Hi Tom, Thank you for explaining the inner workings of the volume-volume fitting in Chimera. The long term goal is to set up a searchable database with what volumes fit in each other, not necessarily limited to EM, and also provide a service where a user can upload a volume and see if it fits in any existing volume in the archive. It sounds like I am used a good set options. I will try some other examples to see if I have better luck in finding the best fit there, may be a set of 70 S ribosomes. /Ingvar On 2013-04-24 06:09, Tom Goddard wrote:
Hi Ingvar,
It sounds like you want to automate the fitting of one EMDB ribosome map to another, and probably other structures too. Probably you are aiming to have it find the best fit as often as possible without taking too long. It is not easy to guarantee you have the best fit, even using an exhaustive search can miss it unless you take very fine rotational and translation steps. Here are answers to some of your questions.
Should one map be resampled before fitting in the other map? No, I don't see any advantage to doing that. The fit is done on the grid points of the first map within the displayed contour level, and the second map is interpolated at those grid points.
Could aligning principle inertia axes help get the best fit. Yes, in some cases that will work. And if you only optimize that initial it will be very fast (less than a second typically). But unless you search lots of other possible orientations you will miss the best fit in some percentage of cases. So it doesn't seem particularly useful if reliability of automated fitting is your goal.
Does it matter if grid sizes are different? No. Trilinear interpolation is being used, so there is no advantage of the grid sizes or spacings matching.
Should any correction be done for fitting different resolution maps to each other? I don't think there would be much advantage to say smoothing the higher resolution map to match the lower resolution map. I don't think that will improve convergence to the best fit. And I don't think it will change the best fit -- although maybe it is theoretically possible.
If you are using the correlation about mean metric then shifting the density values will have no effect, because the mean is subtracted from the density of both maps. But if you are using correlation or overlap a shift may be needed.
I am unpleasantly surprised by your low success rate getting the best fit in 5000 or 50000 searched positions. My experience has been that it is found in a few hundred positions. I will need to try your examples. I'll report in a later email what I find.
Tom
On Apr 22, 2013, at 3:37 AM, ingvar wrote:
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
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