Step 3: Analyze your trajectory

Visualizing your trajectories

VMD is our favorite software for molecular dynamics visualization. It also provides a variety of easy to use GUI tools for trajectory analysis. To watch a trajectory in VMD, first load the original PDB file (the one used by SMOG, if using an all-atom model). If a coarse-grained model is being visualized, then you would need to load the gro file that was generated by SMOG. After the PDB/GRO file is loaded, select "load data into molecule" and load the trajectory files) into the PDB/GRO structure. If you prefer to load the structures from the command line, you can use a command of the form "vmd PDBFILE.pdb XTC_FILE.xtc". This will load the xtc file into the PDB structure.

Calculating quantities from your simulations

Gromacs includes a large number of analysis programs with their distribution. Many of their tools produce text files formatted for use with Grace (a very powerful 2-D plotting tool).

MDTraj is a set of libraries that are designed for the analysis of a wide range of file formats. Whether you use OpenSMOG, Gromacs, NAMD, or LAMMPS, you are likely going to find MDTraj useful. MDtraj has a large user base that makes many extensions available for free.

WHAM is a method for computing thermodynamic quantities from simulations. WHAM is provided with SMOG 2. This can be used to analyze umbrella, replica-exchange, or constant-temperature simulations, in order to computes F(Q), F(Q1,Q2), Cv(T), Q(T), Q1(Q2) and many other quantities (Q is an arbitrary reaction coordinate). Functionality is described in the SMOG 2 Manual. Examples and source code can be found in the SMOG 2 distribution.

Contributed Tools

Members of the SMOG community have voluntarily shared the following tools. They come without any guarantee or support from the SMOG developers.

g_kuh - calculates Q, the fraction of native contacts for a trajectory (.xtc). Found in either of the SMOG enhanced Gromacs versions: 4.5.3 or the 5.0.4. Documentation can be found here.

PyWham (2/10/15) Another implementation of the Weighted Histogram Analysis Method written in Python. Features include, but are not limited to, free energy in multiple dimensions, umbrella bias removal and free energy perturbation, ensemble average of a user-defined quantity, customization of data input methods. Documentation and examples are included in the download package. Direct questions to li.sun@rice.edu.

This resource is provided by the Center for Theoretical Biological Physics.
Please direct questions and comments to info@smog-server.org.
Page created and maintained by Jeff Noel and Paul Whitford