ORCID Profile
0009-0001-6855-9631
Current Organisation
University of Cambridge
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Publisher: International Union of Crystallography (IUCr)
Date: 2021
DOI: 10.1107/S2059798320014746
Abstract: Crystallographic phasing strategies increasingly require the exploration and ranking of many hypotheses about the number, types and positions of atoms, molecules and/or molecular fragments in the unit cell, each with only a small chance of being correct. Accelerating this move has been improvements in phasing methods, which are now able to extract phase information from the placement of very small fragments of structure, from weak experimental phasing signal or from combinations of molecular replacement and experimental phasing information. Describing phasing in terms of a directed acyclic graph allows graph-management software to track and manage the path to structure solution. The crystallographic software supporting the graph data structure must be strictly modular so that nodes in the graph are efficiently generated by the encapsulated functionality. To this end, the development of new software, Phasertng , which uses directed acyclic graphs natively for input/output, has been initiated. In Phasertng , the codebase of Phaser has been rebuilt, with an emphasis on modularity, on scripting, on speed and on continuing algorithm development. As a first application of phasertng , its advantages are demonstrated in the context of phasertng.xtricorder , a tool to analyse and triage merged data in preparation for molecular replacement or experimental phasing. The description of the phasing strategy with directed acyclic graphs is a generalization that extends beyond the functionality of Phasertng , as it can incorporate results from bioinformatics and other crystallographic tools, and will facilitate multifaceted search strategies, dynamic ranking of alternative search pathways and the exploitation of machine learning to further improve phasing strategies.
Publisher: International Union of Crystallography (IUCr)
Date: 15-05-2009
Publisher: Cold Spring Harbor Laboratory
Date: 22-11-2022
DOI: 10.1101/2022.11.21.517405
Abstract: AI-based methods such as AlphaFold have revolutionized structural biology, often making it possible to predict protein structures with high accuracy. The accuracies of these predictions vary, however, and they do not include ligands, covalent modifications or other environmental factors. Here we focus on very-high-confidence parts of AlphaFold predictions, evaluating how well they can be expected to describe the structure of a protein in a particular environment. We compare predictions with experimental crystallographic maps of the same proteins for 102 crystal structures. In many cases, those parts of AlphaFold predictions that were predicted with very high confidence matched experimental maps remarkably closely. In other cases, these predictions differed from experimental maps on a global scale through distortion and domain orientation, and on a local scale in backbone and side-chain conformation. Overall, C α atoms in very-high-confidence parts of AlphaFold predictions differed from corresponding crystal structures by a median of 0.6 Å, and about 10% of these differed by more than 2 Å, each about twice the values found for pairs of crystal structures containing the same components but determined in different space groups. We suggest considering AlphaFold predictions as exceptionally useful hypotheses. We further suggest that it is important to consider the confidence in prediction when interpreting AlphaFold predictions and to carry out experimental structure determination to verify structural details, particularly those that involve interactions not included in the prediction.
Publisher: International Union of Crystallography (IUCr)
Date: 22-01-2010
DOI: 10.1107/S0907444909052925
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.
Publisher: Elsevier BV
Date: 09-2011
Publisher: International Union of Crystallography (IUCr)
Date: 10-2019
DOI: 10.1107/S2059798319011471
Abstract: 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.
Publisher: International Union of Crystallography (IUCr)
Date: 16-05-2012
DOI: 10.1107/S0021889812017293
Abstract: 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.
Publisher: Humana Press
Date: 2008
DOI: 10.1007/978-1-60327-058-8_28
Abstract: Significant time and effort are often required to solve and complete a macromolecular crystal structure. The development of automated computational methods for the analysis, solution, and completion of crystallographic structures has the potential to produce minimally biased models in a short time without the need for manual intervention. The PHENIX software suite is a highly automated system for macromolecular structure determination that can rapidly arrive at an initial partial model of a structure without significant human intervention, given moderate resolution, and good quality data. This achievement has been made possible by the development of new algorithms for structure determination, maximum-likelihood molecular replacement (PHASER), heavy-atom search (HySS), template- and pattern-based automated model-building (RESOLVE, TEXTAL), automated macromolecular refinement (phenix. refine), and iterative model-building, density modification and refinement that can operate at moderate resolution (RESOLVE, AutoBuild). These algorithms are based on a highly integrated and comprehensive set of crystallographic libraries that have been built and made available to the community. The algorithms are tightly linked and made easily accessible to users through the PHENIX Wizards and the PHENIX GUI.
