scholarly journals Capturing Protein-Ligand Recognition Pathways in Coarse-grained Simulation

2019 ◽  
Author(s):  
Bhupendra R. Dandekar ◽  
Jagannath Mondal
Keyword(s):  
Large Scale ◽  
Computational Cost ◽  
Md Simulations ◽  
Coarse Grained ◽  
Attractive Alternative ◽  
Complex Process ◽  
Potential Binding ◽  
Allosteric Sites ◽  
Potential Binding Site ◽  
Complete Process

AbstractProtein-substrate recognition is highly dynamic and complex process in nature. A key approach in deciphering the mechanism underlying the recognition process is to capture the kinetic process of substrate in its act of binding to its designated protein cavity. Towards this end, microsecond long atomistic molecular dynamics (MD) simulation has recently emerged as a popular method of choice, due its ability to record these events at high spatial and temporal resolution. However, success in this approach comes at an exorbitant computational cost. Here we demonstrate that coarse grained models of protein, when systematically optimised to maintain its tertiary fold, can capture the complete process of spontaneous protein-ligand binding from bulk media to cavity, within orders of magnitude shorter wall clock time compared to that of all-atom MD simulations. The simulated and crystallographic binding pose are in excellent agreement. We find that the exhaustive sampling of ligand exploration in protein and solvent, harnessed by coarse-grained simulation at a frugal computational cost, in combination with Markov state modelling, leads to clearer mechanistic insights and discovery of novel recognition pathways. The result is successfully validated against three popular protein-ligand systems. Overall, the approach provides an affordable and attractive alternative of all-atom simulation and promises a way-forward for replacing traditional docking based small molecule discovery by high-throughput coarse-grained simulation for searching potential binding site and allosteric sites. This also provides practical avenues for first-hand exploration of bio-molecular recognition processes in large-scale biological systems, otherwise inaccessible in all-atom simulations.

scholarly journals Antimicrobial action of the cationic peptide, chrysophsin-3: a coarse-grained molecular dynamics study

Soft Matter ◽  
2018 ◽  
Vol 14 (15) ◽  
pp. 2796-2807 ◽  
Author(s):  
Andrea Catte ◽  
Mark R. Wilson ◽  
Martin Walker ◽  
Vasily S. Oganesyan
Keyword(s):  
Molecular Dynamics ◽  
Large Scale ◽  
Md Simulations ◽  
Antimicrobial Action ◽  
Coarse Grained ◽  
Cationic Peptide ◽  
Coarse Grained Molecular Dynamics

Antimicrobial action of a cationic peptide is modelled by large scale MD simulations.

scholarly journals A Virtual Reality Ensemble Molecular Dynamics Workflow to Study Complex Conformational Changes in Proteins

2020 ◽  
Author(s):  
Jordi Juárez-Jiménez ◽  
Philip Tew ◽  
Michael o'connor ◽  
Salome Llabres ◽  
Rebecca Sage ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Virtual Reality ◽  
Conformational Changes ◽  
Large Scale ◽  
A Priori ◽  
Computational Cost ◽  
Md Simulations ◽  
Collective Variables ◽  
Sampling Protocols ◽  
State Models

<p>Molecular dynamics (MD) simulations are increasingly used to elucidate relationships between protein structure, dynamics and their biological function. Currently it is extremely challenging to perform MD simulations of large-scale structural rearrangements in proteins that occur on millisecond timescales or beyond, as this requires very significant computational resources, or the use of cumbersome ‘collective variable’ enhanced sampling protocols. Here we describe a framework that combines ensemble MD simulations and virtual-reality visualization (eMD-VR) to enable users to interactively generate realistic descriptions of large amplitude, millisecond timescale protein conformational changes in proteins. Detailed tests demonstrate that eMD-VR substantially decreases the computational cost of folding simulations of a WW domain, without the need to define collective variables <i>a priori</i>. We further show that eMD-VR generated pathways can be combined with Markov State Models to describe the thermodynamics and kinetics of large-scale loop motions in the enzyme cyclophilin A. Our results suggest eMD-VR is a powerful tool for exploring protein energy landscapes in bioengineering efforts. </p>

scholarly journals Determination of Protein Structural Ensembles by Hybrid-Resolution SAXS Restrained Molecular Dynamics.

