Introduction

Acknowledgements

The limits of interpretation

Protein precision re-examined: Luzzati plots do not estimate final errors

D W J Cruikshank, Manchester

Review: cross-validation and the free-R

Kevin Cowtan, York

Self-validation: an extended Hamilton test

Alessia Bacchi, Victor S Lamzin and Keith S Wilson, Hamburg

Full matrix least squares

Lynn F Ten Eyck, San Diego

Least-squares refinement of macromolecules: estimated standard deviations, NCS restraints and factors affecting convergence

George M Sheldrick, Göttingen

Torsion angle dynamics refinement of the Chaperonin GroEL at 2.8 Å resolution

Paul D Adams, Kerstin Braig, Luke M Rice, and Axel T Brünger, Yale

Real space refinement as a tool for model building

Tom J Oldfield, York

Improved structure refinement through maximum likelihood

Navraj S Pannu and Randy J Read, Edmonton

Maximum-likelihood structure refinement: theory and implementation within BUSTER + TNT

Gérard Bricogne and John Irwin, LMB, Cambridge

Application of maximum likelihood methods for macromolecular refinement

Garib N Murshudov, Eleanor J Dodson, York, Alexei A Vagin, Belgium

X-ray analysis of domain motions in protein crystals

David S Moss, Ian J Tickle, O Theis and A Wostrack, Birkbeck College

Group anisotropic thermal parameter refinement of the light-harvesting complex from purple bacteria Rhodopseudomonas acidophila

Miroslav Z Papiz, Daresbury Laboratory, and Steve M Prince, Glasgow

Is refinement from a random start possible?

— given diffraction data to medium resolution —

Pier Gros, Utrecht

What we can learn from anisotropic temperature factors?

Thomas Schneider, Hamburg

Sharpened maps for more effective refinement in protein crystallography

Susanna Butterworth, Victor S Lamzin, and Keith S Wilson, Hamburg

Removing bias from a model for HIV-1 reverse transcriptase by real space averaging between different crystal forms

Robert Esnouf, Jinshan Ren, Yvonne Jones, David Stammers and Davis S. Stuart, Oxford

Improving electron density maps calculated from weak or anisotropic data

S.J. Gamblin, NIMR, Mill Hill, and D.W. Rodgers and T. Stehle, Harvard

Pseudo symmetry

David Watkin, Oxford

Likelihood-weighted real space restraints for refinement at low resolution

J.P. Abrahams, LMB, Cambridge

Weighting diffraction data

G David Smith, Buffalo