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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.

International audience; 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.