DOI: 10.6084/m9.figshare.808594.v1
title: Model-free simulation approach to molecular diffusion tensors: Lysozyme

description: This file contains part 1 (lysozyme) of the supplementary material for the following publication: Title: Model-free simulation approach to molecular diffusion tensors<br>Authors: Guillaume Chevrot, Konrad Hinsen, Gerald R. Kneller<br>Journal: Journal of Chemical Physics <strong>139</strong>, 154110 (2013) It contains the software implementing the computations described in the article, the input datasets for lysozyme, the resulting output datasets, and the figures. A detailed list is given below. The file lysozyme_diffusion.ap is a HDF5 file that can be read with any HDF5-compatible software, including the free HDFView package (http://www.hdfgroup.org/hdf-java-html/hdfview/). HDFView can be used to inspect the arrays and tables contained in this file. Reading the protein structure requires software that understands the Mosaic data model (http://bitbucket.org/molsim/mosaic/). The file has been prepared using the ActivePapers framework (http://bitbucket.org/khinsen/active_papers_py) for reproducible research. It contains all the software used in the study that is described in the article, as well as most of the data, starting from the rigid-body trajectories. The original all-atom Molecular Dynamics trajectories are not included because of their size. All software is written in the Python language. The ActivePapers framework keeps track of which data was generated using which script and also notes the user name, machine name, and versions of all software packages used when running each script. Readers wishing to modify and re-run the scripts, or to run them on different input data, should download and install the ActivePapers framework. In order to reduce the file size, some intermediate data (the coordinate and velocity trajectories computed from the original rigid-body trajectories) has been removed from lysozyme_diffusion.ap. It can be restored using the ActivePapers command-line tool using the command aptool update <strong>Datasets contained in the file lysozyme_diffusion.ap</strong><br><br> The computations are based on multiple independent simulations of lysozyme in water. The correlation functions are calculated for each simulation and are then averaged. The diffusion tensor is calculated from the averaged correlation functions. The correlation function plots are presented only for the averages. All single-trajectory data has a numerical suffix. In the following list, only the first one ("_1") is shown. <em><strong>1) Input data.</strong></em> <em>1.1) The reference structure of lysozyme, with the atomic masses, in Mosaic format.</em> reference_structures<br> reference_structure_1<br> atom_masses<br> configuration<br> universe <em>1.2) The rigid-body trajectory, consisting of the time axis, the center-of-mass positions, and the orientation stored as a quaternion trajectory.</em> trajectory<br> center_of_mass_1<br> orientation_1<br> time <em>1.3) The temperature of the simulation.</em> temperature <strong><em>2) Preliminary analyses (calclet thermal_averages).</em></strong> <em>2.1) The reduced mass of lysozyme in its solvent box,and the tensor of inertia, needed for step 2.2.</em> mass_and_inertia:<br> reduced_mass_1<br> inertia_tensor_1 <em>2.2) The theoretical variances of the x, y, z components of the linear and angular velocities in thermal equilibrium. For comparison with the variances computed later from the trajectory.</em> thermal_velocities<br> variance_angular_velocity_laboratory_frame<br> variance_angular_velocity_molecular_frame<br> variance_velocity_laboratory_frame<br> variance_velocity_molecular_frame <strong><em>3) Preprocessing of the trajectory (calclet trajectory_processing). This computes the center-of-mass velocities and the angular velocities in the laboratory and molecular frames.</em></strong> <em>3.1) Means and variances of the linear and angular velocities. Ideally, the means should be zero and the variances equal to the values under "thermal_velocities".</em> trajectory_statistics<br> mean_angular_velocity_laboratory_frame_1<br> mean_angular_velocity_molecular_frame_1<br> mean_velocity_laboratory_frame_1<br> mean_velocity_molecular_frame_1<br> variance_angular_velocity_laboratory_frame_1<br> variance_angular_velocity_molecular_frame_1<br> variance_velocity_laboratory_frame_1<br> variance_velocity_molecular_frame_1 <em>3.2) Linear and angular velocities.</em> velocity_trajectories<br> rotation_laboratory_frame<br> rotation_molecular_frame<br> translation_laboratory_frame<br> translation_molecular_frame <em>3.3) Linear and angular positions.</em> coordinate_trajectories<br> rotation_laboratory_frame_1<br> rotation_molecular_frame_1<br> translation_laboratory_frame_1<br> translation_molecular_frame_1 <em><strong>4) Correlation functions and mean-square displacements (calclet correlation_functions).</strong></em> correlation_functions<br> correlation_function_laboratory_frame_1<br> correlation_function_molecular_frame_1<br> correlation_function_laboratory_frame_averaged<br> correlation_function_molecular_frame_averaged<br> correlation_function_time<br> mean_square_displacement_laboratory_frame_1<br> mean_square_displacement_molecular_frame_1<br> mean_square_displacement_laboratory_frame_averaged<br> mean_square_displacement_molecular_frame_averaged<br> mean_square_displacement_time <strong><em>5) Manually set parameters used in computing the diffusion tensors.</em></strong> correlation_function_integration_limit<br> correlation_function_plot_range<br> msd_fit_range<br> msd_plot_range <em><strong>6) Diffusion tensors from the Kubo relations (integral over the velocity correlation functions) and from the slope of the mean-square displacement.</strong></em> diffusion_tensor_kubo_laboratory_frame<br> diffusion_tensor_kubo_molecular_frame<br> diffusion_tensor_msd_laboratory_frame<br> diffusion_tensor_msd_molecular_frame