Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix

Diffraction (X-ray, neutron and electron) and electron cryo-microscopy are powerful methods to determine three-dimensional macromolecular structures, which are required to understand biological processes and to develop new therapeutics against diseases. The overall structure-solution workflow is similar for these techniques, but nuances exist because the properties of the reduced experimental data are different. Software tools for structure determination should therefore be tailored for each method. Phenix is a comprehensive software package for macromolecular structure determination that handles data from any of these techniques. Tasks performed with Phenix include data-quality assessment, map improvement, model building, the validation/rebuilding/refinement cycle and deposition. Each tool caters to the type of experimental data. The design of Phenix emphasizes the automation of procedures, where possible, to minimize repetitive and time-consuming manual tasks, while default parameters are chosen to encourage best practice. A graphical user interface provides access to many command-line features of Phenix and streamlines the transition between programs, project tracking and re-running of previous tasks.

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

A key challenge in the SAD phasing method is solving a structure when the anomalous signal-to-noise ratio is low. A simple theoretical framework for describing measurements of anomalous differences and the resulting useful anomalous correlation and anomalous signal in a SAD experiment is presented. Here, the useful anomalous correlation is defined as the correlation of anomalous differences with ideal anomalous differences from the anomalous substructure. The useful anomalous correlation reflects the accuracy of the data and the absence of minor sites. The useful anomalous correlation also reflects the information available for estimating crystallographic phases once the substructure has been determined. In contrast, the anomalous signal (the peak height in a model-phased anomalous difference Fourier at the coordinates of atoms in the anomalous substructure) reflects the information available about each site in the substructure and is related to the ability to find the substructure. A theoretical analysis shows that the expected value of the anomalous signal is the product of the useful anomalous correlation, the square root of the ratio of the number of unique reflections in the data set to the number of sites in the substructure, and a function that decreases with increasing values of the atomic displacement factor for the atoms in the substructure. This means that the ability to find the substructure in a SAD experiment is increased by high data quality and by a high ratio of reflections to sites in the substructure, and is decreased by high atomic displacement factors for the substructure.

Can I solve my structure by SAD phasing? Planning an experiment, scaling data and evaluating the useful anomalous correlation and anomalous signal

A key challenge in the SAD phasing method is solving a structure when the anomalous signal-to-noise ratio is low. Here, algorithms and tools for evaluating and optimizing the useful anomalous correlation and the anomalous signal in a SAD experiment are described. A simple theoretical framework [Terwilliger et al. (2016), Acta Cryst. D72, 346-358] is used to develop methods for planning a SAD experiment, scaling SAD data sets and estimating the useful anomalous correlation and anomalous signal in a SAD data set. The phenix.plan_sad_experiment tool uses a database of solved and unsolved SAD data sets and the expected characteristics of a SAD data set to estimate the probability that the anomalous substructure will be found in the SAD experiment and the expected map quality that would be obtained if the substructure were found. The phenix.scale_and_merge tool scales unmerged SAD data from one or more crystals using local scaling and optimizes the anomalous signal by identifying the systematic differences among data sets, and the phenix.anomalous_signal tool estimates the useful anomalous correlation and anomalous signal after collecting SAD data and estimates the probability that the data set can be solved and the likely figure of merit of phasing.

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.

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

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.

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Error estimates increase the value of homology models for molecular replacement

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Poorer models with good error estimates trump better models without errors

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A simple protocol creates coordinate error estimates for individual models

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Local coordinate error estimates enable molecular replacement for more targets

Automating crystallographic structure solution and refinement of protein-ligand complexes

High-throughput drug-discovery and mechanistic studies often require the determination of multiple related crystal structures that only differ in the bound ligands, point mutations in the protein sequence and minor conformational changes. If performed manually, solution and refinement requires extensive repetition of the same tasks for each structure. To accelerate this process and minimize manual effort, a pipeline encompassing all stages of ligand building and refinement, starting from integrated and scaled diffraction intensities, has been implemented in Phenix. The resulting system is able to successfully solve and refine large collections of structures in parallel without extensive user intervention prior to the final stages of model completion and validation.

