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8 results for “potential of mean force”
Adenylate Kinase Potential of Mean Force
<p>Adenylate kinase (AdK) is a enzyme that undergoes a large hinge-like motion. Because of an abundance of structural and functional data, it has become a standard system to test computational methods for sampling conformational transitions.<sup>1</sup></p> <p>In 2009, we studied the conformational transition between open and closed <em>E. coli</em> AdK without substrate, i.e. “apo AdK”, with a variety of computational methods.<sup>2</sup> As part of the study we also produced a free energy landscape (a <strong>potential of mean force</strong> or <strong>PMF</strong>) as a function of two collective variables, the angles formed by the LID and NMP domains with the CORE domain.<sup>3</sup> We <sup>2</sup> and others<sup>4</sup><sup>,</sup><sup>5</sup> have used this PMF to compare methods that sample transition paths to the underlying free energy landscape.</p> <p><strong>Terms of Use</strong></p> <p>The data are made available under a <strong>Attribution-ShareAlike 4.0 International</strong> licence (include the following when using the data):</p> <p><em>Adenylate Kinase Potential of Mean Force</em> by O Beckstein, EJ Denning, JR Perilla, TB Woolf is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Based on a work at http://becksteinlab.physics.asu.edu/file_download/11/free_bw2_tol1e-05.dat.</p> <p>When you make use of the data (contained in the file free_bw2_tol1e-05.dat) in published work, <strong>cite</strong> the paper<sup>2</sup></p> <p>O. Beckstein, E. J. Denning, J. R. Perilla, and T. B. Woolf. <em>Zipping and unzipping of adenylate kinase: Atomistic insights into the ensemble of open ? closed transitions</em>. J. Mol. Biol., 394(1):160–176, 2009.</p> <p><strong>Data</strong></p> <p>The file free_bw2_tol1e-05.dat contains the PMF data shown in Fig. 4a of the JMB paper<sup>2</sup>.</p> <p>The image shows the data plotted with cubic spline smoothing.</p> <p>File format</p> <p>free_bw2_tol1e-05.dat is the output from WHAM. The important data columns are</p> <ol> <li>NMP-core angle (degrees)</li> <li>LID-core angle (degrees)</li> <li>free energy in kcal/mol</li> </ol> <p>(Other columns are output from wham and can be ignored.)</p> <p><strong>Methods</strong></p> <p>Conformations of <em>E. coli</em> AdK were umbrella-sampled in the space of the NMP-core and LID-core angles.<sup>3</sup> The protein was modelled in implicit solvent with the ACE2 electrostatics model. The resulting umbrella data were unbiased using Alan Grossfield’s wham code with</p> <ul> <li>bin size 2º</li> <li>tolerance of the self consistency procedure 1e-5 <em>kT</em></li> <li>limits 34º < NMP < 80º and 94º < LID < 156º</li> </ul> <p>The first 2000 frames (200ps) of each window were discarded as equilibration and the remaining 3000 frames were used for the PMF. For further details please see the paper.<sup>2</sup></p> <p><strong>References</strong></p> <ol> <li>S. L. Seyler and O. Beckstein, O. <em>Sampling large conformational transitions: adenylate kinase as a testing ground</em>. Mol. Simul., 40(10–11): 855–877, 2014.</li> <li>O. Beckstein, E. J. Denning, J. R. Perilla, and T. B. Woolf. <em>Zipping and unzipping of adenylate kinase: Atomistic insights into the ensemble of open / closed transitions</em>. J. Mol. Biol., 394(1):160–176, 2009.</li> <li>See the 2009 paper<sup>2</sup> for the definitions and the MDAnalysis tutorial’s Exercise 4 for Python code to calculate the angles.</li> <li>M. Gur, J. D. Madura, and I. Bahar. <em>Global transitions of proteins explored by a multiscale hybrid methodology: Application to adenylate kinase</em> Biophysical Journal, 105(7):1643 – 1652, 2013.</li> <li>A. Uyar, N. Kantarci-Carsibasi, T. Haliloglu, and P. Doruker. <em>Features of large hinge-bending conformational transitions. Prediction of closed structure from open state</em>. Biophysical Journal, 106(12):2656 – 2666, 2014 </li> </ol>
Adsorption free energies and potentials of mean-force for interactions between amino acids, lipid fragments, and nanoparticles
<p>This dataset contains tabulated potentials of mean force (PMFs) and associated adsorption (binding) free energies for interactions of amino acids side chain analogues and lipid fragments (LF) with a range of materials: titanium dioxide, iron oxide, amorphous silica, quartz, and a range of carbon-based materials including amorphous carbon, graphene and carbon nanotubes both in a pristine form and functionalized by certain chemical groups. All data were computed from atomistic molecular dynamics simulations as a part of the SmartNanoTox project 2016-2020. Version 2 of the dataset includes additional materials: zink oxide, zink sulfate in pristine and PMMA-coated forms computed within NanoSolveIt project (2019-2023). The data are intended to be used in coarse-grained models describing interactions of nanomaterials with nanoparticles, for the prediction of the binding affinity of proteins and lipids to nanoparticles, and as biological "fingerprints" of nanomaterials characterizing behavior of the nanomaterials in biological environments. </p>
