<?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%">Prakash, Shikha</style></author><author><style face="normal" font="default" size="100%">Krishna, Anjali</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%">Actin cytoskeleton and membrane interactions: role in GPCR function and organization</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Indian National Science Academy</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">SEP</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">85</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Cell signaling networks are generally initiated at the cell membrane and mediated by receptors such as the G proteincoupled receptors (GPCRs). Adjacent to the cell membrane lies a complex network of proteins - the actin cytoskeleton to which several structural and physiological roles have been attributed. An emerging role of the cytoskeleton is to modulate GPCR function and organization, either directly or indirectly. The GPCR-cytoskeleton cross-talk is a complex hierarchical process where each step has its own set of rules and combinations. Due to the inherent complexity involved at each step and the multiple spatio-temporal levels, a complete picture is yet to emerge. In this review article, we provide an overview of actin-membrane interactions and how they modulate GPCR function and organization.&lt;/p&gt;
</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%">&lt;p&gt;Indian&lt;/p&gt;
</style></custom3><custom4><style face="normal" font="default" size="100%">&lt;p&gt;0.804&lt;/p&gt;
</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%">Prakash, Shikha</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%">Special issue: membrane and receptor dynamics</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Membrane Biology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</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%">252</style></volume><pages><style face="normal" font="default" size="100%">207-211</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">4-5</style></issue><work-type><style face="normal" font="default" size="100%">Editorial Material</style></work-type><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%">&lt;p&gt;1.746&lt;/p&gt;
</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%">Krishna, Anjali</style></author><author><style face="normal" font="default" size="100%">Prakash, Shikha</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%">Sphingomyelin effects in caveolin-1 mediated membrane curvature published as part of the journal of physical chemistry virtual special issue ``computational and experimental advances in biomembranes''</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Physical Chemistry B</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</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%">124</style></volume><pages><style face="normal" font="default" size="100%">5177-5185</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 caveolin-1 (cav-1) protein is an integral component of caveolae and has been reported to colocalize with cholesterol and sphingomyelin-rich curved membrane domains. Here, we analyze the molecular interactions between cav-1 and sphingomyelin containing bilayers using a series of coarse-grain simulations, focusing on lipid clustering and membrane curvature. We considered a palmitoylated-cav-1 construct interacting with phospholipid/cholesterol membranes with asymmetrically distributed sphingomyelin, varying between 5 and 15% in total. We observe that cav-1 binds to the intracellular leaflet and induces a small positive curvature in the leaflet to which it is bound and an opposing negative curvature in the extracellular leaflet. Both cholesterol and sphingomyelin are observed to cluster in cav-1 bound membranes, mainly in the extracellular leaflet. Due to their negative spontaneous curvature, clustering of cholesterol and sphingomyelin facilitates membrane curvature such that the extent of either cholesterol or sphingomyelin clustering is dependent on the curvature induced. Our results suggest that cav-1 binding induces concentration-dependent curvature effects in sphingomyelin-rich membranes. Overall, our work is an important step in understanding the molecular basis of curvature and lipid clustering in cav1 bound cellular membranes.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">25</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><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%">&lt;p&gt;2.857&lt;/p&gt;
</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%">Prakash, Shikha</style></author><author><style face="normal" font="default" size="100%">Krishna, Anjali</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%">Caveolin induced membrane curvature and lipid clustering: two sides of the same coin?</style></title><secondary-title><style face="normal" font="default" size="100%">Faraday Discussions</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">DEC</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">232</style></volume><pages><style face="normal" font="default" size="100%">218-235</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Caveolin-1 (cav-1) is a multi-domain membrane protein that is a key player in cell signaling, endocytosis and mechanoprotection. It is the principle component of cholesterol-rich caveolar domains and has been reported to induce membrane curvature. The molecular mechanisms underlying the interactions of cav-1 with complex membranes, leading to modulation of membrane topology and the formation of cholesterol-rich domains, remain elusive. In this study, we aim to understand the effect of lipid composition by analyzing the interactions of cav-1 with complex membrane bilayers comprised of