Yes, but even the high-resolution structures cannot explain THEIR data to within experimental error. You can see this if you download the CIF file for one of the highest-resolution structures there is: 2vb1 (triclinic lysozyme at 0.6 A), which contains both I and FC: http://www.rcsb.org/pdb/download/downloadFile.do?fileFormat=STRUCTFACT&compression=NO&structureId=2VB1 I even had Z. Dauter send me the original image files for this one, and I don't think it will surprise anyone to hear that I think he did it right. Nevertheless, the average value of |Iobs-Icalc| / sigma(Iobs) is 4.5 for this structure.
Also, if I take "data" from MLFSOM-simulated diffraction images (including anomalous scattering, absorption, shutter jitter, background, etc.) and set ARP/wARP to work building the model, starting from the "SAD-phased map", it inevitably converges to R/Rfree of around 6-7%, even at 2 A resolution. For the real data, however, it never gets below 18%. This is actually not all that remarkable a result, because the "Fobs" from the fake data is actually only ~5% different from Fcalc from the PDB file I put into the simulation. (I did not provide this PDB to ARP/wARP!) Add this to the fact that if your model is "close", but missing a few bits, then those missing bits light up in a Fo-Fc map (like the tail on Kevin Cowtan's cat). These differnece features get BETTER as the model becomes more complete, and in small molecule structures adding in difference features eventually leads to R1 < R(merge) (using Sheldrick's notation from below.) What I don't understand is why protein structures don't "converge" like this. Yes, there are low-occupancy features: Fraser et al. 2009: http://dx.doi.org/10.1038/nature08615 Lang et al. 2010: http://dx.doi.org/10.1002/pro.423 but even if you model these in, the R factor only drops a few percent: van den Bedem et al. 2009: http://dx.doi.org/10.110/S0907444909030613 -James Holton MAD Scientist On Thu, Oct 28, 2010 at 2:13 PM, Bart Hazes <bart.ha...@ualberta.ca> wrote: > 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 < > j-kell...@fsm.northwestern.edu> 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 >> <gshe...@shelx.uni-ac.gwdg.de> 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" >> >> <gshe...@shelx.uni-ac.gwdg.de> >> >> To: <CCP4BB@JISCMAIL.AC.UK> >> >> 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: j-kell...@northwestern.edu >> >> ******************************************* >> >> >> >> >> > >> > > > -- > > ============================================================================ > > 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 > > ============================================================================ > >
