Re: [phenixbb] Molprobity percentile statistic
Thanks Nat and Pavel for your emails,
Thanks for the suggestion of using phenix.polygon. The comparative
analysis of my structure relative to others at my resolution was what
I was after.
And thats the reason I liked the molprobity statistics expressed as
percentile since it helped me realize how far I have to go relative to
other structures at the PDB. If its not too difficult can I cast my
vote to report it inside the phenix GUI. Its just that running the
full analysis inside of phenix is significantly faster than running it
on the duke webservers.
In molprobity If I remember correctly the overall score is some
weighted average, and the molprobity paper also cautions against
taking it too literally. Also If I remember the molprobity percentiles
are absolute and not only composed of structures at similar
resolutions .
Regardless thanks for the hint about phenix.polygon .
I just looked at my phenix.polygon report and I am afraid its a little
asymmetric.
Hari
On Mon, Oct 19, 2009 at 9:58 PM, hari jayaram
Thanks Nat and Pavel for your emails, Thanks for the suggestion of using phenix.polygon. The comparative analysis of my structure relative to others at my resolution was what I was after.
And thats the reason I liked the molprobity statistics expressed as percentile since it helped me realize how far I have to go relative to other structures at the PDB. If its not too difficult can I cast my vote to report it inside the phenix GUI. Its just that running the full analysis inside of phenix is significantly faster than running it on the duke webservers.
In molprobity If I remember correctly the overall score is some weighted average, and the molprobity paper also cautions against taking it too literally. Also If I remember the molprobity percentiles are absolute and not only composed of structures at similar resolutions .
Regardless thanks for the hint about phenix.polygon . I am attaching a snapshot of my polygon ..I an afraid its a little asymmetric.
Hari
On Mon, Oct 19, 2009 at 7:48 PM, Pavel Afonine
wrote: Hi Hari,
if you run phenix.model_vs_data you will get those numbers (namely: Ramachandran outliers, clashscores, CB-deviations; although you will not get "Molprobity score" simply because I don't know what it is and so I didn't add it):
phenix.model_vs_data model.pdb data.mtz
Nat: every time time you run model_vs_data for whatever purpose (getting fmodel, etc...) you get an object that contains all these numbers.
Pavel.
On 10/19/09 3:32 PM, hari jayaram wrote:
Hi i just started using the phenix GUI front-end to Molprobity . Its really nice the way it plugs into coot and displays all the statistics etc. Is there any place that it also reports the percentile clashscore and percentile Molprobity score for overall model quality a la the webserver at molprobity.biochem.duke.edu
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On Oct 19, 2009, at 7:03 PM, hari jayaram wrote:
And thats the reason I liked the molprobity statistics expressed as percentile since it helped me realize how far I have to go relative to other structures at the PDB. If its not too difficult can I cast my vote to report it inside the phenix GUI. Its just that running the full analysis inside of phenix is significantly faster than running it on the duke webservers.
It isn't very difficult at all - but we need to discuss the best source of these scores. An additional complication in Phenix is that we also incorporate diffraction data into validation (depending on which version of the program you're using), which complements the geometry analyses. One side effect is that only PDB entries for which structure factors are available are included in the distributions used by Polygon. (This probably improves the distributions, however.)
Regardless thanks for the hint about phenix.polygon . I just looked at my phenix.polygon report and I am afraid its a little asymmetric.
Since the server bounced the original post I've saved the image you sent here (since it's a good example): http://cci.lbl.gov/~nat/img/phenix/polygon_example.png The colors are a bit confusing (next release will be improved), but this is about what I'd expect to see. What you should look out for is values that fall on the extremed ends of the distribution, or very lopsided or pointy shapes. I can't really suggest absolute guidelines since these statistics are so different and the PDB won't (and shouldn't) have a Gaussian distribution for each one - and keep in mind that most models in the PDB would probably benefit from further refinement. I find this type of analysis very informative, but we don't want people to fall into the trap of leaving obvious errors in the model because they only looked at global scores. Anyway, I've added some more similar graphs for surveying PDB statistics in the GUI - they'll be in the next version. -Nat ------------------- Nathaniel Echols Lawrence Berkeley Lab 510-486-5136 [email protected]
participants (2)
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hari jayaram
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Nathaniel Echols