PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-14 (14)
 

Clipboard (0)
None
Journals
Year of Publication
Document Types
1.  Flexible torsion-angle noncrystallographic symmetry restraints for improved macromolecular structure refinement 
Flexible torsion angle-based NCS restraints have been implemented in phenix.refine, allowing improved model refinement at all resolutions. Rotamer correction and rotamer consistency checks between NCS-related amino-acid side chains further improve the final model quality.
One of the great challenges in refining macromolecular crystal structures is a low data-to-parameter ratio. Historically, knowledge from chemistry has been used to help to improve this ratio. When a macromolecule crystallizes with more than one copy in the asymmetric unit, the noncrystallographic symmetry relationships can be exploited to provide additional restraints when refining the working model. However, although globally similar, NCS-related chains often have local differences. To allow for local differences between NCS-related molecules, flexible torsion-based NCS restraints have been introduced, coupled with intelligent rotamer handling for protein chains, and are available in phenix.refine for refinement of models at all resolutions.
doi:10.1107/S1399004714003277
PMCID: PMC4014122  PMID: 24816103
macromolecular crystallography; noncrystallographic symmetry; NCS; refinement; automation
2.  Ligand placement based on prior structures: the guided ligand-replacement method 
A new module, Guided Ligand Replacement (GLR), has been developed in Phenix to increase the ease and success rate of ligand placement when prior protein-ligand complexes are available.
The process of iterative structure-based drug design involves the X-ray crystal structure determination of upwards of 100 ligands with the same general scaffold (i.e. chemotype) complexed with very similar, if not identical, protein targets. In conjunction with insights from computational models and assays, this collection of crystal structures is analyzed to improve potency, to achieve better selectivity and to reduce liabilities such as absorption, distribution, metabolism, excretion and toxicology. Current methods for modeling ligands into electron-density maps typically do not utilize information on how similar ligands bound in related structures. Even if the electron density is of sufficient quality and resolution to allow de novo placement, the process can take considerable time as the size, complexity and torsional degrees of freedom of the ligands increase. A new module, Guided Ligand Replacement (GLR), was developed in Phenix to increase the ease and success rate of ligand placement when prior protein–ligand complexes are available. At the heart of GLR is an algorithm based on graph theory that associates atoms in the target ligand with analogous atoms in the reference ligand. Based on this correspondence, a set of coordinates is generated for the target ligand. GLR is especially useful in two situations: (i) modeling a series of large, flexible, complicated or macrocyclic ligands in successive structures and (ii) modeling ligands as part of a refinement pipeline that can automatically select a reference structure. Even in those cases for which no reference structure is available, if there are multiple copies of the bound ligand per asymmetric unit GLR offers an efficient way to complete the model after the first ligand has been placed. In all of these applications, GLR leverages prior knowledge from earlier structures to facilitate ligand placement in the current structure.
doi:10.1107/S1399004713030071
PMCID: PMC3919265  PMID: 24419386
ligand placement; guided ligand-replacement method; GLR
3.  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.
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.
doi:10.1107/S139900471302748X
PMCID: PMC3919266  PMID: 24419387
protein–ligand complexes; automation; crystallographic structure solution and refinement
4.  Towards automated crystallographic structure refinement with phenix.refine  
phenix.refine is a program within the PHENIX package that supports crystallographic structure refinement against experimental data with a wide range of upper resolution limits using a large repertoire of model parameterizations. This paper presents an overview of the major phenix.refine features, with extensive literature references for readers interested in more detailed discussions of the methods.
phenix.refine is a program within the PHENIX package that supports crystallographic structure refinement against experimental data with a wide range of upper resolution limits using a large repertoire of model parameterizations. It has several automation features and is also highly flexible. Several hundred parameters enable extensive customizations for complex use cases. Multiple user-defined refinement strategies can be applied to specific parts of the model in a single refinement run. An intuitive graphical user interface is available to guide novice users and to assist advanced users in managing refinement projects. X-ray or neutron diffraction data can be used separately or jointly in refinement. phenix.refine is tightly integrated into the PHENIX suite, where it serves as a critical component in automated model building, final structure refinement, structure validation and deposition to the wwPDB. This paper presents an overview of the major phenix.refine features, with extensive literature references for readers interested in more detailed discussions of the methods.
doi:10.1107/S0907444912001308
PMCID: PMC3322595  PMID: 22505256
structure refinement; PHENIX; joint X-ray/neutron refinement; maximum likelihood; TLS; simulated annealing; subatomic resolution; real-space refinement; twinning; NCS
5.  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.
