Dear Toon,

I think some of your questions are addressed by the work

Praznikar, J. & Turk, D. (2014) Free kick instead of cross-validation in maximum-likelihood refinement of macromolecular crystal structures. Acta Cryst. D70, 3124-3134

(that does not say how to validate the result without the R-free but shows how to get this result).

Please look it.

Note that the authors talk about ML and not about LS refinement; I wonder why you need to use LS.

Best regards,

Sacha Urzhumtsev

De : [email protected] [mailto:[email protected]] De la part de Toon Van Thillo
Envoyé : mercredi 4 avril 2018 11:48
À : [email protected]
Objet : [phenixbb] Rfree and a low resolution data set

Hi all,

Currently I am refining a data set which showed anisotropic diffraction. Aimless suggested cutoffs at 2.3, 2.6 and 3.6 angstrom for the h,k and l axis.

I chose a general 3.6 cutoff to obtain satisfactory statistics for Rmeas, I/sd(I) and CC1/2. At this resolution the data set consists of approximately 2800 reflections.

Generally 5% of the set is set aside as the Rfree test set and I found that a minimum of 500 reflections in total is used to produce a reliable Rfree. However, 5% only amounts to 140 reflections in this case. I am hesitant to include more reflections as I would have to go up to 20% of the reflections to obtain more than 500 reflections for the test set. In a discussion on the CCP4 message boards some time ago it was suggested to do multiple refinements with different test sets:

https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind1411&L=ccp4bb&F=&S=&P=125570

In the thread it was also discussed that a least squares approach is prefered when using a small test set. However, when using a LS target, the resulting Rfree is very high (10% higher than when using the automatic option) and phenix.refine produces awful geometry (24% ramachandran outliers, 105 clashcore...). It seems that the refinement is performed without restraints? Optimize X-ray/stereochemistry weight does not result in improved stereochemistry. My question is if the LS approach is still relevant and if so, is there an explanation (and solution) for the bad statistics?

Kind regards,

Toon Van Thillo