Crystallographic Error Analysis.
A method to detect problem residues in a crystallographic refinement
is presented.
Each protein residue is analyzed in terms
of mainchain (mc) and sidechain (sc) atoms.
Poorly modeled regions in the structure are reliably identified.
The results may be plotted or viewed with ribbons .
Background
Reference
For background on the method and for purposes of citation
the following reference is given:
M. Carson, T.W. Buckner, Z. Yang, S.V.L. Narayana and C.E. Bugg (1994)
Error Detection in Crystallographic Models.
Acta Cryst. D 50:900-909.
Abstract
A variety of criteria were tested for identifying errors
in protein crystal coordinates.
Statistical analysis was based on comparisons of
a highly refined crystal structure
and several preliminary models derived from molecular replacement.
A protocol employing temperature factors, real-space fit residuals,
geometric strains, dihedral angles, and shifts from the previous
refinement cycle is developed.
These results are generally applicable to the detection of
errors in partially refined protein crystal structures.
Key Points
Crystallographic data is required to reliably
assess the quality of a coordinate file.
Statistical analysis implies that a linear model
of 5 independent variables (see abstract) is required to fit the error.
The error model was taken as the deviation between the preliminary
coordinates and the final refined coordinates.
Grossly incorrect residues (1.0A deviation) are identified
with approximately 90% accuracy for the mainchain
and 70% for the sidechains.
Real-space fit using maps calculated with individual B-factors was
the single criteria having the highest per-residue
correlation with coordinate error (about 0.7).
Executing the Analysis
(re-working for WHATIF/CNS - let me know if interested)
Ribbons User Manual / UAB-CBSE / carson@uab.edu