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

International Tables for Crystallography (2006). Vol. F, ch. 18.2, p. 375   | 1 | 2 |

Section 18.2.1. Introduction

A. T. Brunger,a* P. D. Adamsb and L. M. Ricec

aThe Howard Hughes Medical Institute, and Departments of Molecular and Cellular Physiology, Neurology and Neurological Sciences, and Stanford Synchrotron Radiation Laboratory, Stanford Universty, 1201 Welch Road, MSLS P210, Stanford, CA 94305-5489, USA,bThe Howard Hughes Medical Institute and Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA, and cDepartment of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA
Correspondence e-mail:

18.2.1. Introduction

| top | pdf |

The analysis of X-ray diffraction data generally requires sophisticated computational procedures that culminate in refinement and structure validation. The refinement procedure can be formulated as the chemically constrained or restrained nonlinear optimization of a target function, which usually measures the agreement between observed diffraction data and data computed from an atomic model. The ultimate goal of refinement is to optimize simultaneously the agreement of an atomic model with observed diffraction data and with a priori chemical information.

The target function used for this optimization normally depends on several atomic parameters and, most importantly, on atomic coordinates. The large number of adjustable parameters (typically at least three times the number of atoms in the model) gives rise to a very complicated target function. This, in turn, produces what is known as the multiple minima problem: the target function contains many local minima in addition to the global minimum, and this tends to defeat gradient-descent optimization techniques such as conjugate gradient or least-squares methods (Press et al., 1986[link]). These methods are unable to sample molecular conformations thoroughly enough to find the optimal model if the starting one is far from the correct structure.

The challenges of crystallographic refinement arise not only from the high dimensionality of the parameter space, but also from the phase problem. For new crystal structures, initial electron-density maps must be computed from a combination of observed diffraction amplitudes and experimental phases, where the latter are typically of poorer quality and/or at a lower resolution than the former. A different problem arises when structures are solved by molecular replacement (Hoppe, 1957[link]; Rossmann & Blow, 1962[link]), which uses a similar structure as a search model to calculate initial phases. In this case, the resulting electron-density maps can be severely `model-biased', that is, they sometimes seem to confirm the existence of the search model without providing clear evidence of actual differences between it and the true crystal structure. In both cases, initial atomic models usually contain significant errors and require extensive refinement.

Simulated annealing (Kirkpatrick et al., 1983[link]) is an optimization technique particularly well suited to overcoming the multiple minima problem. Unlike gradient-descent methods, simulated annealing can cross barriers between minima and, thus, can explore a greater volume of the parameter space to find better models (deeper minima). Following its introduction to crystallographic refinement (Brünger et al., 1987[link]), there have been major improvements of the original method in four principal areas: the measure of model quality, the search of the parameter space, the target function and the modelling of conformational variability.

For crystallographic refinement, the introduction of cross validation and the free R value (Brünger, 1992[link]) has significantly reduced the danger of overfitting the diffraction data during refinement. Cross validation also produces more realistic coordinate-error estimates based on the Luzzati or [\sigma_{A}] methods (Kleywegt & Brünger, 1996[link]). The complexity of the conformational space has been reduced by the introduction of torsion-angle refinement methods (Diamond, 1971[link]; Rice & Brünger, 1994[link]), which decrease the number of adjustable parameters that describe a model approximately tenfold. The target function has been improved by using a maximum-likelihood approach which takes into account model error, model incompleteness and errors in the experimental data (Bricogne, 1991[link]; Pannu & Read, 1996[link]). Cross validation of parameters for the maximum-likelihood target function was essential in order to obtain better results than with conventional target functions (Pannu & Read, 1996[link]; Adams et al., 1997[link]; Read, 1997[link]). Finally, the sampling power of simulated annealing has been used for exploring the molecule's conformational space in cases where the molecule undergoes dynamic motion or exhibits static disorder (Kuriyan et al., 1991[link]; Burling & Brünger, 1994[link]; Burling et al., 1996[link]).


Adams, P. D., Pannu, N. S., Read, R. J. & Brünger, A. T. (1997). Cross-validated maximum likelihood enhances crystallographic simulated annealing refinement. Proc. Natl Acad. Sci. USA, 94, 5018–5023.Google Scholar
Bricogne, G. (1991). A multisolution method of phase determination by combined maximization of entropy and likelihood. III. Extension to powder diffraction data. Acta Cryst. A47, 803–829.Google Scholar
Brunger, A. T. (1992). The free R value: a novel statistical quantity for assessing the accuracy of crystal structures. Nature (London), 355, 472–474.Google Scholar
Brunger, A. T., Kuriyan, J. & Karplus, M. (1987). Crystallographic R factor refinement by molecular dynamics. Science, 235, 458–460.Google Scholar
Burling, F. T. & Brunger, A. T. (1994). Thermal motion and conformational disorder in protein crystal structures: comparison of multi-conformer and time-averaging models. Isr. J. Chem. 34, 165–175.Google Scholar
Burling, F. T., Weis, W. I., Flaherty, K. M. & Brunger, A. T. (1996). Direct observation of protein solvation and discrete disorder with experimental crystallographic phases. Science, 271, 72–77.Google Scholar
Diamond, R. (1971). A real-space refinement procedure for proteins. Acta Cryst. A27, 436–452.Google Scholar
Hoppe, W. (1957). Die `Faltmolekülmethode' – eine neue Methode zur Bestimmung der Kristallstruktur bei ganz oder teilweise bekannter Molekülstruktur. Acta Cryst. 10, 750–751.Google Scholar
Kirkpatrick, S., Gelatt, C. D. & Vecchi, M. P. Jr (1983). Optimization by simulated annealing. Science, 220, 671–680.Google Scholar
Kleywegt, G. J. & Brunger, A. T. (1996). Cross-validation in crystallography: practice and applications. Structure, 4, 897–904.Google Scholar
Kuriyan, J., Ösapay, K., Burley, S. K., Brunger, A. T., Hendrickson, W. A. & Karplus, M. (1991). Exploration of disorder in protein structures by X-ray restrained molecular dynamics. Proteins, 10, 340–358.Google Scholar
Pannu, N. S. & Read, R. J. (1996). Improved structure refinement through maximum likelihood. Acta Cryst. A52, 659–668.Google Scholar
Press, W. H., Flannery, B. P., Teukolsky, S. A. & Vetterling, W. T. (1986). Editors. Numerical recipes, pp. 498–546. Cambridge University Press.Google Scholar
Read, R. J. (1997). Model phases: probabilities and bias. Methods Enzymol. 278, 110–128.Google Scholar
Rice, L. M. & Brunger, A. T. (1994). Torsion angle dynamics: reduced variable conformational sampling enhances crystallographic structure refinement. Proteins Struct. Funct. Genet. 19, 277–290.Google Scholar
Rossmann, M. G. & Blow, D. M. (1962). The detection of sub-units within the crystallographic asymmetric unit. Acta Cryst. 15, 24–51.Google Scholar

to end of page
to top of page