Phaser crystallographic software
01 Aug 2007-Journal of Applied Crystallography (International Union of Crystallography)-Vol. 40, Iss: 4, pp 658-674
TL;DR: A description is given of Phaser-2.1: software for phasing macromolecular crystal structures by molecular replacement and single-wavelength anomalous dispersion phasing.
Abstract: Phaser is a program for phasing macromolecular crystal structures by both molecular replacement and experimental phasing methods. The novel phasing algorithms implemented in Phaser have been developed using maximum likelihood and multivariate statistics. For molecular replacement, the new algorithms have proved to be significantly better than traditional methods in discriminating correct solutions from noise, and for single-wavelength anomalous dispersion experimental phasing, the new algorithms, which account for correlations between F+ and F−, give better phases (lower mean phase error with respect to the phases given by the refined structure) than those that use mean F and anomalous differences ΔF. One of the design concepts of Phaser was that it be capable of a high degree of automation. To this end, Phaser (written in C++) can be called directly from Python, although it can also be called using traditional CCP4 keyword-style input. Phaser is a platform for future development of improved phasing methods and their release, including source code, to the crystallographic community.
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TL;DR: The PHENIX software for macromolecular structure determination is described and its uses and benefits are described.
Abstract: 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.
18,531 citations
Cites background or methods from "Phaser crystallographic software"
...Phaser, available in PHENIX as phenix.phaser, applies the principle of maximum likelihood to solving crystal structures by molecular replacement, by single-wavelength anomalous diffraction (SAD) or by a combination of both....
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...Phenix.automr carries out automated likelihood-based molecular replacement using phenix.phaser (Read, 2001; McCoy et al., 2005, 2007; McCoy, 2007)....
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...More recent builds of PHENIX contain a new GUI for the AutoMR wizard and future releases will include a new interface for Phaser (McCoy et al., 2007)....
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...Phenix.autosol carries out experimental phasing with phenix.phaser (McCoy et al., 2004, 2007) or phenix.solve (Terwilliger & Berendzen, 1999), density modification with phenix.resolve (Terwilliger, 1999) and preliminary model building using the methods in phenix.autobuild (Terwilliger,…...
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...If a molecular-replacement model is available, phenix. autosol will use phenix.phaser (McCoy et al., 2004, 2007) to complete the anomalous substructure iteratively by constructing log-likelihood gradient maps for the anomalous scatterers based on the model of the non-anomalous structure and any…...
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TL;DR: An overview of the CCP4 software suite for macromolecular crystallography is given.
Abstract: 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.
11,023 citations
Cites background or methods from "Phaser crystallographic software"
...BALBES is based around the MR program MOLREP (Vagin & Teplyakov, 1997, 2010), while MrBUMP can also use the program Phaser (McCoy et al., 2007)....
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...Much of this functionality is common to Phaser and MOLREP, but there are a number of differences in implementation, so that both may prove useful in certain circumstances....
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...For instance, a single run of Phaser can search for several copies each of several components in the structure of a complex, testing different possible search orders and trying different possible choices of space group....
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...Phaser (McCoy et al., 2007) can obtain phase estimates starting from known heavy-atom positions and SAD data....
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...Phaser can also use a partial model, for example from a molecularreplacement solution that is hard to refine, as a source of phase information to help locate weak anomalous scatterers and thus improved phases....
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TL;DR: High-resolution crystal structures of the receptor-binding domain of the spike protein of SARS-CoV-2 and SARS -CoV in complex with ACE2 provide insights into the binding mode of these coronaviruses and highlight essential ACE2-interacting residues.
Abstract: A new and highly pathogenic coronavirus (severe acute respiratory syndrome coronavirus-2, SARS-CoV-2) caused an outbreak in Wuhan city, Hubei province, China, starting from December 2019 that quickly spread nationwide and to other countries around the world1–3. Here, to better understand the initial step of infection at an atomic level, we determined the crystal structure of the receptor-binding domain (RBD) of the spike protein of SARS-CoV-2 bound to the cell receptor ACE2. The overall ACE2-binding mode of the SARS-CoV-2 RBD is nearly identical to that of the SARS-CoV RBD, which also uses ACE2 as the cell receptor4. Structural analysis identified residues in the SARS-CoV-2 RBD that are essential for ACE2 binding, the majority of which either are highly conserved or share similar side chain properties with those in the SARS-CoV RBD. Such similarity in structure and sequence strongly indicate convergent evolution between the SARS-CoV-2 and SARS-CoV RBDs for improved binding to ACE2, although SARS-CoV-2 does not cluster within SARS and SARS-related coronaviruses1–3,5. The epitopes of two SARS-CoV antibodies that target the RBD are also analysed for binding to the SARS-CoV-2 RBD, providing insights into the future identification of cross-reactive antibodies. High-resolution crystal structures of the receptor-binding domain of the spike protein of SARS-CoV-2 and SARS-CoV in complex with ACE2 provide insights into the binding mode of these coronaviruses and highlight essential ACE2-interacting residues.
