Browsing by Subject "Crystallography"
- Results Per Page
- Sort Options
Item Open Access Accelerating crystal structure determination with iterative AlphaFold prediction.(Acta crystallographica. Section D, Structural biology, 2023-03) Terwilliger, Thomas C; Afonine, Pavel V; Liebschner, Dorothee; Croll, Tristan I; McCoy, Airlie J; Oeffner, Robert D; Williams, Christopher J; Poon, Billy K; Richardson, Jane S; Read, Randy J; Adams, Paul DExperimental structure determination can be accelerated with artificial intelligence (AI)-based structure-prediction methods such as AlphaFold. Here, an automatic procedure requiring only sequence information and crystallographic data is presented 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 this procedure: a predicted model rebuilt in one cycle is used as a template for prediction in the next cycle. This procedure was applied to X-ray data for 215 structures released by the Protein Data Bank in a recent six-month period. In 87% of cases our procedure yielded a model with at least 50% of Cα atoms matching those in the deposited models within 2 Å. Predictions from the iterative template-guided prediction procedure were more accurate than those obtained without templates. It is concluded that AlphaFold predictions obtained based on sequence information alone are usually accurate enough to solve the crystallographic phase problem with molecular replacement, and a general strategy for macromolecular structure determination that includes AI-based prediction both as a starting point and as a method of model optimization is suggested.Item Open Access Autonomous Symmetry Analysis and Structure Prototyping for Materials Discovery(2019) Hicks, David JonathanThe structure-property relationship is the foundation for materials modeling, predicting the behavior of compounds based on structural characteristics. With the advancement of ab initio methods and high performance computing, atomic configurations are being explored at an unprecedented rate. To effectively navigate the vast search space, procedures are presented for analyzing and prototyping crystalline compounds for high-throughput simulation. Integrated into the Automatic Flow (AFLOW) framework for computational materials discovery, these tools are the underlying workhorse for symmetry classification and materials generation. In particular, algorithms are detailed for determining the set of isometries for crystals, featuring a comprehensive collection of symmetry descriptions along with routines to handle ill-conditioned structural data. A library of crystallographic structures is also introduced — showcasing nearly 600 prototypes with representatives from each space group — and is complemented with functionality for rapidly creating materials via prototype decoration. Lastly, a module for comparing crystalline compounds is described to identify duplicate entries within large data sets and detect novel structure-types, independent of representation. Mechanisms are featured for converting geometries into a standard prototype convention, providing a direct pathway for incorporation into the crystallographic library. With these autonomous computational approaches, compounds are automatically classified and generated, enabling the design of new and structurally distinct materials.
Item Open Access Computational crystallization.(Arch Biochem Biophys, 2016-07-15) Altan, Irem; Charbonneau, Patrick; Snell, Edward HCrystallization is a key step in macromolecular structure determination by crystallography. While a robust theoretical treatment of the process is available, due to the complexity of the system, the experimental process is still largely one of trial and error. In this article, efforts in the field are discussed together with a theoretical underpinning using a solubility phase diagram. Prior knowledge has been used to develop tools that computationally predict the crystallization outcome and define mutational approaches that enhance the likelihood of crystallization. For the most part these tools are based on binary outcomes (crystal or no crystal), and the full information contained in an assembly of crystallization screening experiments is lost. The potential of this additional information is illustrated by examples where new biological knowledge can be obtained and where a target can be sub-categorized to predict which class of reagents provides the crystallization driving force. Computational analysis of crystallization requires complete and correctly formatted data. While massive crystallization screening efforts are under way, the data available from many of these studies are sparse. The potential for this data and the steps needed to realize this potential are discussed.Item Open Access Crystal Symmetry Algorithms in a High-Throughput Framework for Materials Research(2013) Taylor, Richard HansenThe high-throughput framework AFLOW that has been developed and used successfully over the last decade is improved to include fully-integrated software for crystallographic symmetry characterization. The standards used in the symmetry algorithms conform with the conventions and prescriptions given in the International Tables of Crystallography (ITC). A standard cell choice with standard origin is selected, and the space group, point group, Bravais lattice, crystal system, lattice system, and representative symmetry operations are determined. Following the conventions of the ITC, the Wyckoff sites are also determined and their labels and site symmetry are provided. The symmetry code makes no assumptions on the input cell orientation, origin, or reduction and has been integrated in the AFLOW high-throughput framework for materials discovery by adding to the existing code base and making use of existing classes and functions. The software is written in object-oriented C++ for flexibility and reuse. A performance analysis and examination of the algorithms scaling with cell size and symmetry is also reported.