about sixty lipid types. We have performed a series of coarse-grain molecular dynamics simulations using the Martini force-field with a cav-1 protein construct (residue 82-136) that includes the membrane binding domains and a palmitoyl tail. We observe that cav-1 induces curvature in this complex membrane, though it is restricted to a nanometer length scale. Concurrently, we observe a clustering of cholesterol, sphingolipids and other lipid molecules leading to the formation of nanodomains. Direct microsecond timescale interactions are observed for specific lipids such as cholesterol, phosphatidylserine and phosphatidylethanolamine lipid types. The results indicate that there is an interplay between membrane topology and lipid species. Our work is a step toward understanding how lipid composition and organization regulate the formation of caveolae, in the context of endocytosis and cell signaling.</style></abstract><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%">4.008</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%">Prakash, Shikha</style></author><author><style face="normal" font="default" size="100%">Krishna, Anjali</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%">Caveolin mediated curvature and clustering: from simple to complex membrane</style></title><secondary-title><style face="normal" font="default" size="100%">Biophysical Journal</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">FEB </style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">120</style></volume><pages><style face="normal" font="default" size="100%">232A-233A</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">3</style></issue><work-type><style face="normal" font="default" size="100%">Meeting Abstract</style></work-type><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%">4.033</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%">Prakash, Shikha</style></author><author><style face="normal" font="default" size="100%">Malshikare, Hrushikesh</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%">Molecular mechanisms underlying caveolin-1 mediated membrane curvature</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Membrane Biology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cholesterol clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">Membrane curvature</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein-lipid interactions</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2022</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%">255</style></volume><pages><style face="normal" font="default" size="100%">225-236</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	Caveolin-1 is one of the main protein components of caveolae that acts as a mechanosensor at the cell membrane. The interactions of caveolin-1 with membranes have been shown to lead to complex effects such as curvature and the clustering of specific lipids. Here, we review the emerging concepts on the molecular interactions of caveolin-1, with a focus on insights from coarse-grain molecular dynamics simulations. Consensus structural models of caveolin-1 report a helix-turn-helix core motif with flanking domains of higher disorder that could be membrane composition dependent. Caveolin-1 appears to be mainly surface-bound and does not embed very deep in the membrane to which it is bound. The most interesting aspect of caveolin-1 membrane binding is the interplay of cholesterol clustering and membrane curvature. Although cholesterol has been reported to cluster in the vicinity of caveolin-1 by several approaches, simulations show that the clustering is maximal in membrane leaflet opposing the surface-bound caveolin-1. The intrinsic negative curvature of cholesterol appears to stabilize the negative curvature in the opposing leaflet. In fact, the simulations show that blocking cholesterol clustering (through artificial position restraints) blocks membrane curvature, and vice versa. Concomitant with cholesterol clustering is sphingomyelin clustering, again in the opposing leaflet, but in a concentration-dependent manner. The differential stress due to caveolin-1 binding and the inherent asymmetry of the membrane leaflets could be the determinant for membrane curvature and needs to be further probed. The review is an important step to reconcile the molecular level details emerging from simulations with the mesoscopic details provided by state of the art experimental approaches. [GRAPHICS] .&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">2-3</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><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%">&lt;p&gt;
	2.426&lt;/p&gt;
</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%">Prakash, Shikha</style></author><author><style face="normal" font="default" size="100%">Krishna, Anjali</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%">Cofilin-membrane interactions: electrostatic effects in phosphoinositide lipid binding</style></title><secondary-title><style face="normal" font="default" size="100%">ChemPhysChem</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">complex membrane</style></keyword><keyword><style  face="normal" font="default" size="100%">lipid clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">Martini coarse-grain simulations</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular dynamics simulations</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein-lipid interactions</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">FEB </style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">24</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	The actin cytoskeleton interacts with the cell membrane primarily through the indirect interactions of actin-binding proteins such as cofilin-1. The molecular