title: Estimation of the quality of refined protein crystal structures.
abstract: Crystallographic R work and R free values, which are measures of the ability of the models of macromolecular structures to explain the crystallographic data on which they are based, are often used to assess structure quality. It is widely known, and confirmed here that both are sensitive to the methods used to compute them, and can be manipulated to improve the apparent quality of the model. As an alternative it is proposed here that the quality of crystallographic models should be assessed using a global goodness-of-fit metric RO2A /R work where RO2A is the number of reflections used for refinement divided by the number of nonhydrogen atoms in the structure, and R work is the working R-factor of the refined structure. Also, analysis of structures in the Protein Data Bank suggests that many data sets have been truncated at high resolution, thereby improving the R-factor statistics. To discourage this practice, it is proposed that the resolution of a dataset be defined as the resolution of the shell of data where <I/σI > falls to 1. The proposed goodness-of-fit metric encourages investigators to use all the data available rather than a truncated subset.
authors.affiliation: Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, 06520.
authors.forename: Jimin
authors.initials: J
authors.lastname: Wang
mesh_terms: D018360:Crystallography, X-Ray / Q000379:methods*; D030562:Databases, Protein; D008958:Models, Molecular; D015394:Molecular Structure; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D013762:Tetrahydrofolate Dehydrogenase
chemical_list: D011506:Proteins; C006094:R388; D013762:Tetrahydrofolate Dehydrogenase
keywords: data truncation; free R-factors; protein quality; resolution; statistical gaming; statistical manipulation; working R-factors