On Mon, May 31, 2010 at 9:15 PM, Dale Tronrud <[log in to unmask]> 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
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