A new software suite, called Crystallography & NMR System (CNS), has been developed for macromolecular structure determination by X-ray crystallography or solution nuclear magnetic resonance (NMR) spectroscopy. In contrast to existing structure-determination programs the architecture of CNS is highly flexible, allowing for extension to other structure-determination methods, such as electron microscopy and solid-state NMR spectroscopy. CNS has a hierarchical structure: a high-level hypertext markup language (HTML) user interface, task-oriented user input files, module files, a symbolic structure-determination language (CNS language), and low-level source code. Each layer is accessible to the user. The novice user may just use the HTML interface, while the more advanced user may use any of the other layers. The source code will be distributed, thus source-code modification is possible.The CNS language is sufficiently powerful and flexible that many new algorithms can be easily implemented in the CNS language without changes to the source code. The CNS language allows the user to perform operations on data structures, such as structure factors, electron-density maps, and atomic properties. The power of the CNS language has been demonstrated by the implementation of a comprehensive set of crystallographic procedures for phasing, density modification and refinement. User-friendly task-oriented input files are available for nearly all aspects of macromolecular (i') 1998 International Union of Crystallography Printed in Great Britain -all rights reserved structure determination by X-ray crystallography and solution NMR.

The CCP4 (Collaborative Computational Project, Number 4) software suite is a collection of programs and associated data and software libraries which can be used for macromolecular structure determination by X-ray crystallography. The suite is designed to be flexible, allowing users a number of methods of achieving their aims. The programs are from a wide variety of sources but are connected by a common infrastructure provided by standard file formats, data objects and graphical interfaces. Structure solution by macromolecular crystallography is becoming increasingly automated and the CCP4 suite includes several automation pipelines. After giving a brief description of the evolution of CCP4 over the last 30 years, an overview of the current suite is given. While detailed descriptions are given in the accompanying articles, here it is shown how the individual programs contribute to a complete software package.

This paper describes various components of the macromolecular crystallographic refinement program REFMAC5, which is distributed as part of the CCP4 suite. REFMAC5 utilizes different likelihood functions depending on the diffraction data employed (amplitudes or intensities), the presence of twinning and the availability of SAD/SIRAS experimental diffraction data. To ensure chemical and structural integrity of the refined model, REFMAC5 offers several classes of restraints and choices of model parameterization. Reliable models at resolutions at least as low as 4 Å can be achieved thanks to low-resolution refinement tools such as secondarystructure restraints, restraints to known homologous structures, automatic global and local NCS restraints, 'jelly-body' restraints and the use of novel long-range restraints on atomic displacement parameters (ADPs) based on the KullbackLeibler divergence. REFMAC5 additionally offers TLS parameterization and, when high-resolution data are available, fast refinement of anisotropic ADPs. Refinement in the presence of twinning is performed in a fully automated fashion. REFMAC5 is a flexible and highly optimized refinement package that is ideally suited for refinement across the entire resolution spectrum encountered in macromolecular crystallography.

The diseases caused by Shiga and cholera toxins account for the loss of millions of lives each year. Both belong to the clinically significant subset of bacterial AB5 toxins consisting of an enzymatically active A subunit that gains entry to susceptible mammalian cells after oligosaccharide recognition by the B5 homopentamer. Therapies might target the obligatory oligosaccharide-toxin recognition event, but the low intrinsic affinity of carbohydrate-protein interactions hampers the development of low-molecular-weight inhibitors. The toxins circumvent low affinity by binding simultaneously to five or more cell-surface carbohydrates. Here we demonstrate the use of the crystal structure of the B5 subunit of Escherichia coli O157:H7 Shiga-like toxin I (SLT-I) in complex with an analogue of its carbohydrate receptor to design an oligovalent, water-soluble carbohydrate ligand (named STARFISH), with subnanomolar inhibitory activity. The in vitro inhibitory activity is 1-10-million-fold higher than that of univalent ligands and is by far the highest molar activity of any inhibitor yet reported for Shiga-like toxins I and II. Crystallography of the STARFISH/Shiga-like toxin I complex explains this activity. Two trisaccharide receptors at the tips of each of five spacer arms simultaneously engage all five B subunits of two toxin molecules.

