Paramo Teresa

Activation of Toll-like receptors nucleates assembly of the MyDDosome signaling hub

Published date : 26 Jun 2018

Infection and tissue damage induces assembly of supramolecular organizing centres (SMOCs)), such as the Toll-like receptor (TLR) MyDDosome, to co-ordinate inflammatory signaling. SMOC assembly is thought to drive digital all-or-none responses, yet TLR activation by diverse microbes induces anything from mild to severe inflammation. Using single-molecule imaging of TLR4-MyDDosome signaling in living macrophages, we find that MyDDosomes assemble within minutes of TLR4 stimulation.

type
Journal Paper
journal
eLife, 2018 Jan 24;7. pii: e31377. doi: 10.7554/eLife.31377
Impact Factor
7.616

Energetics of Endotoxin Recognition in the Toll-Like Receptor 4 Innate Immune Response

Published date : 09 Dec 2015

Bacterial outer membrane lipopolysaccharide (LPS) potently stimulates the mammalian innate immune system, and can lead to sepsis, the primary cause of death from infections. LPS is sensed by Toll-like receptor 4 (TLR4) in complex with its lipid-binding coreceptor MD-2, but subtle structural variations in LPS can profoundly modulate the response.

type
Journal Paper
journal
Scientific Reports, 2015 Dec 9;5:17997. doi: 10.1038/srep17997
Impact Factor
5.578

Efficient Characterization of Protein Cavities within Molecular Simulation Trajectories: trj_cavity

Published date : 28 Mar 2014

Protein cavities and tunnels are critical in determining phenomena such as ligand binding, molecular transport, and enzyme catalysis. Molecular dynamics (MD) simulations enable the exploration of the flexibility and conformational plasticity of protein cavities, extending the information available from static experimental structures relevant to, for example, drug design. Here, we present a new tool (trj_cavity) implemented within the GROMACS (www.gromacs.org) framework for the rapid identification and characterization of cavities detected within MD trajectories.

type
Journal Paper
journal
Journal of Chemical Theory and Computation, 2014, 10 (5), pp 2151–2164 DOI: 10.1021/ct401098b
Impact Factor
5.31