4,555 citations
TL;DR: It is shown that cancer-associated IDH1 mutations result in a new ability of the enzyme to catalyse the NADPH-dependent reduction of α-ketoglutarate to R(-)-2-hydroxyglutarate (2HG), and that the excess 2HG which accumulates in vivo contributes to the formation and malignant progression of gliomas.
Abstract: Mutations in the enzyme cytosolic isocitrate dehydrogenase 1 (IDH1) are a common feature of a major subset of primary human brain cancers. These mutations occur at a single amino acid residue of the IDH1 active site, resulting in loss of the enzyme's ability to catalyse conversion of isocitrate to alpha-ketoglutarate. However, only a single copy of the gene is mutated in tumours, raising the possibility that the mutations do not result in a simple loss of function. Here we show that cancer-associated IDH1 mutations result in a new ability of the enzyme to catalyse the NADPH-dependent reduction of alpha-ketoglutarate to R(-)-2-hydroxyglutarate (2HG). Structural studies demonstrate that when arginine 132 is mutated to histidine, residues in the active site are shifted to produce structural changes consistent with reduced oxidative decarboxylation of isocitrate and acquisition of the ability to convert alpha-ketoglutarate to 2HG. Excess accumulation of 2HG has been shown to lead to an elevated risk of malignant brain tumours in patients with inborn errors of 2HG metabolism. Similarly, in human malignant gliomas harbouring IDH1 mutations, we find markedly elevated levels of 2HG. These data demonstrate that the IDH1 mutations result in production of the onco-metabolite 2HG, and indicate that the excess 2HG which accumulates in vivo contributes to the formation and malignant progression of gliomas.
3,508 citations
References
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01 Jan 2002
19,213 citations
"Phaser crystallographic software" refers methods in this paper
..., 2002, 2004) and PyMol (DeLano, 2002), amongst others....
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...At about the same time, Python was chosen as a scripting language by PHENIX, ccp4mg (Potterton et al., 2002, 2004) and PyMol (DeLano, 2002), amongst others....
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TL;DR: The CCP4 (Collaborative Computational Project, number 4) program suite is a collection of programs and associated data and subroutine libraries which can be used for macromolecular structure determination by X-ray crystallography.
Abstract: The CCP4 (Collaborative Computational Project, number 4) program suite is a collection of programs and associated data and subroutine 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 and so there may be more than one program to cover each function. The programs are written mainly in standard Fortran77. They are from a wide variety of sources but are connected by standard data file formats. The package has been ported to all the major platforms under both Unix and VMS. The suite is distributed by anonymous ftp from Daresbury Laboratory and is widely used throughout the world.
17,220 citations
TL;DR: The likelihood function for macromolecular structures is extended to include prior phase information and experimental standard uncertainties and the results derived are consistently better than those obtained from least-squares refinement.
Abstract: This paper reviews the mathematical basis of maximum likelihood The likelihood function for macromolecular structures is extended to include prior phase information and experimental standard uncertainties The assumption that different parts of a structure might have different errors is considered A method for estimating σA using `free' reflections is described and its effects analysed The derived equations have been implemented in the program REFMAC This has been tested on several proteins at different stages of refinement (bacterial α-amylase, cytochrome c′, cross-linked insulin and oligopeptide binding protein) The results derived using the maximum-likelihood residual are consistently better than those obtained from least-squares refinement
14,622 citations
"Phaser crystallographic software" refers methods in this paper
...Rtm is a 1 n row vector of A values between the target and n models, which are approximated for each model using a four-parameter curve (Murshudov et al., 1997) tm ¼ fP 1 fsol exp Bsol 4d2 1=2 exp 2 2RMS2 3d2 ; ð19Þ where fP (= 1 by default) is the fraction of ordered structure modelled, fsol (=…...
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...It has been used subsequently by Murshudov et al. (1997) in REFMAC and by Bricogne & Irwin (1996) in Buster/TNT, and has been shown to work well in practice....
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Book•
01 Jan 1982
TL;DR: In this article, the authors present an overview of the basic concepts of multivariate analysis, including matrix algebra and random vectors, as well as a strategy for analyzing multivariate models.
