Vincent B. Chen

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

The PHENIX software for macromolecular structure determination is described.

MolProbity: all-atom structure validation for macromolecular crystallography

MolProbity structure validation will diagnose most local errors in macromolecular crystal structures and help to guide their correction.

MolProbity: all-atom contacts and structure validation for proteins and nucleic acids

MolProbity is a general-purpose web server offering quality validation for 3D structures of proteins, nucleic acids and complexes. It provides detailed all-atom contact analysis of any steric problems within the molecules as well as updated dihedral-angle diagnostics, and it can calculate and display the H-bond and van der Waals contacts in the interfaces between components. An integral step in the process is the addition and full optimization of all hydrogen atoms, both polar and nonpolar. New analysis functions have been added for RNA, for interfaces, and for NMR ensembles. Additionally, both the web site and major component programs have been rewritten to improve speed, convenience, clarity and integration with other resources. MolProbity results are reported in multiple forms: as overall numeric scores, as lists or charts of local problems, as downloadable PDB and graphics files, and most notably as informative, manipulable 3D kinemage graphics shown online in the KiNG viewer. This service is available free to all users at http://molprobity.biochem.duke.edu.

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.

Structures of the bacterial ribosome in classical and hybrid states of tRNA binding.

Two crystal structures indicate how conformational changes in the ribosome assist protein synthesis. During protein synthesis, the ribosome controls the movement of tRNA and mRNA by means of large-scale structural rearrangements. We describe structures of the intact bacterial ribosome from Escherichia coli that reveal how the ribosome binds tRNA in two functionally distinct states, determined to a resolution of ~3.2 angstroms by means of x-ray crystallography. One state positions tRNA in the peptidyl-tRNA binding site. The second, a fully rotated state, is stabilized by ribosome recycling factor and binds tRNA in a highly bent conformation in a hybrid peptidyl/exit site. The structures help to explain how the ratchet-like motion of the two ribosomal subunits contributes to the mechanisms of translocation, termination, and ribosome recycling.

KING (Kinemage, Next Generation): A versatile interactive molecular and scientific visualization program

Proper visualization of scientific data is important for understanding spatial relationships. Particularly in the field of structural biology, where researchers seek to gain an understanding of the structure and function of biological macromolecules, it is important to have access to visualization programs which are fast, flexible, and customizable. We present KiNG, a Java program for visualizing scientific data, with a focus on macromolecular visualization. KiNG uses the kinemage graphics format, which is tuned for macromolecular structures, but is also ideal for many other kinds of spatially embedded information. KiNG is written in cross‐platform, open‐source Java code, and can be extended by end users through simple or elaborate “plug‐in” modules. Here, we present three such applications of KiNG to problems in structural biology (protein backbone rebuilding), bioinformatics of high‐dimensional data (e.g., protein sidechain chi angles), and classroom education (molecular illustration). KiNG is a mature platform for rapidly creating and capitalizing on scientific visualizations. As a research tool, it is invaluable as a test bed for new methods of visualizing scientific data and information. It is also a powerful presentation tool, whether for structure browsing, teaching, direct 3D display on the web, or as a method for creating pictures and videos for publications. KiNG is freely available for download at http://kinemage.biochem.duke.edu.

Use of knowledge-based restraints in phenix.refine to improve macromolecular refinement at low resolution

Recent developments in PHENIX are reported that allow the use of reference-model torsion restraints, secondary-structure hydrogen-bond restraints and Ramachandran restraints for improved macromolecular refinement in phenix.refine at low resolution.

phenix.model_vs_data: a high-level tool for the calculation of crystallographic model and data statistics

Application of phenix.model_vs_data to the contents of the Protein Data Bank shows that the vast majority of deposited structures can be automatically analyzed to reproduce the reported quality statistics. However, the small fraction of structures that elude automated re-analysis highlight areas where new software developments can help retain valuable information for future analysis.

Graphical tools for macromolecular crystallography in PHENIX

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

The other 90% of the protein: Assessment beyond the Cαs for CASP8 template-based and high-accuracy models†‡

For template‐based modeling in the CASP8 Critical Assessment of Techniques for Protein Structure Prediction, this work develops and applies six new full‐model metrics. They are designed to complement and add value to the traditional template‐based assessment by the global distance test (GDT) and related scores (based on multiple superpositions of Cα atoms between target structure and predictions labeled “Model 1”). The new metrics evaluate each predictor group on each target, using all atoms of their best model with above‐average GDT. Two metrics evaluate how “protein‐like” the predicted model is: the MolProbity score used for validating experimental structures, and a mainchain reality score using all‐atom steric clashes, bond length and angle outliers, and backbone dihedrals. Four other new metrics evaluate match of model to target for mainchain and sidechain hydrogen bonds, sidechain end positioning, and sidechain rotamers. Group‐average Z‐score across the six full‐model measures is averaged with group‐average GDT Z‐score to produce the overall ranking for full‐model, high‐accuracy performance. Separate assessments are reported for specific aspects of predictor‐group performance, such as robustness of approximately correct template or fold identification, and self‐scoring ability at identifying the best of their models. Fold identification is distinct from but correlated with group‐average GDT Z‐score if target difficulty is taken into account, whereas self‐scoring is done best by servers and is uncorrelated with GDT performance. Outstanding individual models on specific targets are identified and discussed. Predictor groups excelled at different aspects, highlighting the diversity of current methodologies. However, good full‐model scores correlate robustly with high Cα accuracy. Proteins 2009.