Thanks, Pavel, I will read and study the paper. Jorge On 1/27/21 5:04 PM, Pavel Afonine wrote:
Hi Jorge,
"tncs_correction = True" is functional, somewhat tested and overall supposed to work. However, we have not done any large scale systematic tests to evaluate what exactly benefits are.
As to "what does this actually do?" -- this option treats the presence of tNCS as described here:
https://doi.org/10.1107/S0907444912045374
Pavel
On 1/27/21 02:46, Jorge Iulek wrote:
Dear all,
I have a dataset at 2.98 A resolution, images processed to CC_1/2 at circa 50% in the last resolution shell, tetramer in the asymmetric unit, local (rotational) symmetry 222. Xtriage indicates a peak in the Patterson function at 20% height of origin peak, at fractional coordinates of 0.0000 0.0000 0.3177. Space group is P21.
I am aware that this brings problems to refinement , as even stated at xtriage output "Translational pseudo-symmetry is very likely present in these data. Be aware that this will change the intensity statistics and may impact subsequent analyses, and in practice may lead to higher R-factors in refinement.", and so I can't lower my R and R-free below ~31 and 36 % , respectively.
At phenix.refine, setting "tncs_correction = True" brings little benefit (what does this actually do? points to references are welcome). I wonder nevertheless if some strategy might help in on lowering these R's and, of course, allow me to build a better model (in spite of the resolution). I tried some combinations of other types of ncs and tls groups, with little success. Particularly, it seems that the refinement of ADPs are a kind of unstable (what I would understand come from the - partial - modular nature of the intensities - due to tncs - and these - ADP - would be reflected by the intensity fall-off with resolution). What about refining ADPs only to a zone which is not affect by data modulation? How to do that? Any other ideas?
Thanks,
Jorge
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