Potential of Mean Force (PMFs) for Zerovalent Iron (Fe (100-110-111)) NanoParticles
<p>The data is potential of mean force for three fcc configurations of zero valent iron, (100), (110) and (111) nano particles interacting with side chain analugos of 22 different amino acids that are the building blocks of the proteins. We used adaptive Well-Tempered Metadynamics method to calculate the adsorption free energies that has previously been described for measuring the adsorption of the biomolecules for TiO2 and Ag. GROMACS and plumed softwares were used to carry out the simulations and the CHARMM-GUI/Nanomaterial Modeler was used create the iron slabs. </p> <p>The system was solvated using the original form of TIP3 water model . It was then neutralized by NaCl regarding the charge of the whole system. In the MD calculation the energy of the system has been minimized by Verlet particle-based cut-off scheme using charge groups and steepest gradient method for 1000 steps. The system was equilibrated under constant pressure, particles and temperature (300 K) condition (NPT) using Berendsen weak coupling or 1.0 ns. The component of the pressure tensor were set to 1.0 bar. The thermostat and the barostat for the relaxation time was 1.0 ps. The system underwent another more unbiased equilibrations for 10 ns in the NVT ensemble conditions. The Nose-Hoover thermostat’s relaxation time constant for the NVT ensemble was 5 ps. The cut-off distance was set to 1.0 nm for the VdW short range interactions. In the metadynamics biased simulation, the Surface Separation Distance (SSD) describes the reaction coordinates. This SSD measures the minimum distance of the center-of-mass (COMs) of the SCA Rmol and the surface atoms ri along the z coordinate.</p>
Data from: Evolution of Large Aβ16-22 Aggregates at Atomic Details and Potential of Mean force Associated to Peptide Unbinding and Fragmentation Events
<p>This data accompanies the paper entitled <em>Evolution of Large Aβ16-22 Aggregates at Atomic Details and Potential of Mean force Associated to Peptide Unbinding and Fragmentation Events</em></p> <p>The zip archive contains the results of molecular dynamics simulations of the 2 systems investigated in the paper: the first one with 139 <em>Aβ16-22 </em><em>peptides, the second one with 106 peptides.</em><em> </em>Each system has been simulated at 300 K. Starting configurations of the peptides are provided for all the systems in GRO Gromos87 format. Trajectories with the positions of the peptides every 100 ps are provided for all the systems in XTC gromacs format. For system 1 we also provide XTC trajectories for all the replicas of the REST2 simulation.</p>
Potential of Mean Force (PMFs) for Zerovalent Aluminium (100-110-111) NanoParticles
<p>The dataset encompasses the potential of mean force data concerning three distinct fcc configurations of zero valent aluminum nanoparticles: (100), (110), and (111), in interaction with side-chain analogs of 22 diverse amino acids, which constitute the basic building blocks of proteins. The calculations employed the adaptive Well-Tempered Metadynamics method, a well-established technique previously utilized for assessing biomolecule adsorption on materials like Fe, TiO2 and Ag. To conduct these simulations, we utilized the GROMACS and PLUMED software packages, while the construction of the aluminium slabs was accomplished with the CHARMM-GUI/Nanomaterial Modeler. In the metadynamics biased simulation, the Surface Separation Distance (SSD) served as the primary reaction coordinate. This SSD quantifies the minimum separation between the center-of-mass (COM) of the side-chain analog (SCA) molecule and the surface atoms (ri) along the z-coordinate.</p>
Data from: Potentials of mean force fail to describe chemical bond-breaking in solution
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Thermodynamics of small-molecule insertion across membrane mixtures: Insight from the potential of mean force
<p>This repository contains input data referenced in supporting information of the paper titled "Thermodynamics of small-molecule insertion across membrane mixtures: Insight from the potential of mean force" by Alessia Centi, Arghya Dutta, Sapun H. Parekh, and Tristan Bereau.</p>
Complementary data to "Molecular dynamics trajectories for 630 drug-membrane potentials of mean force"
<p>Missing data from "Molecular dynamics trajectories for 630 drug-membrane potentials of mean force"</p> <p>Hoffmann, Christian; Centi, Alessia; Menichetti, Roberto; Bereau, Tristan (2020): Molecular dynamics trajectories for 630 drug-membrane potentials of mean force. figshare. Collection.</p> <p>https://doi.org/10.6084/m9.figshare.c.4641551.v1</p> <p>Includes:</p> <ul> <li>'bsResult.xvg' of DIM_P2-P3 in DLPC</li> </ul>
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