We have developed a simple and totally in vitro selection procedure based on cell-free cotranslation using a highly stable and efficient in vitro virus (IVV). Cell-free cotranslation of tagged bait and prey proteins is advantageous for the formation of protein complexes and allows high-throughput analysis of protein-protein interactions (PPI) as a result of providing in vitro instead of in vivo preparation of bait proteins. The use of plural selection rounds and a two-step purification of the IVV selection, followed by in vitro post-selection, is advantageous for decreasing false positives. In a single experiment using bait Fos, more than 10 interactors, including not only direct, but also indirect interactions, were enriched. Further, previously unidentified proteins containing novel leucine zipper (L-ZIP) motifs with minimal binding sites identified by sequence alignment as functional elements were detected as a result of using a randomly primed cDNA library. Thus, we consider that this simple IVV selection system based on cell-free cotranslation could be applicable to high-throughput and comprehensive analysis of PPI and complexes in large-scale settings involving parallel bait proteins.

For high-throughput in vitro protein selection using genotype (mRNA)-phenotype (protein) fusion formation and C-terminal protein labeling as a post-selection analysis, it is important to improve the stability and efficiency of mRNA templates for both technologies. Here we describe an efficient single-strand ligation (90% of the input mRNAs) using a fluorescein-conjugated polyethylene glycol puromycin (Fluor-PEG Puro) spacer. This ligation provides a stable c-jun mRNA with a flexible Fluor-PEG Puro spacer for efficient fusion formation (70% of the input mRNA with the PEG spacer) in a cell-free wheat germ translation system. When using a 5' untranslated region including SP6 promoter and Omega29 enhancer (a part of tobacco mosaic virus Omega), an A(8) sequence (eight consecutive adenylate residues) at the 3' end is suitable for fusion formation, while an XA(8) sequence (XhoI and the A(8) sequence) is suitable for C-terminal protein labeling. Further, we report that Fluor-PEG N-t-butyloxycarbonylpuromycin [Puro(Boc)] spacer enhances the stability and efficiency of c-jun mRNA template for C-terminal protein labeling. These mRNA templates should be useful for puromycin-based technologies (fusion formation and C-terminal protein labeling) to facilitate high-throughput in vitro protein selection for not only evolutionary protein engineering, but also proteome exploration.

Although yeast two-hybrid assay and biochemical methods combined with mass spectrometry have been successfully employed for the analyses of protein-protein interactions in the field of proteomics, these methods encounter various difficulties arising from the usage of living cells, including inability to analyze toxic proteins and restriction of testable interaction conditions. Totally in vitro display technologies such as ribosome display and mRNA display are expected to circumvent these difficulties. In this study, we applied an mRNA display technique to screening for interactions of a basic leucine zipper domain of Jun protein in a mouse brain cDNA library. By performing iterative affinity selection and sequence analyses, we selected 16 novel Jun-associated protein candidates in addition to four known interactors. By means of real-time PCR and pull-down assay, 10 of the 16 newly discovered candidates were confirmed to be direct interactors with Jun in vitro. Furthermore, interaction of 6 of the 10 proteins with Jun was observed in cultured cells by means of co-immunoprecipitation and observation of subcellular localization. These results demonstrate that this in vitro display technology is effective for the discovery of novel protein-protein interactions and can contribute to the comprehensive mapping of protein-protein interactions.

Large-scale data sets of protein-protein interactions (PPIs) are a valuable resource for mapping and analysis of the topological and dynamic features of interactome networks. The currently available large-scale PPI data sets only contain information on interaction partners. The data presented in this study also include the sequences involved in the interactions (i.e., the interacting regions, IRs) suggested to correspond to functional and structural domains. Here we present the first large-scale IR data set obtained using mRNA display for 50 human transcription factors (TFs), including 12 transcription-related proteins. The core data set (966 IRs; 943 PPIs) displays a verification rate of 70%. Analysis of the IR data set revealed the existence of IRs that interact with multiple partners. Furthermore, these IRs were preferentially associated with intrinsic disorder. This finding supports the hypothesis that intrinsically disordered regions play a major role in the dynamics and diversity of TF networks through their ability to structurally adapt to and bind with multiple partners. Accordingly, this domain-based interaction resource represents an important step in refining protein interactions and networks at the domain level and in associating network analysis with biological structure and function.

BackgroundHigh-throughput methods for detecting protein-protein interactions enable us to obtain large interaction networks, and also allow us to computationally identify the associations of proteins as protein complexes. Although there are methods to extract protein complexes as sets of proteins from interaction networks, the extracted complexes may include false positives because they do not account for the structural limitations of the proteins and thus do not check that the proteins in the extracted complex can simultaneously bind to each other. In addition, there have been few searches for deeper insights into the protein complexes, such as of the topology of the protein-protein interactions or into the domain-domain interactions that mediate the protein interactions.ResultsHere, we introduce a combinatorial approach for prediction of protein complexes focusing not only on determining member proteins in complexes but also on the DDI/PPI organization of the complexes. Our method analyzes complex candidates predicted by the existing methods. It searches for optimal combinations of domain-domain interactions in the candidates based on an assumption that the proteins in a candidate can form a true protein complex if each of the domains is used by a single protein interaction. This optimization problem was mathematically formulated and solved using binary integer linear programming. By using publicly available sets of yeast protein-protein interactions and domain-domain interactions, we succeeded in extracting protein complex candidates with an accuracy that is twice the average accuracy of the existing methods, MCL, MCODE, or clustering coefficient. Although the configuring parameters for each algorithm resulted in slightly improved precisions, our method always showed better precision for most values of the parameters.ConclusionsOur combinatorial approach can provide better accuracy for prediction of protein complexes and also enables to identify both direct PPIs and DDIs that mediate them in complexes.