Publisher: International Union of Crystallography (IUCr)
Date: 28-11-2003
DOI: 10.1107/S0909049503024130
Abstract: A new software system called PHENIX (Python-based Hierarchical ENvironment for Integrated Xtallography) is being developed for the automation of crystallographic structure solution. This will provide the necessary algorithms to proceed from reduced intensity data to a refined molecular model, and facilitate structure solution for both the novice and expert crystallographer. Here, the features of PHENIXare reviewed and the recent advances in infrastructure and algorithms are briefly described.
Publisher: International Union of Crystallography (IUCr)
Date: 21-10-2002
DOI: 10.1107/S0907444902016657
Abstract: Structural genomics seeks to expand rapidly the number of protein structures in order to extract the maximum amount of information from genomic sequence databases. The advent of several large-scale projects worldwide leads to many new challenges in the field of crystallographic macromolecular structure determination. A novel software package called PHENIX (Python-based Hierarchical ENvironment for Integrated Xtallography) is therefore being developed. This new software will provide the necessary algorithms to proceed from reduced intensity data to a refined molecular model and to facilitate structure solution for both the novice and expert crystallographer.
Publisher: International Union of Crystallography (IUCr)
Date: 25-12-2013
Publisher: International Union of Crystallography (IUCr)
Date: 21-02-2022
DOI: 10.1107/S2059798322000729
Abstract: The introduction of disulfide bonds into periplasmic proteins is a critical process in many Gram-negative bacteria. The formation and regulation of protein disulfide bonds have been linked to the production of virulence factors. Understanding the different pathways involved in this process is important in the development of strategies to disarm pathogenic bacteria. The well characterized disulfide bond-forming (DSB) proteins play a key role by introducing or isomerizing disulfide bonds between cysteines in substrate proteins. Curiously, the suppressor of copper sensitivity C proteins (ScsCs), which are part of the bacterial copper-resistance response, share structural and functional similarities with DSB oxidase and isomerase proteins, including the presence of a catalytic thioredoxin domain. However, the oxidoreductase activity of ScsC varies with its oligomerization state, which depends on a poorly conserved N-terminal domain. Here, the structure and function of Caulobacter crescentus ScsC (CcScsC) have been characterized. It is shown that CcScsC binds copper in the copper(I) form with subpicomolar affinity and that its isomerase activity is comparable to that of Escherichia coli DsbC, the prototypical dimeric bacterial isomerase. It is also reported that CcScsC functionally complements trimeric Proteus mirabilis ScsC (PmScsC) in vivo , enabling the swarming of P. mirabilis in the presence of copper. Using mass photometry and small-angle X-ray scattering (SAXS) the protein is demonstrated to be trimeric in solution, like PmScsC, and not dimeric like EcDsbC. The crystal structure of CcScsC was also determined at a resolution of 2.6 Å, confirming the trimeric state and indicating that the trimerization results from interactions between the N-terminal α-helical domains of three CcScsC protomers. The SAXS data analysis suggested that the protomers are dynamic, like those of PmScsC, and are able to s le different conformations in solution.
Publisher: Cold Spring Harbor Laboratory
Date: 18-11-2022
DOI: 10.1101/2022.11.18.517112
Abstract: Experimental structure determination can be accelerated with AI-based structure prediction methods such as AlphaFold. Here we present an automatic procedure requiring only sequence information and crystallographic data that uses AlphaFold predictions to produce an electron density map and a structural model. Iterating through cycles of structure prediction is a key element of our procedure: a predicted model rebuilt in one cycle is used as a template for prediction in the next cycle. We applied this procedure to X-ray data for 215 structures released by the Protein Data Bank in a recent 6-month period. In 87% of cases our procedure yielded a model with at least 50% of C α atoms matching those in the deposited models within 2Å. Predictions from our iterative template-guided prediction procedure were more accurate than those obtained without templates. We suggest a general strategy for macromolecular structure determination that includes AI-based prediction both as a starting point and as a method of model optimization.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Airlie J. McCoy.