[Chimera-users] Seeking guidance on Volume-volume fitting

[ingvar]

Dear Chimera,

I am seeking guidance on what would be best practice in doing 
volume-volume fitting, and what expectations should I have on number of 
trial conformations.  For instance would it be beneficial to resample 
one of the volumes to the same grid as the other volume.  What about 
volumes with different resolutions, do this require any special 
handling.
In the case below it would probably be more efficient to just align the 
principal moments of inertia of the envelopes above the contour level 
and then do a search from there, though this will only work well if the 
volumes are very similar.

In an initial study I used 3 EM volumes from EMDB, (EMD-5500, EMD-5501, 
and EMD-2017).  They are all 30S E. coli ribosomes of similar resolution 
(12.9, 14.0, 13.5 Å).  I thought that this would be a relatively easy 
starting point, with the intent to then go on to fit 30S subunit in some 
of the many E. coli 70S EM volumes that are available.
The volumes EMD-5500 and EMD-5501 are in similar orientation, while 
EMD-2017 is in a completely different orientation.
The grids are similar but not identical, 125^3 vs 128^3.  I adjusted 
the contour levels from the EMDB recommended values to make the enclosed 
volumes more similar in size.

Contour levels used:
EMD-5500 -2.8 -> -2.5
EMD-5501 -2.8
EMD-2017 39 -> 32

I am using scripts like the one shown here:

from chimera import runCommand as rc
from chimera.tkgui import saveReplyLog
rc("open data/EMD-5500.map")
rc("volume #0 level -2.5 transparency 0.5")
rc("open data/EMD-5501.map")
rc("volume #1 level -2.8 transparency 0.5")
rc("fitmap #1 #0 search 50000 metric cam envelope true inside 0.2")
saveReplyLog(r'/Users/ingvar/chimera/log/fit5500_5501_loc.txt')

It seems correlation about mean is the best metric for volume-volume 
fitting, and that it is best to only use points inside the envelope.

The maps EMD-5500, and EMD-5501 have the unusual feature that the 
density range is shifted downwards so that the average is well below 0.  
I was concerned about that and moved the density range, but with the 
fitmap parameters above that did not seem to have any significant impact 
(with other sets of parameters the density range appears to be an 
issue).

In this case, it is relatively easy to see when you have found the 
"good" fit, all other fits have clearly worse statistics.

What I was surprised about was the number of trial conformations needed 
to be reasonable certain to find the "good" fit.

5500 in 5501 7 times in 5000
5501 in 5500 2 times in 5000
5500 in 2017 0 times in 5000
5500 in 2017 8 times in 50000
2017 in 5501 0 times in 5000

Many Thanks,
Ingvar Lagerstedt