Abstract: (NOTE: Each chapter begins with an Introduction, and concludes with Exercises and References.) I. GETTING STARTED. 1. Aspects of Multivariate Analysis. Applications of Multivariate Techniques. The Organization of Data. Data Displays and Pictorial Representations. Distance. Final Comments. 2. Matrix Algebra and Random Vectors. Some Basics of Matrix and Vector Algebra. Positive Definite Matrices. A Square-Root Matrix. Random Vectors and Matrices. Mean Vectors and Covariance Matrices. Matrix Inequalities and Maximization. Supplement 2A Vectors and Matrices: Basic Concepts. 3. Sample Geometry and Random Sampling. The Geometry of the Sample. Random Samples and the Expected Values of the Sample Mean and Covariance Matrix. Generalized Variance. Sample Mean, Covariance, and Correlation as Matrix Operations. Sample Values of Linear Combinations of Variables. 4. The Multivariate Normal Distribution. The Multivariate Normal Density and Its Properties. Sampling from a Multivariate Normal Distribution and Maximum Likelihood Estimation. The Sampling Distribution of 'X and S. Large-Sample Behavior of 'X and S. Assessing the Assumption of Normality. Detecting Outliners and Data Cleaning. Transformations to Near Normality. II. INFERENCES ABOUT MULTIVARIATE MEANS AND LINEAR MODELS. 5. Inferences About a Mean Vector. The Plausibility of ...m0 as a Value for a Normal Population Mean. Hotelling's T 2 and Likelihood Ratio Tests. Confidence Regions and Simultaneous Comparisons of Component Means. Large Sample Inferences about a Population Mean Vector. Multivariate Quality Control Charts. Inferences about Mean Vectors When Some Observations Are Missing. Difficulties Due To Time Dependence in Multivariate Observations. Supplement 5A Simultaneous Confidence Intervals and Ellipses as Shadows of the p-Dimensional Ellipsoids. 6. Comparisons of Several Multivariate Means. Paired Comparisons and a Repeated Measures Design. Comparing Mean Vectors from Two Populations. Comparison of Several Multivariate Population Means (One-Way MANOVA). Simultaneous Confidence Intervals for Treatment Effects. Two-Way Multivariate Analysis of Variance. Profile Analysis. Repealed Measures, Designs, and Growth Curves. Perspectives and a Strategy for Analyzing Multivariate Models. 7. Multivariate Linear Regression Models. The Classical Linear Regression Model. Least Squares Estimation. Inferences About the Regression Model. Inferences from the Estimated Regression Function. Model Checking and Other Aspects of Regression. Multivariate Multiple Regression. The Concept of Linear Regression. Comparing the Two Formulations of the Regression Model. Multiple Regression Models with Time Dependant Errors. Supplement 7A The Distribution of the Likelihood Ratio for the Multivariate Regression Model. III. ANALYSIS OF A COVARIANCE STRUCTURE. 8. Principal Components. Population Principal Components. Summarizing Sample Variation by Principal Components. Graphing the Principal Components. Large-Sample Inferences. Monitoring Quality with Principal Components. Supplement 8A The Geometry of the Sample Principal Component Approximation. 9. Factor Analysis and Inference for Structured Covariance Matrices. The Orthogonal Factor Model. Methods of Estimation. Factor Rotation. Factor Scores. Perspectives and a Strategy for Factor Analysis. Structural Equation Models. Supplement 9A Some Computational Details for Maximum Likelihood Estimation. 10. Canonical Correlation Analysis Canonical Variates and Canonical Correlations. Interpreting the Population Canonical Variables. The Sample Canonical Variates and Sample Canonical Correlations. Additional Sample Descriptive Measures. Large Sample Inferences. IV. CLASSIFICATION AND GROUPING TECHNIQUES. 11. Discrimination and Classification. Separation and Classification for Two Populations. Classifications with Two Multivariate Normal Populations. Evaluating Classification Functions. Fisher's Discriminant Function...nSeparation of Populations. Classification with Several Populations. Fisher's Method for Discriminating among Several Populations. Final Comments. 12. Clustering, Distance Methods and Ordination. Similarity Measures. Hierarchical Clustering Methods. Nonhierarchical Clustering Methods. Multidimensional Scaling. Correspondence Analysis. Biplots for Viewing Sample Units and Variables. Procustes Analysis: A Method for Comparing Configurations. Appendix. Standard Normal Probabilities. Student's t-Distribution Percentage Points. ...c2 Distribution Percentage Points. F-Distribution Percentage Points. F-Distribution Percentage Points (...a = .10). F-Distribution Percentage Points (...a = .05). F-Distribution Percentage Points (...a = .01). Data Index. Subject Index.
11,697 citations
10 Mar 1970
8,159 citations
"Phaser crystallographic software" refers background in this paper
...The anisotropic displacement ellipsoid must remain invariant under the application of each symmetry operator of the space group or site-symmetry group, respectively (Giacovazzo, 1992; Grosse-Kunstleve & Adams, 2002)....
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...…( 1, . . . , 6) are determined by refinement to maximize the Wilson log-likelihood function: WilsonLL ¼ X h;acentric ln <0 FO; " N þ 2F þ X h;centric ln W0 FO; " N þ 2F : ð6Þ The anisotropic values can be interconverted to anisotropic B factors or U factors (Grosse-Kunstleve & Adams, 2002)....
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