. Kinases, GTPases, phosphorylated effectors) differentially interact with downstream protein partners. It would also help researchers determine the biochemical basis of disease- causing point mutations, with the rationale that some of these types of changes might change the repertoire of protein interactions (or the strength of those interactions). Ultimately, DEEPN could address the pressing need for a comprehensive technique to find specific protein interactions that is not only inexpensive but also customizable, adaptable, and accessible to RO1-driven labs.

Public Health Relevance

Identifying protein interaction networks is critical to progress in basic and biomedical research in molecular cell biology. We have developed DEEPN as an improved method for identifying sets of binding partners to a protein of interest and alleviate shortcomings of previously used approaches. We propose to develop DEEPN into a readily accessible discover platform that can be used by wide range of independent investigators.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Exploratory/Developmental Grants (R21)
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Membrane Biology and Protein Processing (MBPP)
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Lash, Tiffani Bailey
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University of Iowa
Schools of Medicine
Iowa City
United States
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Krishnamani, Venkatramanan; Peterson, Tabitha A; Piper, Robert C et al. (2018) Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens. J Vis Exp :
Peterson, Tabitha A; Stamnes, Mark A; Piper, Robert C (2018) A Yeast 2-Hybrid Screen in Batch to Compare Protein Interactions. J Vis Exp :