<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pawar, Aiswarya B.</style></author><author><style face="normal" font="default" size="100%">Deshpande, Sneha A.</style></author><author><style face="normal" font="default" size="100%">Gopal, Srinivasa M.</style></author><author><style face="normal" font="default" size="100%">Wassenaar, Tsjerk A.</style></author><author><style face="normal" font="default" size="100%">Athale, Chaitanya A.</style></author><author><style face="normal" font="default" size="100%">Sengupta, Durba</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Thermodynamic and kinetic characterization of transmembrane helix association</style></title><secondary-title><style face="normal" font="default" size="100%">Physical Chemistry Chemical Physics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">NOV</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">2</style></number><publisher><style face="normal" font="default" size="100%">ROYAL SOC CHEMISTRY</style></publisher><pub-location><style face="normal" font="default" size="100%">THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLAND</style></pub-location><volume><style face="normal" font="default" size="100%">17</style></volume><pages><style face="normal" font="default" size="100%">1390-1398</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The transient dimerization of transmembrane proteins is an important event in several cellular processes and computational methods are being increasingly used to quantify their underlying energetics. Here, we probe the thermodynamics and kinetics of a simple transmembrane dimer to understand membrane protein association. A multi-step framework has been developed in which the dimerization profiles are calculated from coarse-grain molecular dynamics simulations, followed by meso-scale simulations using parameters calculated from the coarse-grain model. The calculated value of Delta G(assoc) is approx. -20 kJ mol(-1) and is consistent between three methods. Interestingly, the meso-scale stochastic model reveals low dimer percentages at physiologically-relevant concentrations, despite a favorable Delta G(assoc). We identify generic driving forces arising from the protein backbone and lipid bilayer and complementary factors, such as protein density, that govern self-interactions in membranes. Our results provide an important contribution in understanding membrane protein organization and linking molecular, nano-scale computational studies to meso-scale experimental data.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;</style></custom3><custom4><style face="normal" font="default" size="100%">4.449</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sengupta, Durba</style></author><author><style face="normal" font="default" size="100%">Joshi, Manali</style></author><author><style face="normal" font="default" size="100%">Athale, Chaitanya A.</style></author><author><style face="normal" font="default" size="100%">Chattopadhyay, Amitabha</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Shukla, AK</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">What can simulations tell us about GPCRs: integrating the scales</style></title><secondary-title><style face="normal" font="default" size="100%">G Protein-Coupled Receptors: Signaling, Trafficking and Regulation</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Methods in Cell Biology</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">Elsevier Academic Press Inc.</style></publisher><pub-location><style face="normal" font="default" size="100%">525 B Street, Suite 1900, San Diego, CA 92101-4495 USA :</style></pub-location><volume><style face="normal" font="default" size="100%">132</style></volume><pages><style face="normal" font="default" size="100%">429-452</style></pages><isbn><style face="normal" font="default" size="100%">978-0-12-803612-9; 978-0-12-803595-5</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The functional dynamics of G protein-coupled receptors (GPCRs) encompasses multiple spatiotemporal scales, ranging from femtoseconds to seconds and Angstroms to micrometers. Computational approaches, often in close collaboration with experimental methods, have been invaluable in unraveling GPCR structure and dynamics at these various hierarchical levels. The binding of natural and synthetic ligands to the wild-type and naturally occurring variant receptors have been analyzed by several computational methods. The activation of receptors from the inactive to the active state has been investigated by atomistic simulations and ongoing work on several receptors will help uncover general and receptor-specific mechanisms. The interaction of GPCRs with complex membranes that contain phospholipids and cholesterol have been probed by coarse-grain methods and shown to directly influence receptor association. In this chapter, we discuss computational approaches that have been successful in analyzing each scale of GPCR dynamics. An overview of these approaches will allow a more judicious choice of the appropriate method. We hope that an appreciation of the power of current computational approaches will encourage more critical collaborations. A comprehensive integration of the different approaches over the entire spatiotemporal scales promises to unravel new facets of GPCR function.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Raghunathan, Anu</style></author><author><style face="normal" font="default" size="100%">Athale, Chaitanya A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modelling natural and synthetic biological networks</style></title><secondary-title><style face="normal" font="default" size="100%">Current Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">OCT</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">113</style></volume><pages><style face="normal" font="default" size="100%">1221-1222</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">7</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">Indian</style></custom3><custom4><style face="normal" font="default" size="100%">0.843</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Deshpande, Sneha A.</style></author><author><style face="normal" font="default" size="100%">Pawar, Aiswarya B.</style></author><author><style face="normal" font="default" size="100%">Dighe, Anish</style></author><author><style face="normal" font="default" size="100%">Athale, Chaitanya A.</style></author><author><style face="normal" font="default" size="100%">Sengupta, Durba</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Role of spatial inhomogenity in GPCR dimerisation predicted by receptor association-diffusion models</style></title><secondary-title><style face="normal" font="default" size="100%">Physical Biology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JUN</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">14  </style></volume><pages><style face="normal" font="default" size="100%">036002</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">G protein-coupled receptor (GPCR) association is an emerging paradigm with far reaching implications in the regulation of signalling pathways and therapeutic interventions. Recent super resolution microscopy studies have revealed that receptor dimer steady state exhibits sub-second dynamics. In particular the GPCRs, muscarinic acetylcholine receptor M-1 (M1MR) and formyl peptide receptor (FPR), have been demonstrated to exhibit a fast association/dissociation kinetics, independent of ligand binding. In this work, we have developed a spatial kinetic Monte Carlo model to investigate receptor homo-dimerisation at a single receptor resolution. Experimentally measured association/dissociation kinetic parameters and diffusion coefficients were used as inputs to the model. To test the effect of membrane spatial heterogeneity on the simulated steady state, simulations were compared to experimental statistics of dimerisation. In the simplest case the receptors are assumed to be diffusing in a spatially homogeneous environment, while spatial heterogeneity is modelled to result from crowding, membrane micro-domains and cytoskeletal compartmentalisation or 'corrals'. We show that a simple association-diffusion model is sufficient to reproduce M1MR association statistics, but fails to reproduce FPR statistics despite comparable kinetic constants. A parameter sensitivity analysis is required to reproduce the association statistics of FPR. The model reveals the complex interplay between cytoskeletal components and their influence on receptor association kinetics within the features of the membrane landscape. These results constitute an important step towards understanding the factors modulating GPCR organisation.</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">Foreign</style></custom3><custom4><style face="normal" font="default" size="100%">1.621</style></custom4></record></records></xml>