Protein crystallography for non‐crystallographers, or how to get the best (but not more) from published macromolecular structures

Abstract

The number of macromolecular structures deposited in the Protein Data Bank now exceeds 45 000, with the vast majority determined using crystallographic methods. Thousands of studies describing such structures have been published in the scientific literature, and 14 Nobel prizes in chemistry or medicine have been awarded to protein crystallographers. As important as these structures are for understanding the processes that take place in living organisms and also for practical applications such as drug design, many non‐crystallographers still have problems with critical evaluation of the structural literature data. This review attempts to provide a brief outline of technical aspects of crystallography and to explain the meaning of some parameters that should be evaluated by users of macromolecular structures in order to interpret, but not over‐interpret, the information present in the coordinate files and in their description. A discussion of the extent of the information that can be gleaned from the coordinates of structures solved at different resolution, as well as problems and pitfalls encountered in structure determination and interpretation are also covered.

On the reliability of peptide nonplanarity seen in ultra‐high resolution crystal structures

“…The remarkable similarity of these distributions is consistent with the expectation that at resolutions near 1 Å , the diffraction data are sufficiently extensive that differences in restraints used by different refinement packages should have little influence on the resulting structure. 2,11 Further support for this conclusion is that when a comparison is made of the x distributions for the same set of structures refined by different programs, even the outliers match within a few degrees [compare plots D and S in the Fig. 3(B) left hand panel].…”

“…Also, when the R-values are as low as the ones for the structures analyzed here, a drop from, say, 14% to 13% is a substantial fractional improvement, especially given that overall R-values are global indicators that are not very sensitive to small changes in the positions of a small subset of atoms in a large protein structure. 2 Electron density maps show that models from tight x restraints are not correct As a global statistic, R-values are fairly insensitive to the incorrect positioning of a few atoms, and are unsuitable for evaluating whether any particular part of a model is correct. For this reason, the decisions about how to improve a structure during crystallographic refinement are made based on inspection of electron density maps, and as noted in a recent review about how to avoid pitfalls during structure determination: "a model must always be thoroughly scrutinized visually against electron density maps before accepting it as final."…”

“…In general, peaks that are smaller than 63 3 q rms are considered not reliably distinguishable from noise and the larger the peak the more significantly it indicates something that is wrong with the model. 2 This pair of 13 3 q rms peaks [ Fig. 1(B)] is a glaring indicator of an atom in the wrong position.…”

“…1 At such resolutions the diffraction data are sufficiently extensive that the geometric restraints that define the expected bond lengths, angles, and planarity become less important and less influential, 2,3 and the Additional Supporting Information may be found in the online version of this article.…”

Macromolecular crystallography at synchrotron radiation sources: current status and future developments

“…MX beamlines are in high demand from both academic and industrial users and are most productive. Over the past two decades, there have been many instrumentation and software developments and the MX technique has become accessible to non-experts (Wlodawer et al 2008). Most MX beamlines allow the X-ray wavelength to be tuned for the experiment.…”