On Mon, May 31, 2010 at 9:15 PM, Dale Tronrud <[email protected]> wrote: > One of the great mysteries of refinement is that a model created using > high resolution data will fit a low resolution data set much better than > a model created only using the low resolution data. It appears that there > are many types of errors that degrade the fit to low resolution data that > can only be identified and fixed by using the information from high > resolution data.
Is it such a mystery? Isn't it just a case of overfitting to the experimental errors in the low res data if you tried to use the same parameterization & restraint weighting as for the high res refinement? Consequently you are forced to use fewer parameters and/or higher restraint weighting at low res which obviously is not going to give as good a fit. Cheers -- Ian
