Dorzolamide suppresses PKCδ -TIRAP-p38 MAPK signaling axis to dampen the inflammatory response.

Background: Sepsis is a syndrome due to microbial infection causing impaired multiorgan function. Its underlying cause is immune dysfunction and macrophages play an essential role. Methods: TIRAP interaction with PKCδ in macrophage was studied, revealing downstream signaling by Western blot and quantitative reverse transcriptase PCR. Dorzolamide (DZD) disrupting TIRAP-PKCδ interaction was identified by virtual screening and validated in vitro and in septic mice. Results: The study highlights the indispensable role of TIRAP-PKCδ in p38 MAPK-activation, NF-κB- and AP-1-mediated proinflammatory cytokines expression, whereas DZD significantly attenuated the signaling. Conclusion: Targeting TIRAP-PKCδ interaction by DZD is a novel therapeutic approach for treating sepsis.

Quality Assessment of Predicted Protein Models Using Energies Calculated by the Fragment Molecular Orbital Method.

Protein structure prediction directly from sequences is a very challenging problem in computational biology. One of the most successful approaches employs stochastic conformational sampling to search an empirically derived energy function landscape for the global energy minimum state. Due to the errors in the empirically derived energy function, the lowest energy conformation may not be the best model. We have evaluated the use of energy calculated by the fragment molecular orbital method (FMO energy) to assess the quality of predicted models and its ability to identify the best model among an ensemble of predicted models. The fragment molecular orbital method implemented in GAMESS was used to calculate the FMO energy of predicted models. When tested on eight protein targets, we found that the model ranking based on FMO energies is better than that based on empirically derived energies when there is sufficient diversity among these models. This model diversity can be estimated prior to the FMO energy calculations. Our result demonstrates that the FMO energy calculated by the fragment molecular orbital method is a practical and promising measure for the assessment of protein model quality and the selection of the best protein model among many generated.

A rotation-translation invariant molecular descriptor of partial charges and its use in ligand-based virtual screening.

BACKGROUND: Measures of similarity for chemical molecules have been developed since the dawn of chemoinformatics. Molecular similarity has been measured by a variety of methods including molecular descriptor based similarity, common molecular fragments, graph matching and 3D methods such as shape matching. Similarity measures are widespread in practice and have proven to be useful in drug discovery. Because of our interest in electrostatics and high throughput ligand-based virtual screening, we sought to exploit the information contained in atomic coordinates and partial charges of a molecule. RESULTS: A new molecular descriptor based on partial charges is proposed. It uses the autocorrelation function and linear binning to encode all atoms of a molecule into two rotation-translation invariant vectors. Combined with a scoring function, the descriptor allows to rank-order a database of compounds versus a query molecule. The proposed implementation is called ACPC (AutoCorrelation of Partial Charges) and released in open source. Extensive retrospective ligand-based virtual screening experiments were performed and other methods were compared with in order to validate the method and associated protocol. CONCLUSIONS: While it is a simple method, it performed remarkably well in experiments. At an average speed of 1649 molecules per second, it reached an average median area under the curve of 0.81 on 40 different targets; hence validating the proposed protocol and implementation.