[chimerax-users] Automated Fitting of models into maps and saving fits with a correlation score > X

[Vorländer,Matthias Kopano]

Hi Tom,

Thanks so much, that's amazing!

To be clear, I am not searching tomograms, I am searching artificial tomograms that contain perfect "molmaps" of my protein of interest, so they are free of noise. Trying this in Chimera gave very good fits when asking for for enough initial placement.

Thanks a lot for your help,
Best,
Matthias

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________________________________
From: Tom Goddard <goddard at sonic.net>
Sent: Tuesday, July 27, 2021 12:15:13 AM
To: Vorländer,Matthias Kopano <matthias.vorlaender at imp.ac.at>
Cc: chimerax-users at cgl.ucsf.edu <chimerax-users at cgl.ucsf.edu>
Subject: Re: [chimerax-users] Automated Fitting of models into maps and saving fits with a correlation score > X

Hi Matthias,

  You can run ChimeraX command scripts or Python scripts without a GUI, just start it at the shell with the "--nogui" option, for example

chimerax --nogui mycommands.cxc

I made some example Python code that runs fitmap search and saves PDB files for each unique fit with correlation over 0.9.

https://rbvi.github.io/chimerax-recipes/fit_search/fit_search.html

I don't expect this to be a good way to search tomograms.  It will be very inefficient.  Template matching code designed for that problem is likely to work much better.  Such code usually uses Fourier space methods to search all translations at once, so it can be much faster and more thorough.  Even with that optimization it takes a long time to do the rotational search.

  Tom

Example search of monomers in 14-mer GroEL.

[cid:C18A5B82-FCB0-4D8A-ADF0-400431504487]


On Jul 25, 2021, at 9:05 AM, Vorländer,Matthias Kopano via ChimeraX-users <chimerax-users at cgl.ucsf.edu<mailto:chimerax-users at cgl.ucsf.edu>> wrote:

Dear all,

I have a set of 100  “artificial” tomograms, each containing approx 100 copies of a simulated density map of my protein of interest. I would like to fit each tomogram with the PDB of my POI, and save copies for all fits with a correlation score > 90%. Using the fitmap command in Chimera and then manually selecting the fits that meet the criterium works, but takes quiet a long time for a larger amount of data. I wonder if this could be automated, and if somehow would be able me to put together a script (is it possible to execute such tasks in a  non-gui way, by submitting it to a cluster so that it could be parallelized?).

Thanks a lot in advance,
Best,
Matthias
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