Dear All: Thank you very much for you comments and advices.
Now I have a better understanding on this issue. regards Ros On Fri, Apr 27, 2012 at 9:25 AM, Eleanor Dodson <eleanor.dod...@york.ac.uk>wrote: > Two points. > 1) the fit to ideal geometry as flagged in coot validation does not > guarantee a correct model - the best model should be the one that fits the > experimental data best, without having unlikely geometry. You could easily > get a model with perfect geometry which was incorrectly placed in the unit > cell.. > > 2) the AUTO weighting in REFMAC tries to take into account resolution of > the data,and Rfree Have you used that? > > It isn't infallible of course.. > Eleanor > > > On 27 April 2012 10:57, Robbie Joosten <robbie_joos...@hotmail.com> wrote: > >> Hi Uma, >> >> The optimal weight is indeed resolution dependent, but hard to predict. >> In Refmac you can follow LLfree when you optimize the restraint weight and >> also keep an eye on the gap between R and R-free (it should not be too >> wide). Like Rob said, your geometry should be 'reasonable'. This may be a >> bit vague, but there is no clear target for bond/angle rmsd at a given >> resolution (some referees will disagree). If you look at the rmsZ values >> Refmac gives, the target is a bit clearer: rmsZ < 1.000. The average rmsZ >> does go down with resolution (i.e. lower resolution gives lower rmsZ), >> but an ideal value cannot be given easily (or at all). >> Tightening the restraints improves the effective data/parameter ratio of >> your model. You can also improve it by adding additional restrains (e.g. >> NCS restraints) or by removing parameters (e.g. changing the complexity of >> your B-factor model). >> Note that the absence of geometric outliers does not prove that your >> model is optimal. If you use too tight restraints you can end up hiding >> genuine fitting errors. >> >> Cheers, >> Robbie >> >> ------------------------------ >> Date: Fri, 27 Apr 2012 10:04:11 +0200 >> From: herman.schreu...@sanofi.com >> >> Subject: Re: [ccp4bb] Refmac and sigma value >> To: CCP4BB@JISCMAIL.AC.UK >> >> >> It all will depend on the resolution. At low resolution, relaxing the >> geometric restraints will allow the refinement program to tweak the model >> such that the difference between Fobs and Fcalc is minimized, but not that >> the model gets closer to the "truth". I once struggled for a long time with >> a 3.5Åish data set with a protein where the most important feature was >> a rather flexible loop. It was before maximum likelyhood methods and Rfrees >> and the only way I could get rid of the model bias was to use extremely >> tight geometric restraints. The Rfactor would go up, but suddenly the >> electron density maps would no longer accept incorrectly placed side chains >> and new features, not present in the model, would appear. >> >> So my advice: at low resolution use as tight restraints as possible and >> monitor with Rfree if you are going in the right direction. At high or very >> high resolution, you can follow what your diffraction data tells you. In >> fact many very high resolution structures (< 1.5 Å) have higher rmsd's for >> bond lenghts and angles as medium resolution structures. However, at medium >> or low resolution there is not enough data to justify to relax the >> geometric restraints too much. >> >> Best regards, >> Herman >> >> ------------------------------ >> *From:* CCP4 bulletin board [mailto:CCP4BB@JISCMAIL.AC.UK] *On Behalf Of >> *Robert Nicholls >> *Sent:* Friday, April 27, 2012 9:25 AM >> *To:* CCP4BB@JISCMAIL.AC.UK >> *Subject:* Re: [ccp4bb] Refmac and sigma value >> >> Hi Uma, >> >> Altering sigma affects the strength of geometry restraints throughout the >> model - bonds, angles, etc. Choosing a very low sigma will cause geometry >> to be more tightly restrained towards "ideal" values, which is why you >> observe improvements in Coot validation. Note that strengthening the >> geometry weight causes the observations (data) to be less influential in >> refinement. The "risk" of this is that your model may no longer >> appropriately/optimally describe your data. You can assess this locally by >> manual inspection of the electron density, and globally by considering >> overall refinement statistics (as reported at the bottom of the Refmac5 log >> file). Ideally, you want your model to both describe the data and have >> reasonable geometry. >> >> Regards >> Rob >> >> >> On 26 Apr 2012, at 21:26, Uma Ratu wrote: >> >> Hi, Alex: >> >> > Which sigma do you mean? >> >> The one for automatic weight, not for Jelly-body refinement. >> >> I did not turn the "Jelly-body refinement" on. >> >> Thanks >> >> Ros >> >> On Thu, Apr 26, 2012 at 4:08 PM, aaleshin <aales...@burnham.org> wrote: >> >> Hi Uma, >> Which sigma do you mean? The one for Jelly-body refinement? >> J-B sigma=0.01 means very small fraction of the gradient will be used in >> each step. It is used usually with very low resolution (less then 3A) >> >> Alex >> >> On Apr 26, 2012, at 11:38 AM, Uma Ratu wrote: >> >> > >> > Dear All: >> > >> > I use Refmac5 to refine my structure model. >> > >> > When I set the sigma value to 0.3 (as recommended from tutorial), the >> resulted model has many red-bars by coot validation (geometry, rotamer, >> especially, Temp Facotr). >> > >> > I then lower the sigma value to 0.1, the resulted model is much >> improved by coot validation. >> > >> > I then lower the sigma value to 0.01, the resulted model is almost >> perfect, by coot validation and Molprobity. >> > >> > My question is: what is the risk for very low value sigma value? >> > >> > Thank you for your advice >> > >> > Ros >> >> >> >> >
