Gábor Bunkóczi

PHENIX: a comprehensive Python-based system for macromolecular structure solution

The PHENIX software for macromolecular structure determination is described.

The Phenix software for automated determination of macromolecular structures.

X-ray crystallography is a critical tool in the study of biological systems. It is able to provide information that has been a prerequisite to understanding the fundamentals of life. It is also a method that is central to the development of new therapeutics for human disease. Significant time and effort are required to determine and optimize many macromolecular structures because of the need for manual interpretation of complex numerical data, often using many different software packages, and the repeated use of interactive three-dimensional graphics. The Phenix software package has been developed to provide a comprehensive system for macromolecular crystallographic structure solution with an emphasis on automation. This has required the development of new algorithms that minimize or eliminate subjective input in favor of built-in expert-systems knowledge, the automation of procedures that are traditionally performed by hand, and the development of a computational framework that allows a tight integration between the algorithms. The application of automated methods is particularly appropriate in the field of structural proteomics, where high throughput is desired. Features in Phenix for the automation of experimental phasing with subsequent model building, molecular replacement, structure refinement and validation are described and examples given of running Phenix from both the command line and graphical user interface.

Improvement of molecular-replacement models with Sculptor

The molecular-replacement model-improvement program Sculptor is described, with an analysis of the algorithms used.

phenix.mr_rosetta: molecular replacement and model rebuilding with Phenix and Rosetta

The combination of algorithms from the structure-modeling field with those of crystallographic structure determination can broaden the range of templates that are useful for structure determination by the method of molecular replacement. Automated tools in phenix.mr_rosetta simplify the application of these combined approaches by integrating Phenix crystallographic algorithms and Rosetta structure-modeling algorithms and by systematically generating and evaluating models with a combination of these methods. The phenix.mr_rosetta algorithms can be used to automatically determine challenging structures. The approaches used in phenix.mr_rosetta are described along with examples that show roles that structure-modeling can play in molecular replacement.

Graphical tools for macromolecular crystallography in PHENIX

The foundations and current features of a widely used graphical user interface for macromolecular crystallography are described.

Phaser.MRage: automated molecular replacement

The functionality of the molecular-replacement pipeline phaser.MRage is introduced and illustrated with examples.

Macromolecular X-ray structure determination using weak, single-wavelength anomalous data

We describe a likelihood-based method for determining the substructure of anomalously scattering atoms in macromolecular crystals that allows successful structure determination by single-wavelength anomalous diffraction (SAD) X-ray analysis with weak anomalous signal. With the use of partial models and electron density maps in searches for anomalously scattering atoms, testing of alternative values of parameters and parallelized automated model-building, this method has the potential to extend the applicability of the SAD method in challenging cases.

Automating crystallographic structure solution and refinement of protein–ligand complexes

A software system for automated protein–ligand crystallography has been implemented in the Phenix suite. This significantly reduces the manual effort required in high-throughput crystallographic studies.

Can I solve my structure by SAD phasing? Anomalous signal in SAD phasing

The useful anomalous correlation and the anomalous signal in a SAD experiment are metrics describing the accuracy of the data and the total information content in a SAD data set and are shown to be related to the probability of solving the anomalous substructure and the quality of the initial phases.

Local error estimates dramatically improve the utility of homology models for solving crystal structures by molecular replacement

Summary Predicted structures submitted for CASP10 have been evaluated as molecular replacement models against the corresponding sets of structure factor amplitudes. It has been found that the log-likelihood gain score computed for each prediction correlates well with common structure quality indicators but is more sensitive when the accuracy of the models is high. In addition, it was observed that using coordinate error estimates submitted by predictors to weight the model can improve its utility in molecular replacement dramatically, and several groups have been identified who reliably provide accurate error estimates that could be used to extend the application of molecular replacement for low-homology cases.