No abstract

Obesity is an important risk factor for exacerbating chronic diseases such as cardiovascular disease and cancer. High serum level of saturated free fatty acids such as palmitate is an important contributor for obesity-induced diseases. Here, we examined the contribution of inflammasome activation in vascular cells to free fatty acid-induced endothelial dysfunction and vascular injury in obesity. Our findings demonstrated that high fat diet-induced impairment of vascular integrity and enhanced vascular permeability in the myocardium in mice were significantly attenuated by Nlrp3 gene deletion. In microvascular endothelial cells (MVECs), palmitate markedly induces Nlrp3 inflammasome complex formation leading to caspase-1 activation and IL1β production. By fluorescence microscopy and flow cytometry, we observed that such palmitate-induced Nlrp3 inflammasome activated was accompanied by a reduction in inter-endothelial tight junction proteins ZO-1/ZO-2. Such palmitate-induced decrease of ZO-1/ZO-2 was also correlated with an increase in the permeability of endothelial monolayers treated with palmitates. Moreover, palmitate-induced alterations in ZO-1/ZO-2 or permeability were significantly reversed by an inflammasome activity inhibitor, YVAD, or a high mobility group box 1 (HMGB1) activity inhibitor glycyrrhizin. Lastly, blockade of cathepsin B with Ca-074Me significantly abolished palmitate-induced activation of Nlrp3 inflammasomes, down-regulation of ZO-1/ZO-2, and enhanced permeability in MVECs or their monolayers. Together, these data strongly suggest that activation of endothelial inflammasomes due to increased free fatty acids produces HMGB1, which disrupts inter-endothelial junctions and increases paracellular permeability of endothelium contributing to early onset of endothelial injury during obesity.

No abstract

In protein-ligand binding, only a few residues contribute significantly to the ligand binding. Quantitative characterization of binding free energies of specific residues in protein-ligand binding is extremely useful in our understanding of drug resistance and rational drug design. In this paper, we present an alanine scanning approach combined with an efficient interaction entropy method to compute residue-specific protein-ligand binding free energies in protein-drug binding. In the current approach, the entropic components in the free energies of all residues binding to the ligand are explicitly computed from just a single trajectory MD simulation by using the interaction entropy method. In this approach the entropic contribution to binding free energy is determined from fluctuations of individual residue-ligand interaction energies contained in the MD trajectory. The calculated residue-specific binding free energies give relative values between those for ligand binding to the wild type protein and those to the mutants when specific results mutated to alanine. Computational study for the binding of two classes of drugs (first and second generation drugs) to target protein ALK and its mutant was performed. Important or hot spot residues with large contributions to the total binding energy are quantitatively characterized and the mutation effect for the loss of binding affinity for the first generation drug is explained. Finally, it is very interesting to note that the sum of those individual residue-specific binding free energies are in quite good agreement with the experimentally measured total binding free energies for this protein-ligand system.

This paper proposes a novel method that can predict protein interaction sites in heterocomplexes using residue spatial sequence profile and evolution rate approaches. The former represents the information of multiple sequence alignments while the latter corresponds to a residueÕs evolutionary conservation score based on a phylogenetic tree. Three predictors using a support vector machines algorithm are constructed to predict whether a surface residue is a part of a protein-protein interface. The efficiency and the effectiveness of our proposed approach is verified by its better prediction performance compared with other models. The study is based on a non-redundant data set of heterodimers consisting of 69 protein chains.