Traditional methods for macromolecular refinement often have limited success at low resolution (3.0–3.5 Å or worse), producing models that score poorly on crystallographic and geometric validation criteria. To improve low-resolution refinement, knowledge from macromolecular chemistry and homology was used to add three new coordinate-restraint functions to the refinement program phenix.refine. Firstly, a ‘reference-model’ method uses an identical or homologous higher resolution model to add restraints on torsion angles to the geometric target function. Secondly, automatic restraints for common secondary-structure elements in proteins and nucleic acids were implemented that can help to preserve the secondary-structure geometry, which is often distorted at low resolution. Lastly, we have implemented Ramachandran-based restraints on the backbone torsion angles. In this method, a ϕ,ψ term is added to the geometric target function to minimize a modified Ramachandran landscape that smoothly combines favorable peaks identified from non­redundant high-quality data with unfavorable peaks calculated using a clash-based pseudo-energy function. All three methods show improved MolProbity validation statistics, typically complemented by a lowered R free and a decreased gap between R work and R free.
doi:10.1107/S0907444911047834
PMCID: PMC3322597  PMID: 22505258
macromolecular crystallography; low resolution; refinement; automation
6.  electronic Ligand Builder and Optimization Workbench (eLBOW): a tool for ligand coordinate and restraint generation 
A new software system for automated ligand coordinate and restraint generation is presented.
The electronic Ligand Builder and Optimization Workbench (eLBOW) is a program module of the PHENIX suite of computational crystallographic software. It is designed to be a flexible procedure that uses simple and fast quantum-chemical techniques to provide chemically accurate information for novel and known ligands alike. A variety of input formats and options allow the attainment of a number of diverse goals including geometry optimization and generation of restraints.
doi:10.1107/S0907444909029436
PMCID: PMC2748967  PMID: 19770504
ligands; coordinates; restraints; Python; object-oriented programming
7.  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.
doi:10.1107/S0907444909052925
PMCID: PMC2815670  PMID: 20124702
PHENIX; Python; macromolecular crystallography; algorithms
8.  Decision-making in structure solution using Bayesian estimates of map quality: the PHENIX AutoSol wizard 
Ten measures of experimental electron-density-map quality are examined and the skewness of electron density is found to be the best indicator of actual map quality. A Bayesian approach to estimating map quality is developed and used in the PHENIX AutoSol wizard to make decisions during automated structure solution.
Estimates of the quality of experimental maps are important in many stages of structure determination of macromolecules. Map quality is defined here as the correlation between a map and the corresponding map obtained using phases from the final refined model. Here, ten different measures of experimental map quality were examined using a set of 1359 maps calculated by re-analysis of 246 solved MAD, SAD and MIR data sets. A simple Bayesian approach to estimation of map quality from one or more measures is presented. It was found that a Bayesian estimator based on the skewness of the density values in an electron-density map is the most accurate of the ten individual Bayesian estimators of map quality examined, with a correlation between estimated and actual map quality of 0.90. A combination of the skewness of electron density with the local correlation of r.m.s. density gives a further improvement in estimating map quality, with an overall correlation coefficient of 0.92. The PHENIX AutoSol wizard carries out automated structure solution based on any combination of SAD, MAD, SIR or MIR data sets. The wizard is based on tools from the PHENIX package and uses the Bayesian estimates of map quality described here to choose the highest quality solutions after experimental phasing.
doi:10.1107/S0907444909012098
PMCID: PMC2685735  PMID: 19465773
structure solution; scoring; Protein Data Bank; phasing; decision-making; PHENIX; experimental electron-density maps
9.  Iterative-build OMIT maps: map improvement by iterative model building and refinement without model bias 
A procedure for carrying out iterative model building, density modification and refinement is presented in which the density in an OMITregion is essentially unbiased by an atomic model. Density from a set of overlapping OMIT regions can be combined to create a composite ‘iterative-build’ OMIT map that is everywhere unbiased by an atomic model but also everywhere benefiting from the model-based information present elsewhere in the unit cell. The procedure may have applications in the validation of specific features in atomic models as well as in overall model validation. The procedure is demonstrated with a molecular-replacement structure and with an experimentally phased structure and a variation on the method is demonstrated by removing model bias from a structure from the Protein Data Bank.
doi:10.1107/S0907444908004319
PMCID: PMC2424225  PMID: 18453687
10.  Iterative-build OMIT maps: map improvement by iterative model building and refinement without model bias 
An OMIT procedure is presented that has the benefits of iterative model building density modification and refinement yet is essentially unbiased by the atomic model that is built.
A procedure for carrying out iterative model building, density modification and refinement is presented in which the density in an OMIT region is essentially unbiased by an atomic model. Density from a set of overlapping OMIT regions can be combined to create a composite ‘iterative-build’ OMIT map that is everywhere unbiased by an atomic model but also everywhere benefiting from the model-based information present elsewhere in the unit cell. The procedure may have applications in the validation of specific features in atomic models as well as in overall model validation. The procedure is demonstrated with a molecular-replacement structure and with an experimentally phased structure and a variation on the method is demonstrated by removing model bias from a structure from the Protein Data Bank.
doi:10.1107/S0907444908004319
PMCID: PMC2424225  PMID: 18453687
model building; model validation; macromolecular models; Protein Data Bank; refinement; OMIT maps; bias; structure refinement; PHENIX
11.  Iterative model building, structure refinement and density modification with the PHENIX AutoBuild wizard 
The highly automated PHENIX AutoBuild wizard is described. The procedure can be applied equally well to phases derived from isomorphous/anomalous and molecular-replacement methods.
The PHENIX AutoBuild wizard is a highly automated tool for iterative model building, structure refinement and density modification using RESOLVE model building, RESOLVE statistical density modification and phenix.refine structure refinement. Recent advances in the AutoBuild wizard and phenix.refine include automated detection and application of NCS from models as they are built, extensive model-completion algorithms and automated solvent-molecule picking. Model-completion algorithms in the AutoBuild wizard include loop building, crossovers between chains in different models of a structure and side-chain optimization. The AutoBuild wizard has been applied to a set of 48 structures at resolutions ranging from 1.1 to 3.2 Å, resulting in a mean R factor of 0.24 and a mean free R factor of 0.29. The R factor of the final model is dependent on the quality of the starting electron density and is relatively independent of resolution.
doi:10.1107/S090744490705024X
PMCID: PMC2394820  PMID: 18094468
model building; model completion; macromolecular models; Protein Data Bank; structure refinement; PHENIX
12.  Interpretation of ensembles created by multiple iterative rebuilding of macromolecular models 
Heterogeneity in ensembles generated by independent model rebuilding principally reflects the limitations of the data and of the model-building process rather than the diversity of structures in the crystal.
Automation of iterative model building, density modification and refinement in macromolecular crystallography has made it feasible to carry out this entire process multiple times. By using different random seeds in the process, a number of different models compatible with experimental data can be created. Sets of models were generated in this way using real data for ten protein structures from the Protein Data Bank and using synthetic data generated at various resolutions. Most of the heterogeneity among models produced in this way is in the side chains and loops on the protein surface. Possible interpretations of the variation among models created by repetitive rebuilding were investigated. Synthetic data were created in which a crystal structure was modelled as the average of a set of ‘perfect’ structures and the range of models obtained by rebuilding a single starting model was examined. The standard deviations of coordinates in models obtained by repetitive rebuilding at high resolution are small, while those obtained for the same synthetic crystal structure at low resolution are large, so that the diversity within a group of models cannot generally be a quantitative reflection of the actual structures in a crystal. Instead, the group of structures obtained by repetitive rebuilding reflects the precision of the models, and the standard deviation of coordinates of these structures is a lower bound estimate of the uncertainty in coordinates of the individual models.
doi:10.1107/S0907444907009791
PMCID: PMC2483474  PMID: 17452785
model building; model completion; coordinate errors; models; Protein Data Bank; convergence; reproducibility; heterogeneity; precision; accuracy
13.  Ligand identification using electron-density map correlations 
An automated ligand-fitting procedure is applied to (F o − F c)exp(iϕc) difference density for 200 commonly found ligands from macromolecular structures in the Protein Data Bank to identify ligands from density maps.
A procedure for the identification of ligands bound in crystal structures of macromolecules is described. Two characteristics of the density corresponding to a ligand are used in the identification procedure. One is the correlation of the ligand density with each of a set of test ligands after optimization of the fit of that ligand to the density. The other is the correlation of a fingerprint of the density with the fingerprint of model density for each possible ligand. The fingerprints consist of an ordered list of correlations of each the test ligands with the density. The two characteristics are scored using a Z-score approach in which the correlations are normalized to the mean and standard deviation of correlations found for a variety of mismatched ligand-density pairs, so that the Z scores are related to the probability of observing a particular value of the correlation by chance. The procedure was tested with a set of 200 of the most commonly found ligands in the Protein Data Bank, collectively representing 57% of all ligands in the Protein Data Bank. Using a combination of these two characteristics of ligand density, ranked lists of ligand identifications were made for representative (F o − F c)exp(iϕc) difference density from entries in the Protein Data Bank. In 48% of the 200 cases, the correct ligand was at the top of the ranked list of ligands. This approach may be useful in identification of unknown ligands in new macromolecular structures as well as in the identification of which ligands in a mixture have bound to a macromolecule.
doi:10.1107/S0907444906046233
PMCID: PMC2483487  PMID: 17164532
model building; model completion; shape analysis
14.  Automated ligand fitting by core-fragment fitting and extension into density 
An automated ligand-fitting procedure has been developed and tested on 9327 ligands and (F o − F c)exp(iϕc) difference density from macromolecular structures in the Protein Data Bank.
A procedure for fitting of ligands to electron-density maps by first fitting a core fragment of the ligand to density and then extending the remainder of the ligand into density is presented. The approach was tested by fitting 9327 ligands over a wide range of resolutions (most are in the range 0.8–­4.8 Å) from the Protein Data Bank (PDB) into (F o − F c)exp(iϕc) difference density calculated using entries from the PDB without these ligands. The procedure was able to place 58% of these 9327 ligands within 2 Å (r.m.s.d.) of the coordinates of the atoms in the original PDB entry for that ligand. The success of the fitting procedure was relatively insensitive to the size of the ligand in the range 10–100 non-H atoms and was only moderately sensitive to resolution, with the percentage of ligands placed near the coordinates of the original PDB entry for fits in the range 58–73% over all resolution ranges tested.
doi:10.1107/S0907444906017161
PMCID: PMC2745883  PMID: 16855309
model building; model completion; shape analysis

Results 1-14 (14)