Dataset Information

Three-dimensional structure of human NLRP10/PYNOD pyrin domain reveals a homotypic interaction site distinct from its mouse homologue.

ABSTRACT: NLRPs (Nucleotide-binding domain, leucine-rich repeat and pyrin domain containing proteins) are a family of pattern-recognition receptors (PRRs) that sense intracellular microbial components and endogenous stress signals. NLRP10 (also known as PYNOD) is a unique NLRP member characterized by a lack of the putative ligand-binding leucine-rich repeat domain. Recently, human NLRP10 has been shown to inhibit the self-association of ASC into aggregates and ASC-mediated procaspase-1 processing. However, such activities are not found in mouse NLRP10. Here we report the solution structure and dynamics of human NLRP10 pyrin domain (PYD), whose helix H3 and loop H2-H3 adopt a conformation distinct from those of mouse NLRP10. Docking studies show that human and mouse NLRP10 PYDs may interact differently with ASC PYD. These results provide a possible structural explanation for the contrasting effect of NLRP10 on ASC aggregation in human cells versus mouse models. Finally, we also provide evidence that in human NLRP10 the PYD domain may not interact with the NOD domain to regulate its intrinsic nucleotide hydrolysis activity.

SUBMITTER: Su MY

PROVIDER: S-EPMC3701624 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Three-dimensional structure of human NLRP10/PYNOD pyrin domain reveals a homotypic interaction site distinct from its mouse homologue.

Su Ming-Yuan MY Kuo Chiao-I CI Chang Chi-Fon CF Chang Chung-I CI

PloS one 20130704 7

NLRPs (Nucleotide-binding domain, leucine-rich repeat and pyrin domain containing proteins) are a family of pattern-recognition receptors (PRRs) that sense intracellular microbial components and endogenous stress signals. NLRP10 (also known as PYNOD) is a unique NLRP member characterized by a lack of the putative ligand-binding leucine-rich repeat domain. Recently, human NLRP10 has been shown to inhibit the self-association of ASC into aggregates and ASC-mediated procaspase-1 processing. However ...[more]

PMID: 23861819

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Project description:Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors.

Dataset Information

Three-dimensional structure of human NLRP10/PYNOD pyrin domain reveals a homotypic interaction site distinct from its mouse homologue.

Publications

Three-dimensional structure of human NLRP10/PYNOD pyrin domain reveals a homotypic interaction site distinct from its mouse homologue.

Similar Datasets

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