Item Open Access Soft matter perspective on protein crystal assembly.(Colloids Surf B Biointerfaces, 2016-01-01) Fusco, Diana; Charbonneau, PatrickCrystallography may be the gold standard of protein structure determination, but obtaining the necessary high-quality crystals is also in some ways akin to prospecting for the precious metal. The tools and models developed in soft matter physics to understand colloidal assembly offer some insights into the problem of crystallizing proteins. This topical review describes the various analogies that have been made between proteins and colloids in that context. We highlight the explanatory power of patchy particle models, but also the challenges of providing guidance for crystallizing specific proteins. We conclude with a presentation of possible future research directions. This review is intended for soft matter scientists interested in protein crystallization as a self-assembly problem, and as an introduction to the pertinent physics literature for protein scientists more generally.Item Open Access Structure-Function Studies in Sulfite Oxidase with Altered Active Sites(2009) Qiu, JamesSulfite oxidase, a metabolically important enzyme, catalyzes the physiologically critical conversion of sulfite to sulfate in the terminal step of the degradation of sulfur containing compounds. The enzyme has been the focus for much research since its discovery in the 1950's. A central question to understanding the mechanism of molybdoenzymes such as sulfite oxidase and nitrate reductase concerns the roles of active site residues and the coordination chemistry of the Mo atom in the structure and function of the enzyme. The goal of this work was directed towards the characterization and determination of the structures of active site variants of sulfite oxidase using a spectroscopic, kinetic, and protein crystallographic approach.
Earlier studies have identified a single, highly conserved cysteine residue as the donor of a covalent bond from the protein to molybdenum in sulfite oxidase and nitrate reductase. The C185S and C185A variants of chicken sulfite oxidase exhibited severely attenuated activity. Crystallographic and spectroscopic analysis of both variants revealed a change in the metal coordination, from a dioxo to a trioxo form of Mo.
Assimilatory nitrate reductase is a member of the sulfite oxidase family of molybdopterin enzymes. The crystal structure of the Mo domain of the enzyme from Pichia angusta revealed high structural homology in the active sites of nitrate reductase and sulfite oxidase. Both enzymes utilize the same form of the molybdenum cofactor and have three out of five residues conserved at the active site. Substitution of two active site residues in sulfite oxidase alters the substrate affinity of chicken SO from sulfite to nitrate, resulting in an increase of nitrate reductase activity over wild-type sulfite oxidase. Additionally we identified an additional amino acid position in sulfite oxidase that corresponds to a non-conserved position in NR that further increased NR activity. Finally, these nitrate reductase variants of sulfite oxidase were crystallized and the structures solved. This represents the first example of the transmutation of a molybdenum enzyme to change activity and substrate affinity to those of a homologous enzyme.
Item Open Access Using C-Alpha Geometry to Describe Protein Secondary Structure and Motifs(2015) Williams, Christopher JosephX-ray crystallography 3D atomic models are used in a variety of research areas to understand and manipulate protein structure. Research and application are dependent on the quality of the models. Low-resolution experimental data is a common problem in crystallography which makes solving structures and producing the reliable models that many scientists depend on difficult.
In this work, I develop new, automated tools for validation and correction of low-resolution structures. These tools are gathered under the name CaBLAM, for C-alpha Based Low-resolution Annotation Method. CaBLAM uses a unique, C-alpha-geometry-based parameter space to identify outliers in protein backbone geometry, and to identify secondary structure that may be masked by modeling errors.
CaBLAM was developed in the Python programming language as part of the Phenix crystallography suite and the open CCTBX Project. It makes use of architecture and methods available in the CCTBX toolbox. Quality-filtered databases of high-resolution protein structures, especially the Top8000, were used to construct contours of expected protein behavior for CaBLAM. CaBLAM has also been integrated into the codebase for the Richardson Lab's online MolProbity validation service.
CaBLAM succeeds in providing useful validation feedback for protein structures in the 2.5-4.0A resolution range. This success demonstrates the relative reliability of the C-alpha; trace of a protein in this resolution range. Full mainchain information can be extrapolated from the C-alpha; trace, especially for regular secondary structure elements.
CaBLAM has also informed our approach to validation for low-resolution structures. Moderation of feedback, to reduce validation overload and to focus user attention on modeling errors that are both significant and correctable, is one of our goals. CaBLAM and the related methods that have grown around it demonstrate the progress towards this goal.