2019 ◽  
Author(s):  
Cristina Paissoni ◽  
Alexander Jussupow ◽  
Carlo Camilloni
Keyword(s):  
Dynamical Behavior ◽  
Computational Cost ◽  
Three Dimensional ◽  
Md Simulations ◽  
Coarse Grained ◽  
Conformational Ensemble ◽  
Biomolecular Systems ◽  
Conformational Ensembles ◽  
Independent Experimental Data

<div><div><div><p>SAXS experiments provide low-resolution but valuable information about the dynamics of biomolecular systems, which could be ideally integrated in MD simulations to accurately determine conformational ensembles of flexible proteins. The applicability of this strategy is hampered by the high computational cost required to calculate scattering intensities from three-dimensional structures. We previously presented a metainference-based hybrid resolution method that makes atomistic SAXS-restrained MD simulation feasible by adopting a coarse-grained approach to efficiently back-calculate scattering intensities; here, we extend this technique, applying it in the framework of multiple-replica simulations with the aim to investigate the dynamical behavior of flexible biomolecules. The efficacy of the method is assessed on the K63-diubiquitin multi-domain protein, showing that inclusion of SAXS-restraints is effective in generating reliable and heterogenous conformational ensemble, also improving the agreement with independent experimental data.</p></div></div></div>

scholarly journals Combining Virtual Reality Visualization with Ensemble Molecular Dynamics to Study Complex Protein Conformational Changes

2020 ◽  
Author(s):  
Jordi Juárez-Jiménez ◽  
Philip Tew ◽  
Michael o'connor ◽  
Salome Llabres ◽  
Rebecca Sage ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Virtual Reality ◽  
Conformational Changes ◽  
Large Scale ◽  
Computational Cost ◽  
Md Simulations ◽  
Collective Variables ◽  
Protein Conformational Changes ◽  
Sampling Protocols ◽  
State Models

<p>Molecular dynamics (MD) simulations are increasingly used to elucidate relationships between protein structure, dynamics and their biological function. Currently it is extremely challenging to perform MD simulations of large-scale structural rearrangements in proteins that occur on millisecond timescales or beyond, as this requires very significant computational resources, or the use of cumbersome ‘collective variable’ enhanced sampling protocols. Here we describe a framework that combines ensemble MD simulations and virtual-reality visualization (eMD-VR) to enable users to interactively generate realistic descriptions of large amplitude, millisecond timescale protein conformational changes in proteins. Detailed tests demonstrate that eMD-VR substantially decreases the computational cost of folding simulations of a WW domain, without the need to define collective variables <i>a priori</i>. We further show that eMD-VR generated pathways can be combined with Markov State Models to describe the thermodynamics and kinetics of large-scale loop motions in the enzyme cyclophilin A. Our results suggest eMD-VR is a powerful tool for exploring protein energy landscapes in bioengineering efforts. </p>

scholarly journals Determination of Protein Structural Ensembles by Hybrid-Resolution SAXS Restrained Molecular Dynamics.

2019 ◽  
Author(s):  
Cristina Paissoni ◽  
Alexander Jussupow ◽  
Carlo Camilloni
Keyword(s):  
Dynamical Behavior ◽  
Computational Cost ◽  
Three Dimensional ◽  
Md Simulations ◽  
Coarse Grained ◽  
Conformational Ensemble ◽  
Biomolecular Systems ◽  
Conformational Ensembles ◽  
Independent Experimental Data

<div><div><div><p>SAXS experiments provide low-resolution but valuable information about the dynamics of biomolecular systems, which could be ideally integrated into MD simulations to accurately determine conformational ensembles of flexible proteins. The applicability of this strategy is hampered by the high computational cost required to calculate scattering intensities from three-dimensional structures. We previously presented a hybrid resolution method that makes atomistic SAXS-restrained MD simulation feasible by adopting a coarse-grained approach to efficiently back-calculate scattering intensities; here, we extend this technique, applying it in the framework of metainference with the aim to investigate the dynamical behavior of flexible biomolecules. The efficacy of the method is assessed on the K63-diubiquitin, showing that the inclusion of SAXS-restraints is effective in generating a reliable conformational ensemble, improving the agreement with independent experimental data.</p></div></div></div>

scholarly journals Combining Virtual Reality Visualization with Ensemble Molecular Dynamics to Study Complex Protein Conformational Changes

2020 ◽  
Author(s):  
Jordi Juárez-Jiménez ◽  
Philip Tew ◽  
Michael o'connor ◽  
Salome Llabres ◽  
Rebecca Sage ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Virtual Reality ◽  
Conformational Changes ◽  
Large Scale ◽  
Computational Cost ◽  
Md Simulations ◽  
Collective Variables ◽  
Protein Conformational Changes ◽  
Sampling Protocols ◽  
State Models

