SFCHECK

SFCHECK: a unified set of procedures for evaluating the quality of macromolecular structure-factor data and their agreement with the atomic model. In this paper we present SFCHECK, a stand-alone software package that features a unified set of procedures for evaluating the structure-factor data obtained from X-ray diffraction experiments and for assessing the agreement of the atomic coordinates with these data. The evaluation is performed completely automatically, and produces a concise PostScript pictorial output similar to that of PROCHECK [Laskowski, MacArthur, Moss & Thornton (1993). J. Appl. Cryst. 26, 283-291], greatly facilitating visual inspection of the results. The required inputs are the structure-factor amplitudes and the atomic coordinates. Having those, the program summarizes relevant information on the deposited structure factors and evaluates their quality using criteria such as data completeness, structure-factor uncertainty and the optical resolution computed from the Patterson origin peak. The dependence of various parameters on the nominal resolution (d spacing) is also given. To evaluate the global agreement of the atomic model with the experimental data, the program recomputes the R factor, the correlation coefficient between observed and calculated structure-factor amplitudes and Rfree (when appropriate). In addition, it gives several estimates of the average error in the atomic coordinates. The local agreement between the model and the electron-density map is evaluated on a per-residue basis, considering separately the macromolecule backbone and side-chain atoms, as well as solvent atoms and heterogroups. Among the criteria are the normalized average atomic displacement, the local density correlation coefficient and the polymer chain connectivity. The possibility of computing these criteria using the omit-map procedure is also provided. The described software should be a valuable tool in monitoring the refinement procedure and in assessing structures deposited in databases.

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  1. Carugo, Oliviero (ed.); Eisenhaber, Frank (ed.): Data mining techniques for the life sciences (2016)