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Title: Simulation Parameters used in the Study titled "Efficient Estimation of Binding Free Energies between Peptides and an MHC Class II Molecule Using Coarse-Grained Molecular Dynamics Simulations with a Weighted Histogram Analysis Method" Open Access Deposited

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  • The simulation parameters supplied in this document can be applied to conduct Umbrella Simulations in GROMACS 4.6.5 Two types of parameters are provided, the set distance parameter along the reaction coordinate and the spring force (K) applied during the simulation. These parameters can be supplied to GROMACS 4.6.5 via the following commends in mdp file: pull: umbrella; pull-geometry: distance; pull-coord1-init: set_distance_in_table; pull-coord1-k: K_value_in_table;
Description
  • We provide the parameters used in Umbrella Sampling simulations reported in our study "Efficient Estimation of Binding Free Energies between Peptides and an MHC Class II Molecule Using Coarse-Grained Molecular Dynamics Simulations with a Weighted Histogram Analysis Method", namely the set positions and spring constants for each window in simulations. Two tables are provided. Table 1 lists the names of the peptides and their corresponding sequences. Table 2 lists the parameters. The abstract of our work is the following: We estimate the binding free energy between peptides and an MHC class II molecule using molecular dynamics (MD) simulations with Weighted Histogram Analysis Method (WHAM). We show that, owing to its more thorough sampling in the available computational time, the binding free energy obtained by pulling the whole peptide using a coarse-grained (CG) force field (MARTINI) is less prone to significant error induced by biased-sampling than using an atomistic force field (AMBER). We further demonstrate that using CG MD to pull 3-4 residue peptide segments while leaving the remain-ing peptide segments in the binding groove and adding up the binding free energies of all peptide segments gives robust binding free energy estimations, which are in good agreement with the experimentally measured binding affinities for the peptide sequences studied. Our approach thus provides a promising and computationally efficient way to rapidly and relia-bly estimate the binding free energy between an arbitrary peptide and an MHC class II molecule.
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  • wenjunh@umich.edu
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  • 10/04/2019
Published
  • 01/09/2017
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  • https://doi.org/10.7302/Z2M906KK
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To Cite this Work:
Larson, R. G., Wen, F., Huang, W., Huang, M. (2017). Simulation Parameters used in the Study titled "Efficient Estimation of Binding Free Energies between Peptides and an MHC Class II Molecule Using Coarse-Grained Molecular Dynamics Simulations with a Weighted Histogram Analysis Method" [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/Z2M906KK

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Efficient Estimation of Binding Free Energies between Peptides and an MHC Class II Molecule Using Coarse-Grained Molecular Dynamics Simulations with a Weighted Histogram Analysis Method Ming Huang, Wenjun Huang, Fei Wen, and Ronald G. Larson* Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan 48109-2136 *rlarson@umich.edu Parameters Used in the Umbrella Sampling Simulations We provide the parameters used in Umbrella Sampling simulations carried using GROMACS 4.6.5 reported in our study (DOI: 10.1002/jcc.24845), namely the set positions (x) and spring constants (K) for each window in simulations. Tab 1 lists the names of the peptides and their corresponding sequences. Tab 2-12 lists the parameters. The title of each simulation in Tab 2-12 follows the convention ˇ°Force Field_Peptide Name_Reaction Coordinateˇ±. Note two reaction coordinates are employer, namely center-of-mass (COM) and peeling from one peptide end (peel). Those marked with ˇ°*ˇ± are windows repaired by increasing the spring constant; those marked with ˇ°**ˇ± are windows added to ensure sufficient overlap, where overlap was inadequate in the original histograms. These parameters can be supplied to GROMACS 4.6.5 via the following commends in mdp file: pull: umbrella; pull-geometry: distance; pull-coord1-init: set_distance_in_table; pull-coord1-k: K_value_in_table;

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