Frederic VELLIEUX wrote:
Anisotropy in the diffraction pattern could simply be due to the shape of the
crystals. The intensity of diffraction is a function of the volume of
diffracting matter that is hit by the X-ray beam. Think for example of a thin
plate crystal, which you rotate in the X-ray beam. When the plate is
perpendicular to the X-ray beam, the volume of matter hit by the X-rays is much
smaller than when the plate is parallel to the X-ray beam.
Fred, I think this is a bit misleading.
Although diffraction anisotropy is often accompanied by platy or
needle-y crystals, the crystal shape is neither necessary nor sufficient
to produce an anisotropic B factor. You will get an anisotropic SCALE
factor if bits of the crystal are moving in and out of the beam as you
describe, but scale factors and B factors are not the same thing! B
factors arise from differences between neighboring unit cells (within a
few microns of each other), and anisotropic B factors arise when the
average displacement of atoms from their ideal lattice points is higher
in one direction than another. Admittedly, both scale and B can have an
effect on the resolution limit, but the latter kills high-angle spots
much more rapidly than the former.
Operationally, I recommend treating anisotropic data just like isotropic
data. There is nothing wrong with measuring a lot of zeros (think about
systematic absences), other than making irrelevant statistics like
Rmerge higher. One need only glance at the formula for any R factor to
see that it is undefined when the "true" F is zero. Unfortunately,
there are still a lot of reviewers out there who were trained that "the
Rmerge in the outermost resolution bin must be 20%", and so some very
sophisticated ellipsoidal cut-off programs have been written to try and
meet this criterion without throwing away good data. I am actually not
sure where this idea came from, but I challenge anyone to come up with a
sound statistical basis for it. Better to use I/sigma(I) as a guide, as
it really does tell you how much "information vs noise" you have at a
given resolution.
-James Holton
MAD Scientist