<p>Molecular dynamics (MD) simulations are increasingly used to elucidate relationships between protein structure, dynamics and their biological function. Currently it is extremely challenging to perform MD simulations of large-scale structural rearrangements in proteins that occur on millisecond timescales or beyond, as this requires very significant computational resources, or the use of cumbersome ‘collective variable’ enhanced sampling protocols. Here we describe a framework that combines ensemble MD simulations and virtual-reality visualization (eMD-VR) to enable users to interactively generate realistic descriptions of large amplitude, millisecond timescale protein conformational changes in proteins. Detailed tests demonstrate that eMD-VR substantially decreases the computational cost of folding simulations of a WW domain, without the need to define collective variables <i>a priori</i>. We further show that eMD-VR generated pathways can be combined with Markov State Models to describe the thermodynamics and kinetics of large-scale loop motions in the enzyme cyclophilin A. Our results suggest eMD-VR is a powerful tool for exploring protein energy landscapes in bioengineering efforts. </p>

scholarly journals Constant pH simulations with the coarse-grained MARTINI model — Application to oleic acid aggregates

2013 ◽  
Vol 91 (9) ◽  
pp. 839-846 ◽  
Author(s):  
W.F. Drew Bennett ◽  
Alexander W. Chen ◽  
Serena Donnini ◽  
Gerrit Groenhof ◽  
D. Peter Tieleman
Keyword(s):  
Fatty Acids ◽  
Oleic Acid ◽  
Phase Behavior ◽  
Large Scale ◽  
Md Simulations ◽  
Industrial Applications ◽  
Coarse Grained ◽  
Head Group ◽  
Constant Ph ◽  
Surfactant Phase

Long chain fatty acids are biologically important molecules with complex and pH sensitive aggregation behavior. The carboxylic head group of oleic acid is ionizable, with the pKa shifting to larger values, even above a value of 7, in certain aggregate states. While experiments have determined the macroscopic phase behavior, we have yet to understand the molecular level details for this complex behavior. This level of detail is likely required to fully appreciate the role of fatty acids in biology and for nanoscale biotechnological and industrial applications. Here, we introduce the use of constant pH molecular dynamics (MD) simulations with the coarse-grained MARTINI model and apply the method to oleic acid aggregates and a model lipid bilayer. By running simulations at different constant pH values, we determined titration curves and the resulting pKa for oleic acid in different environments. The coarse-grained model predicts positive pKa shifts, with a shift from 4.8 in water to 6.5 in a small micelle, and 6.6 in a dioleoylphosphatidylcholine (DOPC) bilayer, similar to experimental estimates. The size of the micelles increased as the pH increased, and correlated with the fraction of deprotonated oleic acid. We show this combination of constant pH MD and the coarse-grained MARTINI model can be used to model pH-dependent surfactant phase behavior. This suggests a large number of potential new applications of large-scale MARTINI simulations in other biological systems with ionizable molecules.

scholarly journals Explicit and implicit modeling of nanobubbles in hydrophobic confinement

2010 ◽  
Vol 82 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Joachim Dzubiella
Keyword(s):  
Large Scale ◽  
Md Simulations ◽  
Energy Functional ◽  
Coarse Grained ◽  
Implicit Solvent ◽  
Implicit Solvent Model ◽  
Solvent Model ◽  
Nanofluidic Devices ◽  
Capillary Evaporation ◽  
Hydrophobic Solutes

Water at normal conditions is a fluid thermodynamically close to the liquid-vapor phase coexistence and features a large surface tension. This combination can lead to interesting capillary phenomena on microscopic scales. Explicit water molecular dynamics (MD) computer simulations of hydrophobic solutes, for instance, give evidence of capillary evaporation on nanometer scales, i.e., the formation of nanometer-sized vapor bubbles (nanobubbles) between confining hydrophobic surfaces. This phenomenon has been exemplified for solutes with varying complexity, e.g., paraffin plates, coarse-grained homopolymers, biological and solid-state channels, and atomistically resolved proteins. It has been argued that nanobubbles strongly impact interactions in nanofluidic devices, translocation processes, and even in protein stability, function, and folding. As large-scale MD simulations are computationally expensive, the efficient multiscale modeling of nanobubbles and the prediction of their stability poses a formidable task to the'nanophysical' community. Recently, we have presented a conceptually novel and versatile implicit solvent model, namely, the variational implicit solvent model (VISM), which is based on a geometric energy functional. As reviewed here, first solvation studies of simple hydrophobic solutes using VISM coupled with the numerical level-set scheme show promising results, and, in particular, capture nanobubble formation and its subtle competition to local energetic potentials in hydrophobic confinement.

