Tables for
Volume F
Crystallography of biological macromolecules
Edited by M. G. Rossmann and E. Arnold

International Tables for Crystallography (2006). Vol. F. ch. 21.1, p. 498   | 1 | 2 |

Section Classes of quality indicators

G. J. Kleywegta*

aDepartment of Cell and Molecular Biology, Uppsala University, Biomedical Centre, Box 596, SE-751 24 Uppsala, Sweden
Correspondence e-mail: Classes of quality indicators

| top | pdf |

Many statistics, methods and programs were developed in the 1990s to help identify errors in protein models. These methods generally fall into two classes: one in which only coordinates and B factors are considered (such methods often entail comparison of a model to information derived from structural databases) and another in which both the model and the crystallographic data are taken into account. Alternatively, one can distinguish between methods that essentially measure how well the refinement program has succeeded in imposing restraints (e.g. deviations from ideal geometry, conventional R value) and those that assess aspects of the model that are `orthogonal' to the information used in refinement (e.g. free R value, patterns of non-bonded interactions, conformational torsion-angle distributions). An additional distinction can be made between methods that provide overall (global) statistics for a model (such methods are suitable for monitoring the progress of the refinement and rebuilding process) and those that provide information at the level of residues or atoms (such methods are more useful for detecting local problems in a model). It is important to realise that almost all coordinate-based validation methods detect outliers (i.e. atoms or residues with unusual properties): to assess whether an outlier arises from an error in the model or whether it is a genuine, but unusual, feature of the structure, one must inspect the (preferably unbiased) electron-density maps (Jones et al., 1996[link])!

In this section, some quality indicators will be discussed that have been found to be particularly useful in daily protein crystallographic practice for the purpose of detecting problems in intermediate models. Section 21.1.7[link] provides a more extensive discussion of many of the quality criteria that are or have been used by macromolecular crystallographers.


First citation Jones, T. A., Kleywegt, G. J. & Brünger, A. T. (1996). Storing diffraction data. Nature (London), 381, 18–19.Google Scholar

to end of page
to top of page