Phaser.MRage: automated molecular replacement

Phaser.MRage is a molecular-replacement automation framework that implements a full model-generation workflow and provides several layers of model exploration to the user. It is designed to handle a large number of models and can distribute calculations efficiently onto parallel hardware. In addition, phaser.MRage can identify correct solutions and use this information to accelerate the search. Firstly, it can quickly score all alternative models of a component once a correct solution has been found. Secondly, it can perform extensive analysis of identified solutions to find protein assemblies and can employ assembled models for subsequent searches. Thirdly, it is able to use a priori assembly information (derived from, for example, homologues) to speculatively place and score molecules, thereby customizing the search procedure to a certain class of protein molecule (for example, antibodies) and incorporating additional biological information into molecular replacement.

Improved estimates of coordinate error for molecular replacement

A function for estimating the effective root-mean-square deviation in coordinates between two proteins has been developed that depends on both the sequence identity and the size of the protein and is optimized for use with molecular replacement in Phaser. A top peak translation-function Z-score of over 8 is found to be a reliable metric of when molecular replacement has succeeded.

The estimate of the root-mean-square deviation (r.m.s.d.) in coordinates between the model and the target is an essential parameter for calibrating likelihood functions for molecular replacement (MR). Good estimates of the r.m.s.d. lead to good estimates of the variance term in the likelihood functions, which increases signal to noise and hence success rates in the MR search. Phaser has hitherto used an estimate of the r.m.s.d. that only depends on the sequence identity between the model and target and which was not optimized for the MR likelihood functions. Variance-refinement functionality was added to Phaser to enable determination of the effective r.m.s.d. that optimized the log-likelihood gain (LLG) for a correct MR solution. Variance refinement was subsequently performed on a database of over 21 000 MR problems that sampled a range of sequence identities, protein sizes and protein fold classes. Success was monitored using the translation-function Z-score (TFZ), where a TFZ of 8 or over for the top peak was found to be a reliable indicator that MR had succeeded for these cases with one molecule in the asymmetric unit. Good estimates of the r.m.s.d. are correlated with the sequence identity and the protein size. A new estimate of the r.m.s.d. that uses these two parameters in a function optimized to fit the mean of the refined variance is implemented in Phaser and improves MR outcomes. Perturbing the initial estimate of the r.m.s.d. from the mean of the distribution in steps of standard deviations of the distribution further increases MR success rates.

Graphical tools for macromolecular crystallography in PHENIX

A new Python-based graphical user interface for the PHENIX suite of crystallography software is described. This interface unifies the command-line programs and their graphical displays, simplifying the development of new interfaces and avoiding duplication of function. With careful design, graphical interfaces can be displayed automatically, instead of being manually constructed. The resulting package is easily maintained and extended as new programs are added or modified.

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

In molecular replacement, the quality of models can be improved by transferring information contained in sequence alignment to the template structure. A family of algorithms has been developed that make use of the sequence-similarity score calculated from residue-substitution scores smoothed over nearby residues to delete or downweight parts of the model that are unreliable. These algorithms have been implemented in the program Sculptor, together with well established methods that are in common use for model improvement. An analysis of the new algorithms has been performed by studying the effect of algorithm parameters on the quality of models. Benchmarking against existing techniques shows that models from Sculptor compare favourably, especially if the alignment is unreliable. Carrying out multiple trials using alternative models created from the same structure but using different algorithm parameters can significantly improve the success rate.

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

The PHENIX software for macromolecular structure determination is described.

Macromolecular X-ray crystallography is routinely applied to understand biological processes at a molecular level. How­ever, significant time and effort are still required to solve and complete many of these structures because of the need for manual interpretation of complex numerical data using many software packages and the repeated use of interactive three-dimensional graphics. PHENIX has been developed to provide a comprehensive system for macromolecular crystallo­graphic structure solution with an emphasis on the automation of all procedures. This has relied on the development of algorithms that minimize or eliminate subjective input, the development of algorithms that automate procedures that are traditionally performed by hand and, finally, the development of a framework that allows a tight integration between the algorithms.

Structure of the lipopeptide antibiotic tsushimycin

The amphomycin derivative tsushimycin has been crystallized and its structure determined at 1.0 A resolution. The asymmetric unit contains 12 molecules and with 1300 independent atoms this structure is one of the largest solved using ab initio direct methods. The antibiotic is comprised of a cyclodecapeptide core, an exocyclic amino acid and a fatty-acid residue. Its backbone adopts a saddle-like conformation that is stabilized by a Ca2+ ion bound within the peptide ring and accounts for the Ca2+-dependence of this antibiotic class. Additional Ca2+ ions link the antibiotic molecules to dimers that enclose an empty space resembling a binding cleft. The dimers possess a large hydrophobic surface capable of interacting with the bacterial cell membrane. The antibiotic daptomycin may exhibit a similar conformation, as the amino-acid sequence is conserved at positions involved in Ca2+ binding.

