So let's say I take a 0.6 Ang structure, artificially introduce noise into corresponding Fobs to make the resolution go down to 2 Ang, and refine using the 0.6 Ang model--do I actually get R's better than the artificially-inflated sigmas? Or let's say I experimentally decrease I/sigma by attenuating the beam and collect another data set--same situation?
JPK ----- Original Message ----- From: Bart Hazes To: [email protected] Sent: Thursday, October 28, 2010 4:13 PM Subject: Re: [ccp4bb] Against Method (R) There are many cases where people use a structure refined at high resolution as a starting molecular replacement structure for a closely related/same protein with a lower resolution data set and get substantially better R statistics than you would expect for that resolution. So one factor in the "R factor gap" is many small errors that are introduced during model building and not recognized and fixed later due to limited resolution. In a perfect world, refinement would find the global minimum but in practice all these little errors get stuck in local minima with distortions in neighboring atoms compensating for the initial error and thereby hiding their existence. Bart On 10-10-28 11:33 AM, James Holton wrote: It is important to remember that if you have Gaussian-distributed errors and you plot error bars between +1 sigma and -1 sigma (where "sigma" is the rms error), then you expect the "right" curve to miss the error bars about 30% of the time. This is just a property of the Gaussian distribution: you expect a certain small number of the errors to be large. If the curve passes within the bounds of every single one of your error bars, then your error estimates are either too big, or the errors have a non-Gaussian distribution. For example, if the noise in the data somehow had a uniform distribution (always between +1 and -1), then no data point will ever be "kicked" further than "1" away from the "right" curve. In this case, a data point more than "1" away from the curve is evidence that you either have the wrong model (curve), or there is some other kind of noise around (wrong "error model"). As someone who has spent a lot of time looking into how we measure intensities, I think I can say with some considerable amount of confidence that we are doing a pretty good job of estimating the errors. At least, they are certainly not off by an average of 40% (20% in F). You could do better than that estimating the intensities by eye! Everybody seems to have their own favorite explanation for what I call the "R factor gap": solvent, multi-confomer structures, absorption effects, etc. However, if you go through the literature (old and new) you will find countless attempts to include more sophisticated versions of each of these hypothetically "important" systematic errors, and in none of these cases has anyone ever presented a physically reasonable model that explained the observed spot intensities from a protein crystal to within experimental error. Or at least, if there is such a paper, I haven't seen it. Since there are so many possible things to "correct", what I would like to find is a structure that represents the transition between the "small molecule" and the "macromolecule" world. Lysozyme does not qualify! Even the famous 0.6 A structure of lysozyme (2vb1) still has a "mean absolute chi": <|Iobs-Icalc|/sig(I)> = 4.5. Also, the 1.4 A structure of the tetrapeptide QQNN (2olx) is only a little better at <|chi|> = 3.5. I realize that the "chi" I describe here is not a "standard" crystallographic statistic, and perhaps I need a statistics lesson, but it seems to me there ought to be a case where it is close to 1. -James Holton MAD Scientist On Thu, Oct 28, 2010 at 9:04 AM, Jacob Keller <[email protected]> wrote: So I guess there is never a case in crystallography in which our models predict the data to within the errors of data collection? I guess the situation might be similar to fitting a Michaelis-Menten curve, in which the fitted line often misses the error bars of the individual points, but gets the overall pattern right. In that case, though, I don't think we say that we are inadequately modelling the data. I guess there the error bars are actually too small (are underestimated.) Maybe our intensity errors are also underestimated? JPK On Thu, Oct 28, 2010 at 9:50 AM, George M. Sheldrick <[email protected]> wrote: > > Not quite. I was trying to say that for good small molecule data, R1 is > usally significantly less than Rmerge, but never less than the precision > of the experimental data measured by 0.5*<sigmaI>/<I> = 0.5*Rsigma > (or the very similar 0.5*Rpim). > > George > > Prof. George M. Sheldrick FRS > Dept. Structural Chemistry, > University of Goettingen, > Tammannstr. 