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International
Tables for Crystallography Volume F Crystallography of biological macromolecules Edited by M. G. Rossmann and E. Arnold © International Union of Crystallography 2006 |
International Tables for Crystallography (2006). Vol. F. ch. 18.4, p. 400
Section 18.4.5.5. Automatic location of water sites
a
National Cancer Institute, Brookhaven National Laboratory, Building 725A-X9, Upton, NY 11973, USA,bStructural Biology Laboratory, Department of Chemistry, University of York, York YO10 5DD, England, and CLRC, Daresbury Laboratory, Daresbury, Warrington, WA4 4AD, England, and cStructural Biology Laboratory, Department of Chemistry, University of York, York YO10 5DD, England |
The protein itself has a clearly defined chemical structure, and the number of atoms to be positioned and how they are bonded to one another are known at the start of model building. The solvent region is in marked contrast to this, as the number of ordered water sites is not known a priori, and the distances between them are less well defined, their occupancy is uncertain, and there may be overlapping networks of partially occupied solvent sites. Those of low occupancy lie at the level of significance of the Fourier maps.
Selection of partially occupied solvent sites poses a most cumbersome problem in the modelling over and above that of the macromolecule itself, and can be highly subjective and very time consuming. Improved resolution of the data reveals additional weak or partially occupied solvent sites, which generally do not behave well during refinement. Water atoms modelled into relatively weak peaks in electron density tend to drift out of the density during refinement due to the weak gradients that define their positions.
Given the huge number of water sites in question, automatic and at least semi-objective protocols are required. Several procedures have been developed for the automated identification of water sites during refinement [inter alia ARP (Lamzin & Wilson, 1997![[link]](/graphics/greenarr.gif)
) and SHELXL (Sheldrick & Schneider, 1997)] and others allow selective inspection of such sites using graphics [O (Jones et al., 1991) and Quanta (Molecular Simulations Inc., San Diego)]. These depend on a combination of peak height in the density map and geometric considerations.
References
Jones, T. A., Zou, J.-Y., Cowan, S. W. & Kjeldgaard, M. (1991). Improved methods for building protein models in electron density maps and the location of errors in these models. Acta Cryst. A47, 110–119.Google Scholar
Lamzin, V. S. & Wilson, K. S. (1997). Automated refinement for protein crystallography. Methods Enzymol. 277, 269–305.Google Scholar
Sheldrick, G. M. & Schneider, T. R. (1997). SHELXL: high-resolution refinement. Methods Enzymol. 277, 319–343.Google Scholar