The CCP4 (Collaborative Computational Project, Number 4) software suite for macromolecular structure determination by X-ray crystallography groups brings together many programs and libraries that, by means of well established conventions, interoperate effectively without adhering to strict design guidelines. Because of this inherent flexibility, users are often presented with diverse, even divergent, choices for solving every type of problem. Recently, CCP4 introduced CCP4i2, a modern graphical interface designed to help structural biologists to navigate the process of structure determination, with an emphasis on pipelining and the streamlined presentation of results. In addition, CCP4i2 provides a framework for writing structure-solution scripts that can be built up incrementally to create increasingly automatic procedures.

The interaction of the plasma protein vitronectin with plasminogen activator inhibitor-1 (PAI-1) is central to human health. Vitronectin binding extends the lifetime of active PAI-1, which controls hemostasis by inhibiting fibrinolysis and has also been implicated in angiogenesis. The PAI-1-vitronectin binding interaction also affects cell adhesion and motility. For these reasons, elevated PAI-1 activities are associated both with coronary thrombosis and with a poor prognosis in many cancers. Here we show the crystal structure at a resolution of 2.3 A of the complex of the somatomedin B domain of vitronectin with PAI-1. The structure of the complex explains how vitronectin binds to and stabilizes the active conformation of PAI-1. It also explains the tissue effects of PAI-1, as PAI-1 competes for and sterically blocks the interaction of vitronectin with cell surface receptors and integrins. Structural understanding of the essential biological roles of the interaction between PAI-1 and vitronectin opens the prospect of specifically designed blocking agents for the prevention of thrombosis and treatment of cancer.

When crystal structures of proteins or small molecules are used to address questions of scientific relevance, the accuracy and precision of the atomic coordinates are crucial. Accordingly, the atomic model is generally improved by refining it to improve agreement with the observed diffraction data. The refinement of crystal structures is conventionally based on least-squares methods but such procedures are handicapped since conditions necessary for the use of the least-squares target are not satisfied. It is proposed here that refinement should be based on maximum likelihood and two maximum-likelihood targets have been implemented in the program XPLOR. Preliminary tests with protein structures give dramatic results. Compared to leastsquares refinement, maximum-likelihood refinement can achieve more than twice the improvement in average phase error. The resulting electron-density maps are correspondingly clearer and suffer less from model bias.

Recently, the target function for crystallographic refinement has been improved through a maximum likelihood analysis, which makes proper allowance for the effects of data quality, model errors, and incompleteness. The maximum likelihood target reduces the significance of false local minima during the refinement process, but it does not completely eliminate them, necessitating the use of stochastic optimization methods such as simulated annealing for poor initial models. It is shown that the combination of maximum likelihood with cross-validation, which reduces overfitting, and simulated annealing by torsion angle molecular dynamics, which simplifies the conformational search problem, results in a major improvement of the radius of convergence of refinement and the accuracy of the refined structure. Torsion angle molecular dynamics and the maximum likelihood target function interact synergistically, the combination of both methods being significantly more powerful than each method individually. This is demonstrated in realistic test cases at two typical minimum Bragg spacings (d min ‫؍‬ 2.0 and 2.8 Å, respectively), illustrating the broad applicability of the combined method. In an application to the refinement of a new crystal structure, the combined method automatically corrected a mistraced loop in a poor initial model, moving the backbone by 4 Å.The aim of macromolecular crystallography is to obtain an accurate atomic model based on the observed diffraction data. This model needs to be optimized to obtain the best agreement with the observed diffraction data. Several specialized optimization methods have been developed to refine macromolecules, including partial and full matrix least-squares (1, 2), conjugate gradient minimization (3), and molecular dynamicsbased simulated annealing (4, 5). However, the refinement of macromolecular structures is often difficult for several reasons. First, the data-to-parameter ratio is low, creating the danger of overfitting the diffraction data. Therefore, the apparent ratio of data to parameters is often increased by incorporation of chemical information, i.e., bond length and bond angle restraints obtained from ideal values seen in high resolution structures (1). Second, the initial model is often poor because of the typically limited quality of experimental phases, and there is, therefore, some discrepancy to the actual structure of the crystallized molecule. Third, local (false) minima exist in the target function. The more local minima there are and the deeper the minima are, the more likely refinement will fail. Fourth, model bias in the electron density maps complicates the process of manual refitting between cycles of automated refinement. This problem is exacerbated by overfitting the diffraction data in the refinement process.Methods have been devised to address these difficulties. Cross-validation, in the form of the free R-value, can be used to detect overfitting (6-8). The radius of convergence of refinement can be increased by the use of stochastic opt...