I will agree with Ulrich. Even at 3.0 A, it is possible to have a structure with reasonable accuracy which can explain the biological function/ or is consistent with available biochemical data. Ranvir --- Ulrich Genick <[EMAIL PROTECTED]> wrote:
> Here are my 2-3 cents worth on the topic: > > The first thing to keep in mind is that the goal of > a structure > determination > is not to get the best stats or to claim the highest > possible > resolution. > The goal is to get the best possible structure and > to be confident that > observed features in a structure are real and not > the result of noise. > > From that perspective, if any of the conclusions > one draws from a > structure > change depending on whether one includes data with > an I/sigI in the > highest > resolution shell of 2 or 1, one probably treads on > thin ice. > > The general guide that one should include only data, > for which the > shell's average > I/sigI > 2 comes from the following simple > consideration. > > > F/sigF = 2 I/sigI > > So if you include data with an I/sigI of 2 then your > F/sigF =4. In > other words you will > have a roughly 25% experimental uncertainty in your > F. > Now assume that you actually knew the structure of > your protein and > you would > calculate the crystallographic R-factor between the > Fcalcs from your > true structure and the > observed F. > In this situation, you would expect to get a > crystallographic R- > factor around 25%, > simply because of the average error in your > experimental structure > factor. > Since most macromolecular structures have R-factors > around 20%, it > makes little > sense to include data, where the experimental > uncertainty alone will > guarantee that your R-factor will be worse. > Of course, these days maximum-likely-hood refinement > will just down > weight > such data and all you do is to burn CPU cycles. > > > If you actually want to do a semi rigorous test of > where you should stop > including data, simply include increasingly higher > resolution data in > your > refinement and see if your structure improves. > If you have really high resolution data (i.e. > better than 1.2 Angstrom) > you can do matrix inversion in SHELX and get > estimated standard > deviations (esd) > for your refined parameters. As you include more and > more data the > esds should > initially decrease. Simply keep including higher > resolution data > until your esds > start to increase again. > > Similarly, for lower resolution data you can monitor > some molecular > parameters, which are not > included in the stereochemical restraints and see, > if the inclusion > of higher-resolution data makes the > agreement between the observed and expected > parameters better. For > example SHELX does not > restrain torsion angles in aliphatic portions of > side chains. If your > structure improves, those > angles should cluster more tightly around +60 -60 > and 180... > > > > > Cheers, > > Ulrich > > > > Could someone point me to some standards for data > quality, > > especially for publishing structures? I'm > wondering in particular > > about highest shell completeness, multiplicity, > sigma and Rmerge. > > > > A co-worker pointed me to a '97 article by > Kleywegt and Jones: > > > > http://xray.bmc.uu.se/gerard/gmrp/gmrp.html > > > > "To decide at which shell to cut off the > resolution, we nowadays > > tend to use the following criteria for the highest > shell: > > completeness > 80 %, multiplicity > 2, more than > 60 % of the > > reflections with I > 3 sigma(I), and Rmerge < 40 > %. In our opinion, > > it is better to have a good 1.8 Å structure, than > a poor 1.637 Å > > structure." > > > > Are these recommendations still valid with maximum > likelihood > > methods? We tend to use more data, especially in > terms of the > > Rmerge and sigma cuttoff. > > > > Thanks in advance, > > > > Shane Atwell > > > ____________________________________________________________________________________ TV dinner still cooling? Check out "Tonight's Picks" on Yahoo! TV. http://tv.yahoo.com/
