Masamichi Ishizaka

A Comprehensive Resource of Interacting Protein Regions for Refining Human Transcription Factor Networks

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.

Protein complex prediction via verifying and reconstructing the topology of domain-domain interactions

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.

Efficiency of puromycin-based technologies mediated by release factors and a ribosome recycling factor

Two puromycin-based techniques, in vitro virus (IVV) and C-terminal labelling of proteins, were developed based on the observation that puromycin binds the C-terminus of a protein. Puromycin technology is a useful tool for the detection of proteins and analysis of protein–protein interactions (PPIs); however, problems arise due to the existence of stop codons in the native mRNAs. Release factors (RFs) that enter the A-site of the ribosome at stop codons compete with puromycin. To overcome this issue, we have used a highly controllable reconstituted cell-free system for puromycin-based techniques, and observed efficient IVV formation and C-terminal labelling using templates possessing a stop codon. The optimal conditions of IVV formation using templates possessing a stop codon was RF (−), while that of C-terminal labelling was RF (−) and the ribosome recycling factor (RRF) (+). Thus, we have overcome the experimental limitations of conventional IVV. In addition, we discovered that RRF significantly increases the efficiency of C-terminal protein labelling, but not IVV formation.