Development of an Analytical Injectivity Model for Non-Newtonian Polymer Solutions

SPE Journal ◽  
2014 ◽  
Vol 19 (03) ◽  
pp. 381-389 ◽  
Author(s):  
Zhitao Li ◽  
Mojdeh Delshad
Keyword(s):  
Analytical Model ◽  
Oil Recovery ◽  
Large Scale ◽  
Polymer Solutions ◽  
Computational Cost ◽  
Attractive Alternative ◽  
Chemical Flooding ◽  
Fine Grid ◽  
Well Model ◽  
Polymer Flood

Summary In applications of polymer flood for enhanced oil recovery (EOR), polymer injectivity is of great concern because project economics is sensitive to injection rates. In-situ non-Newtonian polymer rheology is the most crucial factor that affects polymer injectivity. There are several ongoing polymer-injection field tests in which the field injectivities differ significantly from the simulation forecasts. We have developed an analytical model to more accurately calculate and predict polymer injectivity during the field projects to help with optimum injection strategies. Significant viscosity variations during polymer flood occur in the vicinities of wellbores where velocities are high. As the size of a wellblock increases, velocity smears, and thus polymer injectivity is erroneously calculated. In the University of Texas Chemical Flooding Simulator (UTCHEM), the solution was to use an effective radius to capture the “grid effect,” which is empirical and impractical for large-scale field simulations with several hundred wells. Another approach is to use local grid refinement near wells, but this adds to the computational cost and limits the size of the problem. An attractive alternative to previous approaches is to extend the Peaceman well model (Peaceman 1983) to non-Newtonian polymer solutions. The polymer rheological model and its implementation in UTCHEM were validated by simulating single-phase polymer injectivity in coreflood experiments. On the basis of the Peaceman well model and UTCHEM polymer rheological models covering both shear-thinning and shear-thickening polymers, an analytical polymer-injectivity model was developed. The analytical model was validated by comparing results of different gridblock sizes and radial numerical simulation. We also tested a field case by comparing results of a fine-grid simulation and its upscaled coarse-grid model. A pilot-scale polymer flood was simulated to demonstrate the capability of the proposed analytical model. The model successfully captured polymer injectivity in all these cases with no need to introduce empirical parameters.

scholarly journals In Silico Prediction and Validation of CB2 Allosteric Binding Sites to Aid the Design of Allosteric Modulators

Molecules ◽  
2022 ◽  
Vol 27 (2) ◽  
pp. 453
Author(s):  
Jiayi Yuan ◽  
Chen Jiang ◽  
Junmei Wang ◽  
Chih-Jung Chen ◽  
Yixuan Hao ◽  
...  
Keyword(s):  
Binding Site ◽  
Binding Sites ◽  
Cannabinoid Receptors ◽  
Md Simulations ◽  
Docking Studies ◽  
Allosteric Modulators ◽  
3D Structures ◽  
Potential Binding ◽  
Allosteric Sites ◽  
Allosteric Binding Sites

Although the 3D structures of active and inactive cannabinoid receptors type 2 (CB2) are available, neither the X-ray crystal nor the cryo-EM structure of CB2-orthosteric ligand-modulator has been resolved, prohibiting the drug discovery and development of CB2 allosteric modulators (AMs). In the present work, we mainly focused on investigating the potential allosteric binding site(s) of CB2. We applied different algorithms or tools to predict the potential allosteric binding sites of CB2 with the existing agonists. Seven potential allosteric sites can be observed for either CB2-CP55940 or CB2-WIN 55,212-2 complex, among which sites B, C, G and K are supported by the reported 3D structures of Class A GPCRs coupled with AMs. Applying our novel algorithm toolset-MCCS, we docked three known AMs of CB2 including Ec2la (C-2), trans-β-caryophyllene (TBC) and cannabidiol (CBD) to each site for further comparisons and quantified the potential binding residues in each allosteric binding site. Sequentially, we selected the most promising binding pose of C-2 in five allosteric sites to conduct the molecular dynamics (MD) simulations. Based on the results of docking studies and MD simulations, we suggest that site H is the most promising allosteric binding site. We plan to conduct bio-assay validations in the future.

Sign in / Sign up

Export Citation Format

Share Document