I am creating a file for the ligand by generating the omit map in order to visualize it into pymol. Does anyone have any explanation what the Refmac sigma-AA weighted map data is, and why it is necessary? Thanks in advance !
Hi Prem,
The "m" is the figure of merit (quality) of the phase information for a reflection and as Peter says, weights down the contributions of reflections with a lot of error. The "D" multiplies the structure factor calculated from the model (Fc) and the product is a good estimate of the correct part of Fc. This gives 2mFo-DFc instead of the conventional 2Fo-Fc coefficients for a reduced-bias electron density map. Overall the sigmaA-weighted map is a much better map to use than a 2Fo-Fc map.
the sigmaA weights down contributions of reflections with low expected phase error, it reduces noise in the maps.
Dear Peter,
Can you please elaborate your explanation, I will be more clear if it would have little bit more explanatory. Thank you so much for your reply.
Hi Prem,
The "m" is the figure of merit (quality) of the phase information for a reflection and as Peter says, weights down the contributions of reflections with a lot of error. The "D" multiplies the structure factor calculated from the model (Fc) and the product is a good estimate of the correct part of Fc. This gives 2mFo-DFc instead of the conventional 2Fo-Fc coefficients for a reduced-bias electron density map. Overall the sigmaA-weighted map is a much better map to use than a 2Fo-Fc map.
The quantity sigmaA and D are very similar, I think sigmaA arises when using normalized structure factors. As Tom said, D can be seen as the fraction of the model that is correct. The 'uncorrect part' is a random component that introduces noise. Once you estimate what D is (or sigmaA), you know the expected noise levels and can suppress its contribution to the total map. m, the figure of merit is the estimated cosine of the phase error.
I highly recommend reading Randy Read's early papers as well as Srinivasan's work that describe this theory. The book 'some statistical applications in X-ray crystallography' is ok as well. A bit more light-weight reading, and likely the best place to start, is this webpage:
http://www-structmed.cimr.cam.ac.uk/Course/Statistics/statistics.html
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