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Rid R, Herzog J, Maier RH, Hundsberger H, Eger A, Hintner H, Bauer JW, Onder K. Real-time monitoring of relative peptide-protein interaction strengths in the yeast two-hybrid system. Assay Drug Dev Technol 2013; 11:269-75. [PMID: 23679850 DOI: 10.1089/adt.2012.496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The yeast two-hybrid (Y2H) system is one of the most technically straightforward, effective, and widely used tools for the discovery of the binary peptide or protein interactions. However, its exceptional detection sensitivity poses a serious challenge for affinity ranking and hence prioritizing the resultant large number of putative interactors for follow-up analyses. To overcome this apparent bottleneck, we describe here a novel yeast growth curve-based interaction-monitoring approach that permits semiautomatic quantification, comparison, and statistically ascertained scoring of a large collection of Y2H interactions under real-time conditions. Initially, we conducted a proof-of-concept test of five literature-validated peptide-protein interactions with known affinities in the low μM range, and subsequently used the method to classify 88 novel vitamin D receptor-binding peptides derived from high-throughput screening of a highly diverse artificial peptide aptamer library. Based on our in-depth data evaluation, we conclude that real-time monitoring of clone growth as a measure of relative binding strength offers a facile, cost-effective, accurate, reproducible, and further adaptable complement to standard Y2H-derived clone management.
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Affiliation(s)
- Raphaela Rid
- Division of Molecular Dermatology, Department of Dermatology, Paracelsus Private Medical University Salzburg, Salzburg, Austria
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Roberts GG, Parrish JR, Mangiola BA, Finley RL. High-throughput yeast two-hybrid screening. Methods Mol Biol 2012; 812:39-61. [PMID: 22218853 DOI: 10.1007/978-1-61779-455-1_3] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Charting the interactions among proteins is essential for understanding biological processes. While a number of complementary technologies for detecting protein interactions are available, the yeast two-hybrid system is one of the few that have been successfully scaled up. Two-hybrid screens have been used to construct extensive protein interaction maps for humans and several model organisms, and these maps have proven invaluable for studies on a variety of biological systems. These maps, however, have not come close to covering all proteins or interactions detectable by yeast two-hybrid. This is due in part to the difficulty of using library screening methods to sample all possible binary combinations of proteins. Ideally, every binary pair of proteins would be tested individually to ensure that every detectable interaction is identified. For organisms with large proteomes, however, this is not economically feasible and instead efficient pooling schemes must be implemented. The high-throughput two-hybrid screening methods presented here are designed to efficiently maximize coverage for selected sets of proteins or entire proteomes. We present two high-throughput screening protocols. Both methods are designed to identify interactors for any number of bait proteins expressed as DNA-binding domain (BD) fusions. The choice of which protocol to use depends largely on the nature of the available library of proteins fused to an activation domain (AD). The first protocol is appropriate for screening a library of AD clones, such as a cDNA library, a domain library, or a large pool of AD clones. By contrast, the second protocol is appropriate for screening a large array of individual sequence-verified AD clones. This protocol screens small pools of AD clones from the array in a two-phase scheme. Although the methods presented were developed using the LexA version of the yeast two-hybrid system, we include notes as appropriate to accommodate users of other versions.
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Affiliation(s)
- George G Roberts
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI, USA
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Dittmar JC, Reid RJ, Rothstein R. ScreenMill: a freely available software suite for growth measurement, analysis and visualization of high-throughput screen data. BMC Bioinformatics 2010; 11:353. [PMID: 20584323 PMCID: PMC2909220 DOI: 10.1186/1471-2105-11-353] [Citation(s) in RCA: 65] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2010] [Accepted: 06/28/2010] [Indexed: 11/29/2022] Open
Abstract
Background Many high-throughput genomic experiments, such as Synthetic Genetic Array and yeast two-hybrid, use colony growth on solid media as a screen metric. These experiments routinely generate over 100,000 data points, making data analysis a time consuming and painstaking process. Here we describe ScreenMill, a new software suite that automates image analysis and simplifies data review and analysis for high-throughput biological experiments. Results The ScreenMill, software suite includes three software tools or "engines": an open source Colony Measurement Engine (CM Engine) to quantitate colony growth data from plate images, a web-based Data Review Engine (DR Engine) to validate and analyze quantitative screen data, and a web-based Statistics Visualization Engine (SV Engine) to visualize screen data with statistical information overlaid. The methods and software described here can be applied to any screen in which growth is measured by colony size. In addition, the DR Engine and SV Engine can be used to visualize and analyze other types of quantitative high-throughput data. Conclusions ScreenMill automates quantification, analysis and visualization of high-throughput screen data. The algorithms implemented in ScreenMill are transparent allowing users to be confident about the results ScreenMill produces. Taken together, the tools of ScreenMill offer biologists a simple and flexible way of analyzing their data, without requiring programming skills.