Crystal structures of cephaibols

The crystal structures of the peptaibol antibiotics cephaibol A, cephaibol B and cephaibol C have been determined at ca. 0.9 A resolution. All three adopt a helical conformation with a sharp bend (of about 55 degrees) at the central hydroxyproline. All isovalines were found to possess the D configuration, superposition of all four models (there are two independent molecules in the cephaibol B structure) shows that the N-terminal helix is rigid and the C-terminus is flexible. There are differences in the hydrogen bonding patterns for the three structures that crystallize in different space groups despite relatively similar unit cell dimensions, but only in the case of cephaibol C does the packing emulate the formation of a membrane channel believed to be important for their biological function.

In-house measurement of the sulfur anomalous signal and its use for phasing

Five test structures (orthorhombic and trigonal trypsin, cubic and rhombohedral insulin and thaumatin) have been solved by the SAD (single-wavelength anomalous diffraction) method using highly redundant data collected at 100 K with a CCD detector, rotating-anode generator and three-circle goniometer. The very weak anomalous scattering (primarily from sulfur) was sufficient to locate all the anomalous scatterers using the integrated direct and Patterson methods in SHELXD. These positions and occupancies were used without further refinement to estimate phases that were extended to native (in-house) resolution by the sphere of influence algorithm in SHELXE. The final map correlation coefficients relative to the anisotropically refined structures were in the range 0.81-0.97. The use of highly redundant medium-resolution laboratory data for sulfur-SAD phasing combined with high-resolution synchrotron native data for phase expansion and structure refinement clearly has considerable potential.

In-house phase determination of the lima bean trypsin inhibitor: a low-resolution sulfur-SAD case

SAD (single-wavelength anomalous diffraction) has enormous potential for phasing proteins using only the anomalous signal of the almost ubiquitous native sulfur, but requires extremely precise data. The previously unknown structure of the lima bean trypsin inhibitor (LBTI) was solved using highly redundant data collected to 3 A using a CCD detector with a rotating-anode generator and three-circle goniometer. The seven 'super-S' atoms (disulfide bridges) were located by dual-space recycling with SHELXD and the high solvent content enabled the density-modification program SHELXE to generate high-quality maps despite the modest resolution. Subsequently, a 2.05 A synchrotron data set was collected and used for further phase extension and structure refinement.

Stereoselective Synthesis of the Two trans-(16-Hydroxymethyl)-3-methoxy-13α-estra-1,3,5(10)-trien-17-ol Isomers

Reduction of 16-(hydroxymethylidene)-3-methoxy-13α-estra-1,3,5(10)-trien-17-one yielded a mixture of two diastereomeric diols in the 6:1 ratio. The configurations of the newly formed stereogenic centres were determined by X-ray crystallography and NMR spectroscopy (NOE experiments) on the compounds in their cyclic acetaldehyde acetal forms.

Structures of glycopeptide antibiotics with peptides that model bacterial cell-wall precursors

The vancomycin-related antibiotics balhimycin and degluco-balhimycin have been crystallized in complexes with di-, tri- and pentapeptides that emulate bacterial cell-wall precursors, and four structures determined at atomic resolution (<1 A). In addition to the features expected from previous structural and spectroscopic studies, two new motifs were observed that may prove important in the design of antibiotics modified to overcome bacterial resistance. A changed binding mode was found in two dipeptide complexes, and a new type of face-to-face oligomerization (in addition to the well-established back-to-back dimerization) was seen when the model peptide reaches a critical fraction of the size of the cell-wall precursor pentapeptide. The extensive interactions involving both antibiotic and peptide molecules in this interface should appreciably enhance the kinetic and thermodynamic stability of the complexes. In the pentapeptide complex, the relative positions of the peptides are close to those required for d-Ala elimination, so this structure may provide a realistic model for the prevention of the enzyme-catalyzed cell-wall crosslinking by antibiotic binding.