scholarly journals Determining crystal structures through crowdsourcing and coursework

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
Scott Horowitz ◽  
◽  
Brian Koepnick ◽  
Raoul Martin ◽  
Agnes Tymieniecki ◽  
...  
Keyword(s):  
Crystal Structures ◽  
Electron Density ◽  
Undergraduate Students ◽  
Model Building ◽  
Computer Game ◽  
Real Space ◽  
Amyloid Toxicity ◽  
New Family ◽  
Density Maps ◽  
New Feature

Abstract We show here that computer game players can build high-quality crystal structures. Introduction of a new feature into the computer game Foldit allows players to build and real-space refine structures into electron density maps. To assess the usefulness of this feature, we held a crystallographic model-building competition between trained crystallographers, undergraduate students, Foldit players and automatic model-building algorithms. After removal of disordered residues, a team of Foldit players achieved the most accurate structure. Analysing the target protein of the competition, YPL067C, uncovered a new family of histidine triad proteins apparently involved in the prevention of amyloid toxicity. From this study, we conclude that crystallographers can utilize crowdsourcing to interpret electron density information and to produce structure solutions of the highest quality.

scholarly journals Atomic resolution experimental phase information reveals extensive disorder and bound 2-methyl-2,4-pentanediol in Ca2+-calmodulin

2016 ◽  
Vol 72 (1) ◽  
pp. 83-92 ◽  
Author(s):  
Jiusheng Lin ◽  
Henry van den Bedem ◽  
Axel T. Brunger ◽  
Mark A. Wilson
Keyword(s):  
Crystal Structures ◽  
Electron Density ◽  
Full Range ◽  
Binding Pocket ◽  
Data Set ◽  
Conformational Substates ◽  
Density Maps ◽  
Hydrophobic Binding ◽  
Ef Hands ◽  
Critical Regions

Calmodulin (CaM) is the primary calcium signaling protein in eukaryotes and has been extensively studied using various biophysical techniques. Prior crystal structures have noted the presence of ambiguous electron density in both hydrophobic binding pockets of Ca2+-CaM, but no assignment of these features has been made. In addition, Ca2+-CaM samples many conformational substates in the crystal and accurately modeling the full range of this functionally important disorder is challenging. In order to characterize these features in a minimally biased manner, a 1.0 Å resolution single-wavelength anomalous diffraction data set was measured for selenomethionine-substituted Ca2+-CaM. Density-modified electron-density maps enabled the accurate assignment of Ca2+-CaM main-chain and side-chain disorder. These experimental maps also substantiate complex disorder models that were automatically built using low-contour features of model-phased electron density. Furthermore, experimental electron-density maps reveal that 2-methyl-2,4-pentanediol (MPD) is present in the C-terminal domain, mediates a lattice contact between N-terminal domains and may occupy the N-terminal binding pocket. The majority of the crystal structures of target-free Ca2+-CaM have been derived from crystals grown using MPD as a precipitant, and thus MPD is likely to be bound in functionally critical regions of Ca2+-CaM in most of these structures. The adventitious binding of MPD helps to explain differences between the Ca2+-CaM crystal and solution structures and is likely to favor more open conformations of the EF-hands in the crystal.

scholarly journals Automated detection of disulfide bridges in electron density maps using linear discriminant analysis

2005 ◽  
Vol 38 (1) ◽  
pp. 121-125 ◽  
Author(s):  
Thomas R. Ioerger
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Electron Density ◽  
Model Building ◽  
Computational Method ◽  
Training Data ◽  
Decision Threshold ◽  
Disulfide Bridges ◽  
Linear Discriminant ◽  
Density Maps

The ability to recognize disulfide bridges automatically in electron density maps would be useful to both protein crystallographers and automated model-building programs. A computational method is described for recognizing disulfide bridges in uninterpreted maps based on linear discriminant analysis. For each localized spherical region in a map, a vector of rotation-invariant numeric features is calculated that captures various aspects of the local pattern of density. These features values are then input into a linear equation, with coefficients computed to optimize discrimination of a set of training examples (disulfides and non-disulfides), and compared with a decision threshold. The method is shown to be highly accurate at distinguishing disulfides from non-disulfides in both the original training data and in real (experimental) electron density maps of other proteins.

scholarly journals Rapid model building of α-helices in electron-density maps

2010 ◽  
Vol 66 (3) ◽  
pp. 268-275 ◽  
Author(s):  
Thomas C. Terwilliger
Keyword(s):  
High Resolution ◽  
Electron Density ◽  
Model Building ◽  
Main Chain ◽  
Rapid Identification ◽  
Side Chains ◽  
Low Resolution ◽  
Density Maps ◽  
Experimental Electron Density