4, > D37077 Goettingen, Germany > Tel. +49-551-39-3021 or -3068 > Fax. +49-551-39-22582 > > > On Thu, 28 Oct 2010, Jacob Keller wrote: > >> So I guess a consequence of what you say is that since in cases where there is >> no solvent the R values are often better than the precision of the actual >> measurements (never true with macromolecular crystals involving solvent), >> perhaps our real problem might be modelling solvent? >> Alternatively/additionally, I wonder whether there also might be more >> variability molecule-to-molecule in proteins, which we may not model well >> either. >> >> JPK >> >> ----- Original Message ----- From: "George M. Sheldrick" >> <[email protected]> >> To: <[email protected]> >> Sent: Thursday, October 28, 2010 4:05 AM >> Subject: Re: [ccp4bb] Against Method (R) >> >> >> > It is instructive to look at what happens for small molecules where >> > there is often no solvent to worry about. They are often refined >> > using SHELXL, which does indeed print out the weighted R-value based >> > on intensities (wR2), the conventional unweighted R-value R1 (based >> > on F) and <sigmaI>/<I>, which it calls R(sigma). For well-behaved >> > crystals R1 is in the range 1-5% and R(merge) (based on intensities) >> > is in the range 3-9%. As you suggest, 0.5*R(sigma) could be regarded >> > as the lower attainable limit for R1 and this is indeed the case in >> > practice (the factor 0.5 approximately converts from I to F). Rpim >> > gives similar results to R(sigma), both attempt to measure the >> > precision of the MERGED data, which are what one is refining against. >> > >> > George >> > >> > Prof. George M. Sheldrick FRS >> > Dept. Structural Chemistry, >> > University of Goettingen, >> > Tammannstr. 4, >> > D37077 Goettingen, Germany >> > Tel. +49-551-39-3021 or -3068 >> > Fax. +49-551-39-22582 >> > >> > >> > On Wed, 27 Oct 2010, Ed Pozharski wrote: >> > >> > > On Tue, 2010-10-26 at 21:16 +0100, Frank von Delft wrote: >> > > > the errors in our measurements apparently have no >> > > > bearing whatsoever on the errors in our models >> > > >> > > This would mean there is no point trying to get better crystals, right? >> > > Or am I also wrong to assume that the dataset with higher I/sigma in the >> > > highest resolution shell will give me a better model? >> > > >> > > On a related point - why is Rmerge considered to be the limiting value >> > > for the R? Isn't Rmerge a poorly defined measure itself that >> > > deteriorates at least in some circumstances (e.g. increased redundancy)? >> > > Specifically, shouldn't "ideal" R approximate 0.5*<sigmaI>/<I>? >> > > >> > > Cheers, >> > > >> > > Ed. >> > > >> > > >> > > >> > > -- >> > > "I'd jump in myself, if I weren't so good at whistling." >> > > Julian, King of Lemurs >> > > >> > > >> >> >> ******************************************* >> Jacob Pearson Keller >> Northwestern University >> Medical Scientist Training Program >> Dallos Laboratory >> F. Searle 1-240 >> 2240 Campus Drive >> Evanston IL 60208 >> lab: 847.491.2438 >> cel: 773.608.9185 >> email: [email protected] >> ******************************************* >> >> > -- ============================================================================ Bart Hazes (Associate Professor) Dept. of Medical Microbiology & Immunology University of Alberta 1-15 Medical Sciences Building Edmonton, Alberta Canada, T6G 2H7 phone: 1-780-492-0042 fax: 1-780-492-7521 ============================================================================ ******************************************* Jacob Pearson Keller Northwestern University Medical Scientist Training Program Dallos Laboratory F. Searle 1-240 2240 Campus Drive Evanston IL 60208 lab: 847.491.2438 cel: 773.608.9185 email: [email protected] *******************************************