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Affiliation(s)
- John C Dittmar
- Columbia University Medical Center, Dept, Genetics & Development, 701 West 168th Street, New York, NY 10032-2704, USA
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Parrish JR, Yu J, Liu G, Hines JA, Chan JE, Mangiola BA, Zhang H, Pacifico S, Fotouhi F, DiRita VJ, Ideker T, Andrews P, Finley RL. A proteome-wide protein interaction map for Campylobacter jejuni. Genome Biol 2008; 8:R130. [PMID: 17615063 PMCID: PMC2323224 DOI: 10.1186/gb-2007-8-7-r130] [Citation(s) in RCA: 170] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2007] [Revised: 05/14/2007] [Accepted: 07/05/2007] [Indexed: 11/12/2022] Open
Abstract
'Systematic identification of protein interactions for the bacterium Campylobacter jejuni using high-throughput yeast two-hybrid screens detected interactions for 80% of the organism's proteins. Background Data from large-scale protein interaction screens for humans and model eukaryotes have been invaluable for developing systems-level models of biological processes. Despite this value, only a limited amount of interaction data is available for prokaryotes. Here we report the systematic identification of protein interactions for the bacterium Campylobacter jejuni, a food-borne pathogen and a major cause of gastroenteritis worldwide. Results Using high-throughput yeast two-hybrid screens we detected and reproduced 11,687 interactions. The resulting interaction map includes 80% of the predicted C. jejuni NCTC11168 proteins and places a large number of poorly characterized proteins into networks that provide initial clues about their functions. We used the map to identify a number of conserved subnetworks by comparison to protein networks from Escherichia coli and Saccharomyces cerevisiae. We also demonstrate the value of the interactome data for mapping biological pathways by identifying the C. jejuni chemotaxis pathway. Finally, the interaction map also includes a large subnetwork of putative essential genes that may be used to identify potential new antimicrobial drug targets for C. jejuni and related organisms. Conclusion The C. jejuni protein interaction map is one of the most comprehensive yet determined for a free-living organism and nearly doubles the binary interactions available for the prokaryotic kingdom. This high level of coverage facilitates pathway mapping and function prediction for a large number of C. jejuni proteins as well as orthologous proteins from other organisms. The broad coverage also facilitates cross-species comparisons for the identification of evolutionarily conserved subnetworks of protein interactions.
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Affiliation(s)
- Jodi R Parrish
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI, USA 48201
| | - Jingkai Yu
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI, USA 48201
| | - Guozhen Liu
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI, USA 48201
| | - Julie A Hines
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI, USA 48201
| | - Jason E Chan
- Department of Bioengineering and Program in Bioinformatics, University of California at San Diego, San Diego, CA, USA 92093
| | - Bernie A Mangiola
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI, USA 48201
| | - Huamei Zhang
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI, USA 48201
| | - Svetlana Pacifico
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI, USA 48201
| | - Farshad Fotouhi
- Department of Computer Science, Wayne State University, Detroit, MI, USA 48201
| | - Victor J DiRita
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, USA 48109
| | - Trey Ideker
- Department of Bioengineering and Program in Bioinformatics, University of California at San Diego, San Diego, CA, USA 92093
| | - Phillip Andrews
- Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, MI, USA 48109
| | - Russell L Finley
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI, USA 48201
- Department of Biochemistry and Molecular Biology, Wayne State University School of Medicine, Detroit, MI, USA 48201
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