Publication Abstract from PubMed
In macromolecular crystallography the agreement between observed and predicted structure factors (Rcryst and Rfree ) is seldom better than 20%. This is much larger than the estimate of experimental error (Rmerge ). The difference between Rcryst and Rmerge is the R-factor Gap. There is no such gap in small-molecule crystallography where calculated structure factors are generally considered more accurate than the experimental measurements. Perhaps the true noise level of macromolecular data is higher than expected? Or is the gap caused by inaccurate phases that trap refined models in local minima? By generating simulated diffraction patterns with the program MLFSOM and including every conceivable source of experimental error, we show that neither of these are the case. Processing our simulated data yields indistinguishable values to real data for all crystallographic statistics except one: the final Rcryst and Rfree . These values drop to 3.8% and 5.5% for simulated data, suggesting that the reason for high R-factors in macromolecular crystallography is neither experimental error nor phase bias, but rather an underlying inadequacy in the models we use to explain our observations. Current inabilities to accurately represent the entire macromolecule with both its flexibility and its protein and solvent environment may be improved by synergies between X-ray scattering (SAXS), computation, and crystallography. The exciting implication of this result is that macromolecular data contains substantial hidden, and as yet, untapped potential to resolve ambiguities in the true nature of the nanoscale, a task that the second century of crystallography promises to fulfill.
The R-factor gap in macromolecular crystallography: an untapped potential for insights on accurate structures.,Holton JM, Classen S, Frankel KA, Tainer JA FEBS J. 2014 Jul 9. doi: 10.1111/febs.12922. PMID:25040949[1]
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.