mechanisms underlying the specific interactions of cofilin-1 with membrane lipids are still unclear. Here, we performed coarse-grain molecular dynamics simulations of cofilin-1 with complex lipid bilayers to analyze the specificity of protein-lipid interactions. We observed the maximal interactions with phosphoinositide (PIP) lipids, especially PIP2 and PIP3 lipids. A good match was observed between the residues predicted to interact and previous experimental studies. The clustering of PIP lipids around the membrane bound protein leads to an overall lipid demixing and gives rise to persistent membrane curvature. Further, through a series of control simulations, we observe that both electrostatics and geometry are critical for specificity of lipid binding. Our current study is a step towards understanding the physico-chemical basis of cofilin-PIP lipid interactions.&lt;/p&gt;
</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%">&lt;p&gt;
	Foreign&lt;/p&gt;
</style></custom3><custom4><style face="normal" font="default" size="100%">&lt;p&gt;
	3.520&lt;/p&gt;
</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%">Malshikare, Hrushikesh</style></author><author><style face="normal" font="default" size="100%">Prakash, Shikha</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%">Differential membrane curvature induced by distinct protein conformers</style></title><secondary-title><style face="normal" font="default" size="100%">Soft Matter</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</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%">19</style></volume><pages><style face="normal" font="default" size="100%">4021-4028</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	Membrane topology changes are associated with various cellular processes and are modulated by synergistic effects between lipid composition and membrane-associated proteins. However, how protein shape or conformational dynamics couples to membrane molecular properties remains unclear. In this work, we aim to investigate this coupling behavior using the curvature-inducing protein caveolin-1. We considered distinct protein conformers of the helical hairpin protein corresponding to different protein shapes, such as the wedge and the banana-shaped conformers. The different protein conformers were simulated in a coarse-grain representation in the presence of cholesterol-sphingomyelin rich membrane. We observed that membrane curvature is dependent on protein shape and is the lowest for the wedge conformer and maximal for the banana conformer. The differences in the net stress between the two membrane leaflets, calculated from the lateral pressure profile distributions in lipid bilayers for different protein conformers, show a similar trend. In conjunction, we show that cholesterol and sphingomyelin clustering in the membrane is modulated by protein shape. Overall, our results provide molecular-level insights into the coupling between membrane topology, protein shape and lipid clustering in cell membranes.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">22</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><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%">&lt;p&gt;
	3.4&lt;/p&gt;
</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%">Saraf, Deepashri</style></author><author><style face="normal" font="default" size="100%">Prakash, Shikha</style></author><author><style face="normal" font="default" size="100%">Pinjari, Aadil</style></author><author><style face="normal" font="default" size="100%">Pujari, Bhalchandra</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%">Surface-induced demixing of self-assembled isomeric mixtures of citral</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Molecular Liquids</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Citral</style></keyword><keyword><style  face="normal" font="default" size="100%">Isomers</style></keyword><keyword><style  face="normal" font="default" size="100%">Metal-organic interactions</style></keyword><keyword><style  face="normal" font="default" size="100%">molecular dynamics</style></keyword><keyword><style  face="normal" font="default" size="100%">Self-assembly</style></keyword><keyword><style  face="normal" font="default" size="100%">Shannon entropy</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JUL </style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">381</style></volume><pages><style face="normal" font="default" size="100%">121803</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 self-assembly of organic molecules and their interactions with metal surfaces have been of considerable interest, both for developing novel functional materials and for understanding fundamental design principles of nanostructures. In this study, we investigate the solution and surface-induced self-assembly of two stereoisomers of citral molecules (geranial and neral) using atomistic molecular dynamics simulations. We demonstrate that the morphology of the aggregates in water is concentration dependent (ranging from distorted spherical to slab-like aggregates) but independent of isomer effects. The isomeric mixtures of citral indicate homogeneous mixing based on differential density maps and high values of Shannon entropy. Interestingly, surface-confinement of citral aggregates on a Cu(111) surface leads to phase segregation and demixing of the two isomers that is more apparent in the surface-bound monolayer in comparison to the adjacent layers. Positional ordering and formation of domains are observed over a series of isomeric citral mixtures with varying compositions, as indicated by high differential density and low values of Shannon entropy. Our work provides new insights into molecular self-assembly of organic molecules in nanostructures and metal-organic overlayers.(c) 2023 Elsevier B.V. All rights reserved.&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Article</style></work-type><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%">&lt;p&gt;
