Dataset Information

A comparison and optimization of yeast two-hybrid systems.

ABSTRACT: Two-hybrid (Y2H) assays are available in a variety of different versions, including bacterial, yeast, and mammalian systems. However, even when done exclusively in yeast, multiple different host strains, vectors, reporter genes, or protocols can be used. Here we systematically compare protein-protein interactions (PPIs) from several previously published Y2H datasets. PPIs of a human gold-standard dataset were generated by Y2H assays as well as other methods such as LUMIER or protein fragment complementation assays (PCAs). Different Y2H methods detect substantially different subsets of these PPIs, even when protocols are standardized. In order to maximize the number of interactions found and to minimize the number of false positive interactions we recommend to combine multiple vectors and protocols. While the combined results of all 18 methods detected about 92% of a gold-standard interaction set, a combination of just three Y2H assays detected up to 78% of these protein pairs, or up to 83% when a fourth assay was included. These findings indicate that three or four separate assays may be sufficient to detect the majority of protein-protein interactions in many systems.

SUBMITTER: Caufield JH

PROVIDER: S-EPMC5737776 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A comparison and optimization of yeast two-hybrid systems.

Caufield J H JH Sakhawalkar Neha N Uetz Peter P

Methods (San Diego, Calif.) 20121208 4

Two-hybrid (Y2H) assays are available in a variety of different versions, including bacterial, yeast, and mammalian systems. However, even when done exclusively in yeast, multiple different host strains, vectors, reporter genes, or protocols can be used. Here we systematically compare protein-protein interactions (PPIs) from several previously published Y2H datasets. PPIs of a human gold-standard dataset were generated by Y2H assays as well as other methods such as LUMIER or protein fragment com ...[more]

PMID: 23231818

Similar Datasets

Project description:BackgroundThe importance and power of isotope-based metabolic flux analysis and its contribution to understanding the metabolic network is increasingly recognized. Its application is, however, still limited partly due to computational inefficiency. 13C metabolic flux analysis aims to compute in vivo metabolic fluxes in terms of metabolite balancing extended by carbon isotopomer balances and involves a nonlinear least-squares problem. To solve the problem more efficiently, improved numerical optimization techniques are necessary.ResultsFor flux computation, we developed a gradient-based hybrid optimization algorithm. Here, independent flux variables were compactified into [0, 1)-ranged variables using a single transformation rule. The compactified parameters could be discriminated between non-identifiable and identifiable variables after model linearization. The developed hybrid algorithm was applied to the central metabolism of Bacillus subtilis with only succinate and glutamate as carbon sources. This creates difficulties caused by symmetry of succinate leading to limited introduction of 13C labeling information into the system. The algorithm was found to be superior to its parent algorithms and to global optimization methods both in accuracy and speed. The hybrid optimization with tolerance adjustment quickly converged to the minimum with close to zero deviation and exactly re-estimated flux variables. In the metabolic network studied, some fluxes were found to be either non-identifiable or nonlinearly correlated. The non-identifiable fluxes could correctly be predicted a priori using the model identification method applied, whereas the nonlinear flux correlation was revealed only by identification runs using different starting values a posteriori.ConclusionThis fast, robust and accurate optimization method is useful for high-throughput metabolic flux analysis, a posteriori identification of possible parameter correlations, and also for Monte Carlo simulations to obtain statistical qualities for flux estimates. In this way, it contributes to future quantitative studies of central metabolic networks in the framework of systems biology.

Dataset Information

A comparison and optimization of yeast two-hybrid systems.

Publications

A comparison and optimization of yeast two-hybrid systems.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets