BackgroundDeciphering protein-protein interaction (PPI) in domain level enriches valuable information about binding mechanism and functional role of interacting proteins. The 3D structures of complex proteins are reliable source of domain-domain interaction (DDI) but the number of proven structures is very limited. Several resources for the computationally predicted DDI have been generated but they are scattered in various places and their prediction show erratic performances. A well-organized PPI and DDI analysis system integrating these data with fair scoring system is necessary.MethodWe integrated three structure-based DDI datasets and twenty computationally predicted DDI datasets and constructed an interaction analysis system, named IDDI, which enables to browse protein and domain interactions with their relationships. To integrate heterogeneous DDI information, a novel scoring scheme is introduced to determine the reliability of DDI by considering the prediction scores of each DDI and the confidence levels of each prediction method in the datasets, and independencies between predicted datasets. In addition, we connected this DDI information to the comprehensive PPI information and developed a unified interface for the interaction analysis exploring interaction networks at both protein and domain level.ResultIDDI provides 204,705 DDIs among total 7,351 Pfam domains in the current version. The result presents that total number of DDIs is increased eight times more than that of previous studies. Due to the increment of data, 50.4% of PPIs could be correlated with DDIs which is more than twice of previous resources. Newly designed scoring scheme outperformed the previous system in its accuracy too. User interface of IDDI system provides interactive investigation of proteins and domains in interactions with interconnected way. A specific example is presented to show the efficiency of the systems to acquire the comprehensive information of target protein with PPI and DDI relationships. IDDI is freely available at http://pcode.kaist.ac.kr/iddi/.
The molecular interaction between tumor suppressor p53 and the anti-apoptotic Bcl-2 family proteins plays an essential role in the transcription-independent apoptotic pathway of p53. In this study, we investigated the binding of p53 DNA-binding domain (p53DBD) with the anti-apoptotic Bcl-2 family proteins, Bcl-w, Mcl-1, and Bcl-2, using GST pull-down assay and NMR spectroscopy. The GST pull-down assays and NMR experiments demonstrated the direct binding of the p53DBD with Bcl-w, Mcl-1, and Bcl-2. Further, NMR chemical shift perturbation data showed that Bcl-w and Mcl-1 bind to the positively charged DNA-binding surface of p53DBD. Noticeably, the refined structural models of the complexes between p53DBD and Bcl-w, Mcl-1, and Bcl-2 showed that the binding mode of p53DBD is highly conserved among the anti-apoptotic Bcl-2 family proteins. Furthermore, the chemical shift perturbations on Bcl-w, Mcl-1, and Bcl-2 induced by p53DBD binding occurred not only at the p53DBD-binding acidic region but also at the BH3 peptide-binding pocket, which suggests an allosteric conformational change similar to that observed in Bcl-XL. Taken altogether, our results revealed a structural basis for a conserved binding mechanism between p53DBD and the anti-apoptotic Bcl-2 family proteins, which shed light on to the molecular understanding of the transcription-independent apoptosis pathway of p53.
Post-translational modification (PTM) plays a crucial role in biological functions and corresponding disease developments. Discovering disease-associated non-synonymous SNPs (nsSNPs) altering PTM sites can help to estimate the various PTM candidates involved in diseases, therefore, an integrated analysis between SNPs, PTMs and diseases is necessary. However, only a few types of PTMs affected by nsSNPs have been studied without considering disease-association until now. In this study, we developed a new database called PTM-SNP which contains a comprehensive collection of human nsSNPs that affect PTM sites, together with disease information. Total 179,325 PTM-SNPs were collected by aligning missense SNPs and stop-gain SNPs on PTM sites (position 0) or their flanking region (position -7 to 7). Disease-associated SNPs from GWAS catalogs were also matched with detected PTM-SNP to find disease associated PTM-SNPs. Our result shows PTM-SNPs are highly associated with diseases, compared with other nsSNP sites and functional classes including near gene, intron and so on. PTM-SNP can provide an insight about discovering important PTMs involved in the diseases easily through the web site. PTM-SNP is freely available at http://gcode.kaist.ac.kr/ptmsnp.
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