Dataset Information

Blocking the interaction between S100A9 protein and RAGE V domain using S100A12 protein.

ABSTRACT: The proteins S100A9 and S100A12 are associated with the human S100 calcium-binding protein family. These proteins promote interaction with target proteins and alter their conformation when they bind to calcium ions in EF-hand motifs. The V domain of RAGE (Receptor for Advanced Glycation End products) is crucial for S100A9 binding. The binding of RAGE with S100 family proteins aids in cell proliferation. In this report, we demonstrate that S100A12 protein hinders the binding of S100A9 with the RAGE V-domain. We used fluorescence and NMR spectroscopy to analyze the interaction of S100A9 with S100A12. The binary complex models of S100A9-S100A12 were developed using data obtained from 1H-15N HSQC NMR titrations and the HADDOCK program. We overlaid the complex models of S100A9-S100A12 with the same orientation of S100A9 and the RAGE V-domain. This complex showed that S100A12 protein blocks the interaction between S100A9 and the RAGE V-domain. It means S100A12 may be used as an antagonist for S100A9. The results could be favorable for developing anti-cancer drugs based on S100 family proteins.

SUBMITTER: Katte R

PROVIDER: S-EPMC6001950 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Blocking the interaction between S100A9 protein and RAGE V domain using S100A12 protein.

Katte Revansiddha R Yu Chin C

PloS one 20180614 6

The proteins S100A9 and S100A12 are associated with the human S100 calcium-binding protein family. These proteins promote interaction with target proteins and alter their conformation when they bind to calcium ions in EF-hand motifs. The V domain of RAGE (Receptor for Advanced Glycation End products) is crucial for S100A9 binding. The binding of RAGE with S100 family proteins aids in cell proliferation. In this report, we demonstrate that S100A12 protein hinders the binding of S100A9 with the RA ...[more]

PMID: 29902210

Similar Datasets

Project description:The number of diabetic patients in Europe and world-wide is growing. Diabetes confers a 2-fold higher risk for vascular disease. Lack of insulin production (Type 1 diabetes, T1D) or lack of insulin responsiveness (Type 2 diabetes, T2D) causes systemic metabolic changes such as hyperglycemia (HG) which contribute to the pathology of diabetes. Monocytes and macrophages are key innate immune cells that control inflammatory reactions associated with diabetic vascular complications. Inflammatory programming of macrophages is regulated and maintained by epigenetic mechanisms, in particular histone modifications. The aim of our study was to identify the epigenetic mechanisms involved in the hyperglycemia-mediated macrophage activation. Using Affymetrix microarray profiling and RT-qPCR we identified that hyperglycemia increased the expression of S100A9 and S100A12 in primary human macrophages. Expression of S100A12 was sustained after glucose levels were normalized. Glucose augmented the response of macrophages to Toll-like receptor (TLR)-ligands Palmatic acid (PA) and Lipopolysaccharide (LPS) i.e., pro-inflammatory stimulation. The abundance of activating histone Histone 3 Lysine 4 methylation marks (H3K4me1, H3K4me3) and general acetylation on histone 3 (AceH3) with the promoters of these genes was analyzed by chromatin immunoprecipitation. Hyperglycemia increased acetylation of histones bound to the promoters of S100A9 and S100A12 in M1 macrophages. In contrast, hyperglycemia caused a reduction in total H3 which correlated with the increased expression of both S100 genes. The inhibition of histone methyltransferases SET domain-containing protein (SET)7/9 and SET and MYND domain-containing protein (SMYD)3 showed that these specifically regulated S100A12 expression. We conclude that hyperglycemia upregulates expression of S100A9, S100A12 via epigenetic regulation and induces an activating histone code on the respective gene promoters in M1 macrophages. Mechanistically, this regulation relies on action of histone methyltransferases SMYD3 and SET7/9. The results define an important role for epigenetic regulation in macrophage mediated inflammation in diabetic conditions.

Project description:BackgroundWe can describe protein-protein interactions (PPIs) as sets of distinct domain-domain interactions (DDIs) that mediate the physical interactions between proteins. Experimental data confirm that DDIs are more consistent than their corresponding PPIs, lending support to the notion that analyses of DDIs may improve our understanding of PPIs and lead to further insights into cellular function, disease, and evolution. However, currently available experimental DDI data cover only a small fraction of all existing PPIs and, in the absence of structural data, determining which particular DDI mediates any given PPI is a challenge.ResultsWe present two contributions to the field of domain interaction analysis. First, we introduce a novel computational strategy to merge domain annotation data from multiple databases. We show that when we merged yeast domain annotations from six annotation databases we increased the average number of domains per protein from 1.05 to 2.44, bringing it closer to the estimated average value of 3. Second, we introduce a novel computational method, parameter-dependent DDI selection (PADDS), which, given a set of PPIs, extracts a small set of domain pairs that can reconstruct the original set of protein interactions, while attempting to minimize false positives. Based on a set of PPIs from multiple organisms, our method extracted 27% more experimentally detected DDIs than existing computational approaches.ConclusionsWe have provided a method to merge domain annotation data from multiple sources, ensuring large and consistent domain annotation for any given organism. Moreover, we provided a method to extract a small set of DDIs from the underlying set of PPIs and we showed that, in contrast to existing approaches, our method was not biased towards DDIs with low or high occurrence counts. Finally, we used these two methods to highlight the influence of the underlying annotation density on the characteristics of extracted DDIs. Although increased annotations greatly expanded the possible DDIs, the lack of knowledge of the true biological false positive interactions still prevents an unambiguous assignment of domain interactions responsible for all protein network interactions.Executable files and examples are given at: http://www.bhsai.org/downloads/padds/

Dataset Information

Blocking the interaction between S100A9 protein and RAGE V domain using S100A12 protein.

Publications

Blocking the interaction between S100A9 protein and RAGE V domain using S100A12 protein.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets