Chemogenomic Analysis of G-Protein Coupled Receptors and Their Ligands Deciphers Locks and Keys Governing Diverse Aspects of Signalling

Understanding the molecular mechanism of signalling in the important super-family of G-protein-coupled receptors (GPCRs) is causally related to questions of how and where these receptors can be activated or inhibited. In this context, it is of great interest to unravel the common molecular features of GPCRs as well as those related to an active or inactive state or to subtype specific G-protein coupling. In our underlying chemogenomics study, we analyse for the first time the statistical link between the properties of G-protein-coupled receptors and GPCR ligands. The technique of mutual information (MI) is able to reveal statistical inter-dependence between variations in amino acid residues on the one hand and variations in ligand molecular descriptors on the other. Although this MI analysis uses novel information that differs from the results of known site-directed mutagenesis studies or published GPCR crystal structures, the method is capable of identifying the well-known common ligand binding region of GPCRs between the upper part of the seven transmembrane helices and the second extracellular loop. The analysis shows amino acid positions that are sensitive to either stimulating (agonistic) or inhibitory (antagonistic) ligand effects or both. It appears that amino acid positions for antagonistic and agonistic effects are both concentrated around the extracellular region, but selective agonistic effects are cumulated between transmembrane helices (TMHs) 2, 3, and ECL2, while selective residues for antagonistic effects are located at the top of helices 5 and 6. Above all, the MI analysis provides detailed indications about amino acids located in the transmembrane region of these receptors that determine G-protein signalling pathway preferences

Outer membrane protein folding from an energy landscape perspective

The cell envelope is essential for the survival of Gram-negative bacteria. This specialised membrane is densely packed with outer membrane proteins (OMPs), which perform a variety of functions. How OMPs fold into this crowded environment remains an open question. Here, we review current knowledge about OFMP folding mechanisms in vitro and discuss how the need to fold to a stable native state has shaped their folding energy landscapes. We also highlight the role of chaperones and the β-barrel assembly machinery (BAM) in assisting OMP folding in vivo and discuss proposed mechanisms by which this fascinating machinery may catalyse OMP folding

Comparative Molecular Dynamics Simulation Studies of Realistic Eukaryotic, Prokaryotic, and Archaeal Membranes

We present a comparative all-atom molecular dynamics simulation study of 18 biomembrane systems with lipid compositions corresponding to eukaryotic, bacterial, and archaebacterial membranes together with three single-component lipid bilayers. A total of 105 lipid types used in this study include diverse sterols and glycerol-based lipids with acyl chains of various lengths, unsaturation degrees, and branched or cyclic moieties. Our comparative analysis provides deeper insight into the influences of sterols and lipid unsaturation on the structural and mechanical properties of these biomembranes, including water permeation into the membrane hydrocarbon core. For sterol-containing membranes, sterol fraction is correlated with the membrane thickness, the area compressibility modulus, and lipid order but anticorrelated with the area per lipid and sterol tilt angles. Similarly, for all 18 biomembranes, lipid order is correlated with the membrane thickness and area compressibility modulus. Sterols and lipid unsaturation produce opposite effects on membrane thickness, but only sterols influence water permeation into the membrane. All membrane systems are accessible for public use in CHARMM-GUI Archive. They can be used as templates to expedite future modeling of realistic cell membranes with transmembrane and peripheral membrane proteins to study their structure, dynamics, molecular interactions, and function in a nativelike membrane environment.http://deepblue.lib.umich.edu/bitstream/2027.42/171945/2/acs.jcim.1c01514.pdfPublished versio

Age-Dependent Neuroimmune Modulation of IGF-1R in the Traumatic Mice

<p>Abstract</p> <p>Background</p> <p>Age-dependent neuroimmune modulation following traumatic stress is accompanied by discordant upregulation of Fyn signaling in the frontal cortex, but the mechanistic details of the potential cellular behavior regarding IGF-1R/Fyn have not been established.</p> <p>Methods</p> <p>Trans-synaptic IGF-1R signaling during the traumatic stress was comparably examined in wild type, Fyn (−/−) and MOR (−/−) mice. Techniques included primary neuron culture, in vitro kinase activity, immunoprecipitation, Western Blot, sucrose discontinuous centrifugation. Besides that, [³ H] incorporation was used to assay lymphocyte proliferation and NK cell activity.</p> <p>Results</p> <p>We demonstrate robust upregulation of synaptic Fyn activity following traumatic stress, with higher amplitude in 2-month mice than that in 1-year counterpart. We also established that the increased Fyn signaling is accompanied by its molecular connection with IGF-1R within the synaptic zone. Detained analysis using Fyn (−/−) and MOR (−/−) mice reveal that IGF-1R/Fyn signaling is governed to a large extent by mu opioid receptor (MOR), and with age-dependent manner; these signaling cascades played a central role in the modulation of lymphocyte proliferation and NK cell activity.</p> <p>Conclusions</p> <p>Our data argued for a pivotal role of synaptic IGF-1R/Fyn signaling controlled by MOR downstream signaling cascades were crucial for the age-dependent neuroimmune modulation following traumatic stress. The result here might present a new quality of synaptic cellular communication governing the stress like events and have significant potential for the development of therapeutic approaches designed to minimize the heightened vulnerability during aging.</p

Modeling GPCRs

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