Hello,

just to point out  that CC alone may not be adequate measure, as we show here:

Acta Cryst. (2014). D70, 2593-2606.
Metrics for comparison of crystallographic maps

In addition, a histogram of per-atom correlations along with a histogram of actual map values per-atom may be useful.

Pavel

On 10/9/14 10:30 AM, Tim Gruene wrote:
Dear S,

a CC of 0.49 is quite strong, unless you mean 0.49% - did you take a
look at the resulting pdb-file?

Best,
Tim

On 10/09/2014 06:56 PM, Sneha Rangarajan wrote:

Hello everyone,

I have a question about ligand fitting into density.
At this point my maps look quite good with decent density for the peptide (ligand)[Rfactprs 26/31].

I tried using ligandfit by giving it the pdb and mtz of the ligand free model along with peptide.pdb (peptide stripped from a pdb where it was complexed with a homologous protein).
However the output was a ligand.pdb file with a CC of 0.49. I am not sure how to interpret this. Does this mean it could not find the density for the ligand?

Is there a better way to fit the peptide into density?

Thanks,
S


From: [email protected] [mailto:[email protected]] On Behalf Of Sneha Rangarajan
Sent: Wednesday, October 08, 2014 10:30 AM
To: Nathaniel Echols
Cc: [email protected]
Subject: Re: [phenixbb] (no subject)

This was a great idea. My Rfactors after a second round of autobuild are now 25/32. I think it might be getting there afterall ☺

S

From: Nathaniel Echols [mailto:[email protected]]
Sent: Friday, October 03, 2014 3:08 PM
To: Sneha Rangarajan
Cc: Pavel Afonine; [email protected]<mailto:[email protected]>
Subject: Re: [phenixbb] (no subject)

On Fri, Oct 3, 2014 at 11:58 AM, Sneha Rangarajan <[email protected]<mailto:[email protected]>> wrote:
I did another round of refinement with default settings (XYZ,realsp, IndB and occ) with and without weight optimization.
Without weight opt, the Rfactors are 23/36 with RMSbonds-0.0108 and RMSangles-1.750

One idea would be to run AutoBuild again.  I've seen cases before where it didn't converge using the default settings, and feeding a previous result back into the program for a second run produced significantly better models.  It might help get rid of the overfitting.

-Nat



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