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• W2964143164 endingPage "104372" @default.
• W2964143164 startingPage "104372" @default.
• W2964143164 abstract "The innate immune system provides a first line of defense against foreign microorganisms, and is typified by the Toll-like receptor (TLR) family. TLR4 is of particular interest, since over-stimulation of its pathway by excess lipopolysaccharide (LPS) molecules from the outer membranes of Gram-negative bacteria can result in sepsis, which causes millions of deaths each year. In this review, we outline our use of molecular simulation approaches to gain a better understanding of the determinants of LPS recognition, towards the search for novel immunotherapeutics. We first describe how atomic-resolution simulations have enabled us to elucidate the regulatory conformational changes in TLR4 associated with different LPS analogues, and hence a means to rationalize experimental structure-activity data. Furthermore, multiscale modelling strategies have provided a detailed description of the thermodynamics and intermediate structures associated with the entire TLR4 relay - which consists of a number of transient receptor/coreceptor complexes - allowing us trace the pathway of LPS transfer from bacterial membranes to the terminal receptor complex at the plasma membrane surface. Finally, we describe our efforts to leverage these computational models, in order to elucidate previously undisclosed anti-inflammatory mechanisms of endogenous host-defense peptides found in wounds. Collectively, this work represents a promising avenue for the development of novel anti-septic treatments, inspired by nature's innate defense strategies." @default.
• W2964143164 created "2019-07-30" @default.
• W2964143164 creator A5001674990 @default.
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• W2964143164 date "2019-09-01" @default.
• W2964143164 modified "2023-10-02" @default.
• W2964143164 title "Multiscale modeling of innate immune receptors: Endotoxin recognition and regulation by host defense peptides" @default.
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• W2964143164 doi "https://doi.org/10.1016/j.phrs.2019.104372" @default.
• W2964143164 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31351116" @default.
• W2964143164 hasPublicationYear "2019" @default.
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