	6.633&lt;/p&gt;
</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%">Kharche, Shalmali</style></author><author><style face="normal" font="default" size="100%">Yadav, Manjul</style></author><author><style face="normal" font="default" size="100%">Hande, Vrushali</style></author><author><style face="normal" font="default" size="100%">Prakash, Shikha</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%">Improved protein dynamics and hydration in the martini3 coarse-grain model</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Chemical Information and Modelling </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JAN</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">64</style></volume><pages><style face="normal" font="default" size="100%">837-850</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 Martini coarse-grain force-field has emerged as an important framework to probe cellular processes at experimentally relevant time- and length-scales. However, the recently developed version, the Martini3 force-field with the implemented Go model (Martini3Go), as well as previous variants of the Martini model have not been benchmarked and rigorously tested for globular proteins. In this study, we consider three globular proteins, ubiquitin, lysozyme, and cofilin, and compare protein dynamics and hydration with observables from experiments and all-atom simulations. We show that the Martini3Go model is able to accurately model the structural and dynamic features of small globular proteins. Overall, the structural integrity of the proteins is maintained, as validated by contact maps, radii of gyration (Rg), and SAXS profiles. The chemical shifts predicted from the ensemble sampled in the simulations are consistent with the experimental data. Further, a good match is observed in the protein-water interaction energetics, and the hydration levels of the residues are similar to atomistic simulations. However, the protein-water interaction dynamics is not accurately represented and appears to depend on the protein structural complexity, residue specificity, and water dynamics. Our work is a step toward testing and assessing the Martini3Go model and provides insights into future efforts to refine Martini models with improved solvation effects and better correspondence to the underlying all-atom systems.&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Article</style></work-type><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%">&lt;p&gt;
	5.6&lt;/p&gt;
</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%">Yadav, Manjul</style></author><author><style face="normal" font="default" size="100%">Kharche, Shalmali</style></author><author><style face="normal" font="default" size="100%">Prakash, Shikha</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%">Benchmarking a dual-scale hybrid simulation framework for small globular proteins combining the CHARMM36 and Martini2 models</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Molecular Graphics &amp; Modelling</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Hybrid simulations</style></keyword><keyword><style  face="normal" font="default" size="100%">Martini force-field</style></keyword><keyword><style  face="normal" font="default" size="100%">Mixed models</style></keyword><keyword><style  face="normal" font="default" size="100%">Multi-scale simulations</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2025</style></year><pub-dates><date><style  face="normal" font="default" size="100%">MAR</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">135</style></volume><pages><style face="normal" font="default" size="100%">108926</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	Multi-scale models in which varying resolutions are considered in a single molecular dynamics simulation setup are gaining importance in integrative modeling. However, combining atomistic and coarse-grain resolutions, especially for coarse-grain force fields derived from top-down approaches, have not been well explored. In this study, we have implemented and tested a dual-resolution simulation approach to model globular proteins in atomistic detail (represented by the CHARMM36 model) with the surrounding solvent in Martini2 coarse-grain detail. The hybrid scheme considered is an extension of a model implemented earlier for mainly lipid and water molecules. We have considered a set of small globular proteins and have extensively compared to atomistic benchmark simulations as well as a host of experimental observables. We show that the protein structural dynamics sampled in the hybrid scheme is robust, and the intra-protein contact maps are reproduced, despite increased fluctuations of the loop regions. A good match is observed with experimental small angle X-ray scattering (SAXS) and NMR observables, such as chemical shifts and (3)J((HN-H alpha))-coupling, with the best match obtained for the chemical shifts. However, deviations are observed in the water dynamics and protein-water interactions which we attribute to the limitation of solvent screening in the coarse-grain force field. The computational speed-up achieved is about 2-3 times compared to an all-atom system. Overall, the hybrid model is able to retain the main features of the underlying atomistic conformational landscape with a two-fold speed-up in computational cost.&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Article</style></work-type><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%">&lt;p&gt;
	2.7&lt;/p&gt;
</style></custom4></record></records></xml>