Five cool features in PyMOL (that you may have missed)

Here are some lesser-known PyMOL tricks that let you do some pretty cool (and useful) things:

1.  Display B-factors

If you want to see the “b-factor putty” view where the backbone is displayed as a tube with a diameter correlated to the b-factor of the structure, simply click the Action button of the object (the “A” button); mouse to “preset”; and select “b-factor putty.”  The structure will automatically convert to a colored putty view suitable for slide figures or publications.

Create B-factor putty in PyMOL.
Create B-factor putty in PyMOL.

2.  Poisson-Boltzmann electrostatics

Want to get a sense for the patches of positive and negative potential on the surface of your protein?   No need for esoteric PB electrostatics solvers (at least at first!); PyMOL has got you covered.   Click on the “action” button as before, but this time select “generate” and then “vacuum electrostatics.”    Finally select “protein contact potential (local).”

Be sure to heed (or, more typically, ignore) the warning about how the results are qualitative and not quantitative.  And then proceed to enjoy the lovely patches of negative and positive potential rendered on the surface of your protein of interest.

Screen shot 2014-09-22 at 9.45.34 PM

3.  Quickly get an estimate of the solvent-accessible surface area (SASA) of a PDB structure

This takes advantage of the “get_area” command in PyMol.  You’ll have to dive into the command line here to take advantage of this trick.  For example, if you have a PDB object 1UBQ (ubiquitin) you would do the following at the command prompt (the bar below the structure viewing area):

set dot_solvent, 1   ##  set dot_solvent to calculate the SASA

set dot_density, 4  ## most dots for most accurate calculation

get_area, 1UBQ  ## calculate the SASA

Keep in mind that as in point #2, the SASA calculation in PyMOL is an estimate.  For very accurate calculation, use a dedicated SASA solver.

4.  Render a scene as a pen-and-ink sketch

This tip is a lot of fun, because it lets you see your molecules as if they had been drawn in a pen-and-ink manner, like a real cartoon.  This is sometimes useful for presentations or other less-formal venues where you want to clearly illustrate something about your molecular structure.

Once again, we will use the command prompt.  Start off by typing:

set ray_trace_mode, 3


PyMOL will render your scene and display the result.  Here is what it looks like for 1UBQ:

Screen shot 2014-09-29 at 12.44.30 PM

5.  “Rock” and roll

Finally, the last tip is extremely simple.   If you and your colleagues are sitting around looking at a molecule structure, sometimes it helps to view it from different angles.  Instead of twiddling the mouse back and forth, you can simply click the “rock” button at the top right of the PyMOL window to start the scene panning gently from side-to-side to aid visualization.

Please feel free to post any other useful PyMOL tricks and tips in the comments below.

Note:  I am running PyMOL 1.3 Incentive on a Mac OS X 10.6.8 system.

FTMap: fast and free* druggable hotspot prediction

*free to academics

FTMap is a useful and fast online tool that attempts to mimic experimental fragment-screening methodologies (SAR-by-NMR and X-ray crystallography) using in silico methods.   The algorithm is based on the premise that ligand binding sites in proteins often show “hotspots” that contribute most of the free energy to binding.

Often, fragment screening will identify these hotspots when clusters of different types of fragments all bind to the same subsite of a larger binding site.   In fact, x-ray crystallography studies of protein structures solved in a variety of organic solvents demonstrate that small organic fragments often form clusters in active sites.

In the FTMap approach, small organic probes are used for an initial rigid-body docking against the entire protein surface.  The “FT” of FTMap stands for the use of fast Fourier transform (FFT) methods to quickly sample billions of probe positions while calculating accurate energies based on a robust energy expression.

Following docking of each probe, thousands of poses are energy-minimized and clustered based on proximity.  The clusters are then ranked for lowest energy.   Consensus sites (“hot spots”) are determined by looking for overlapping clusters of different types of probes within several angstroms of each other.   If several consensus sites appear near each other on the protein surface, that is a strong indication of a potentially druggable binding site.