7. Analysis

A typical analysis tasks reads the trajectory (XTC) or energy (EDR) file, computes quantities, and produces datafiles that can be plotted or processed further, e.g. using Python scripts. A strength of Gromacs is that it comes with a wide range of tools that each do one particular analysis task well (see the Gromacs manual and the Gromacs documentation).

7.1. Basic analysis

As examples, we perform a number of common analysis tasks.

• 7.1.1. RMSD
• 7.1.2. RMSF
• 7.1.3. Distances
• 7.1.4. Radius of gyration

7.2. More Gromacs tools

A number of interesting quantities and observables [1] can be calculated with Gromacs tools. A selection is shown below but you are encouraged to read the Gromacs manual and the Gromacs documentation to find out what else is available.

Selection of Gromacs analysis tools

The full list of Gromacs commands contains 98 different tools. A small selection of commonly used ones are shown here:

gmx energy

basic thermodynamic properties of the system

gmx rms

calculate the root mean square deviation from a reference structure

gmx rmsf

calculate the per-residue root mean square fluctuations

gmx gyrate

calculate the radius of gyration

gmx mindist and gmx distance

calculate the distance between atoms or groups of atoms (make a index file with gmx make_ndx to define the groups of interest). gmx mindist is especially useful to find water molecules close to a region of interest.

gmx do_dssp

Use the DSSP algorithm [Kabsch1983] to analyze the secondary structure (helices, sheets, …).

See also

• For analysis coupled with visualization look at VMD.
• For analyzing MD trajectories in many common formats (including the XTC, TRR, etc. used by Gromacs) using Python, have a look at the MDAnalysis Python library (the MDAnalysis Tutorial is a good place to start… and it also uses AdK as an example).

Footnotes

[1]

“Observable” is used in the widest sense in that we know an estimator function of all or a subset of the system’s phase space coordinates that is averaged to provide a quantity of interest. In many cases it requires considerable more work to connect such an “observable” to a true experimental observable that is measured in an experiment.