How does a small molecule bind at a cryptic binding site?

Protein-protein interactions (PPIs) are ubiquitous biomolecular processes that are central to virtually all aspects of cellular function. Identifying small molecules that modulate specific disease-related PPIs is a strategy with enormous promise for drug discovery. The design of drugs to disrupt PPIs is challenging, however, because many potential drug-binding sites at PPI interfaces are “cryptic”: When unoccupied by a ligand, cryptic sites are often flat and featureless, and thus not readily recognizable in crystal structures, with the geometric and chemical characteristics of typical small-molecule binding sites only emerging upon ligand binding. The rational design of small molecules to inhibit specific PPIs would benefit from a better understanding of how such molecules bind at PPI interfaces. To this end, we have conducted unbiased, all-atom MD simulations of the binding of four small-molecule inhibitors (SP4206 and three SP4206 analogs) to interleukin 2 (IL2)—which performs its function by forming a PPI with its receptor—without incorporating any prior structural information about the ligands’ binding. In multiple binding events, a small molecule settled into a stable binding pose at the PPI interface of IL2, resulting in a protein–small-molecule binding site and pose virtually identical to that observed in an existing crystal structure of the IL2-SP4206 complex. Binding of the small molecule stabilized the IL2 binding groove, which when the small molecule was not bound emerged only transiently and incompletely. Moreover, free energy perturbation (FEP) calculations successfully distinguished between the native and non-native IL2–small-molecule binding poses found in the simulations, suggesting that binding simulations in combination with FEP may provide an effective tool for identifying cryptic binding sites and determining the binding poses of small molecules designed to disrupt PPI interfaces by binding to such sites.     

A prawn transglutaminase: Molecular characterization and biochemical properties

In this study, we report the bioinformatics characterization, gene expression, transglutaminase activity and coagulation assays of transglutaminase (TGase) of freshwater prawn Macrobrachium rosenbergii identified from the constructed cDNA library by GS FLX™ technology. Even though, TGase have sequence similarity, they differ extensively in their substrate specificity and are thought to play an important in variety of functions such as development, tissue differentiation and immune responses etc. Gene expression studies show that MrTGase is widely distributed in the tissues such as heart, muscle, intestine, brain, etc., but higher amounts are found in hemocyte. Results of TGase mRNA relative expression in hemocyte, before and after infected with white spot syndrome baculovirus (WSBV) and Vibrio harveyi show that the gene expression initially increases up to 24 h and then it falls down. Coagulation assay results showed that the endogenous TGase is involved in the rapid assembly of a specific, plasma clotting protein. Structural studies show that MrTGase contains a typical TGc domain between 323 and 424, and two putative integrin-binding motifs at Arg¹⁸⁰–Gly¹⁸¹–Asp¹⁸² and Arg²⁶⁹–Gly²⁷⁰–Asp²⁷¹. The predicted 3D model of MrTGase contains 47.04% coils (366 amino acid residues), 26.74% extended strand (208 residues), 21.72% α-helix (169 residues) and 4.5% beta turns (35 residues). BLASTp analysis of MrTGase exhibited high sequence similarities with other crustacean TGase, with the highest observed in white shrimp (77.1%). Moreover, the phylogenetic analysis also showed that MrTGase clustered with the other members of crustacean TGase. Overall, these results suggested that MrTGase is a major and functional TGase of M. rosenbergii for haemolymph coagulation and also in spread of infection.     

Cardiomyocyte Antihypertrophic Effect of Adipose Tissue Conditioned Medium from Rats and Its Abrogation by Obesity is Mediated by the Leptin to Adiponectin Ratio

White adipocytes are known to function as endocrine organs by secreting a plethora of bioactive adipokines which can regulate cardiac function including the development of hypertrophy. We determined whether adipose tissue conditioned medium (ATCM) generated from the epididymal regions of normal rats can affect the hypertrophic response of cultured rat ventricular myocytes to endothelin-1 (ET-1) administration. Myocytes were treated with ET-1 (10 nM) for 24 hours in the absence or presence of increasing ATCM concentrations. ATCM supressed the hypertrophic response to ET-1 in a concentration-dependent manner, an effect enhanced by the leptin receptor antagonist and attenuated by an antibody against the adiponectin AdipoR1 receptor. Antihypertrophic effects were also observed with ATCM generated from perirenal-derived adipose tissue. However, this effect was absent in ATCM from adipose tissue harvested from corpulent JCR:LA-cp rats. Detailed analyses of adipokine content in ATCM from normal and corpulent rats revealed no differences in the majority of products assayed, although a significant increase in leptin concentrations concomitant with decreased adiponectin levels was observed, resulting in a 11 fold increase in the leptin to adiponectin ratio in ATCM from JCR:LA-cp. The antihypertrophic effect of ATCM was associated with increased phosphorylation of AMP-activated protein kinase (AMPK), an effect abrogated by the AdipoR1 antibody. Moreover, the antihypertrophic effect of ATCM was mimicked by an AMPK activator. There was no effect of ET-1 on mitogen-activated protein kinase (MAPK) activities 24 hour after its addition either in the presence or absence of ATCM. Our study suggests that adipose tissue from healthy subjects exerts antihypertrophic effects via an adiponectin–dependent pathway which is impaired in obesity, most likely due to adipocyte remodelling resulting in enhanced leptin and reduced adiponectin levels.     

Evaluation and integration of 49 genome-wide experiments and the prediction of previously unknown obesity-related genes

MOTIVATION: Genome-wide experiments only rarely show resounding success in yielding genes associated with complex polygenic disorders. We evaluate 49 obesity-related genome-wide experiments with publicly available findings including microarray, genetics, proteomics and gene knock-down from human, mouse, rat and worm, in terms of their ability to rediscover a comprehensive set of genes previously found to be causally associated or having variants associated with obesity. RESULTS: Individual experiments show poor predictive ability for rediscovering known obesity-associated genes. We show that intersecting the results of experiments significantly improves the sensitivity, specificity and precision of the prediction of obesity-associated genes. We create an integrative model that statistically significantly outperforms all 49 individual genome-wide experiments. We find that genes known to be associated with obesity are significantly implicated in more obesity-related experiments and use this to provide a list of genes that we predict to have the highest likelihood of association for obesity. The approach described here can include any number and type of genome-wide experiments and might be useful for other complex polygenic disorders as well.     

Is Demography Destiny? Application of Machine Learning Techniques to Accurately Predict Population Health Outcomes from a Minimal Demographic Dataset

For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC) from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD) outcomes (four NCDs and two major clinical risk factors), based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88) and those excluded from the development for use as a completely separated validation sample (median correlation 0.85), demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease.