Yosuke Ozawa

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.

Comprehensive sequence analysis of translation termination sites in various eukaryotes

Recent investigations into the translation termination sites of various organisms have revealed that not only stop codons but also sequences around stop codons have an effect on translation termination. To investigate the relationship between these sequence patterns and translation as well as its termination efficiency, we analysed the correlation between strength of consensus and translation efficiency, as predicted according to Codon Adaptation Index (CAI) value. We used RIKEN full-length mouse cDNA sequences and ten other eukaryotic UniGene datasets from NCBI for the analyses. First, we conducted sequence profile analyses following translation termination sites. We found base G and A at position +1 as a strong consensus for mouse cDNA. A similar consensus was found for other mammals, such as Homo sapiens, Rattus norvegicus and Bos taurus. However, some plants had different consensus sequences. We then analysed the correlation between the strength of consensus at each position and the codon biases of whole coding regions, using information content and CAI value. The results showed that in mouse cDNA, CAI value had a positive correlation with information content at positions +1. We also found that, for positions with strong consensus, the strength of the consensus is likely to have a positive correlation with CAI value in some other eukaryotes. Along with these observations, biological insights into the relationship between gene expression level, codon biases and consensus sequence around stop codons will be discussed.

Comparative Study of Translation Termination Sites and Release Factors (RF1 and RF2) in Procaryotes

Translation termination is catalyzed by release factors that recognize stop codons. However, previous works have shown that in some bacteria, the termination process also involves bases around stop codons. Recently, Ito et al. analyzed release factors and identified the amino acids therein that recognize stop codons. However, the amino acids that recognize bases around stop codons remain unclear. To identify the candidate amino acids that recognize the bases around stop codons, we aligned the protein sequences of the release factors of various bacteria and searched for amino acids that were conserved specifically in the sequence of bacteria that seemed to regulate translation termination by bases around stop codons. As a result, species having several highly conserved residues in RF1 and RF2 showed positive correlations between their codon usage bias and conservation of the bases around the stop codons. In addition, some of the residues were located very close to the SPF motif, which deciphers stop codons. These results suggest that these conserved amino acids enable the release factors to recognize the bases around the stop codons.