A method for the identification of α-helices in electron-density maps at low resolution followed by interpretation at moderate to high resolution is presented. Rapid identification is achieved at low resolution, where α-helices appear as tubes of density. The positioning and direction of the α-helices is obtained at moderate to high resolution, where the positions of side chains can be seen. The method was tested on a set of 42 experimental electron-density maps at resolutions ranging from 1.5 to 3.8 Å. An average of 63% of the α-helical residues in these proteins were built and an average of 76% of the residues built matched helical residues in the refined models of the proteins. The overall average r.m.s.d. between main-chain atoms in the modeled α-helices and the nearest atom with the same name in the refined models of the proteins was 1.3 Å.

scholarly journals Building brain network in electron density map determined by micro-CT

2014 ◽  
Vol 70 (a1) ◽  
pp. C1752-C1752
Author(s):  
Rino Saiga ◽  
Susumu Takekoshi ◽  
Naoya Nakamura ◽  
Akihisa Takeuchi ◽  
Kentaro Uesugi ◽  
...  
Keyword(s):  
Density Distribution ◽  
Electron Density ◽  
Electron Density Distribution ◽  
Model Building ◽  
Three Dimensional ◽  
Brain Network ◽  
X Ray ◽  
Density Maps ◽  
Density Map ◽  
Electron Density Map

In macromolecular crystallography, an electron density distribution is traced to build a model of the target molecule. We applied this method to model building for electron density maps of a brain network. Human cerebral tissue was stained with heavy atoms [1]. The sample was then analyzed at the BL20XU beamline of SPring-8 to obtain a three-dimensional map of X-ray attenuation coefficients representing the electron density distribution. Skeletonized wire models were built by placing and connecting nodes in the map [2], as shown in the figure below. The model-building procedures were similar to those reported for crystallographic analyses of macromolecular structures, while the neuronal network was automatically traced by using a Sobel filter. Neuronal circuits were then analytically resolved from the skeletonized models. We suggest that X-ray microtomography along with model building in the electron density map has potential as a method for understanding three-dimensional microstructures relevant to biological functions.

scholarly journals Statistical quality indicators for electron-density maps

2012 ◽  
Vol 68 (4) ◽  
pp. 454-467 ◽  
Author(s):  
Ian J. Tickle
Keyword(s):  
Electron Density ◽  
Statistical Significance ◽  
Significance Test ◽  
Real Space ◽  
Structure Model ◽  
Space Correlation ◽  
The Real ◽  
Difference Density ◽  
Density Maps ◽  
The Difference

The commonly used validation metrics for the local agreement of a structure model with the observed electron density, namely the real-space R (RSR) and the real-space correlation coefficient (RSCC), are reviewed. It is argued that the primary goal of all validation techniques is to verify the accuracy of the model, since precision is an inherent property of the crystal and the data. It is demonstrated that the principal weakness of both of the above metrics is their inability to distinguish the accuracy of the model from its precision. Furthermore, neither of these metrics in their usual implementation indicate the statistical significance of the result. The statistical properties of electron-density maps are reviewed and an improved alternative likelihood-based metric is suggested. This leads naturally to a χ2 significance test of the difference density using the real-space difference density Z score (RSZD). This is a metric purely of the local model accuracy, as required for effective model validation and structure optimization by practising crystallographers prior to submission of a structure model to the PDB. A new real-space observed density Z score (RSZO) is also proposed; this is a metric purely of the model precision, as a substitute for other precision metrics such as the B factor.

Mapping lipid and detergent molecules at the surface of membrane proteins

2011 ◽  
Vol 39 (3) ◽  
pp. 775-779 ◽  
Author(s):  
Richard J. Cogdell ◽  
Alastair T. Gardiner ◽  
Aleksander W. Roszak ◽  
Sigitas Stončius ◽  
Pavel Kočovský ◽  
...  
Keyword(s):  
Membrane Proteins ◽  
Crystal Structures ◽  
Present Article ◽  
Electron Density ◽  
Rhodobacter Sphaeroides ◽  
Binding Sites ◽  
Heavy Atom ◽  
Hydrophobic Surface ◽  
Density Maps ◽  
Blastochloris Viridis

Electron-density maps for the crystal structures of membrane proteins often show features suggesting binding of lipids and/or detergent molecules on the hydrophobic surface, but usually it is difficult to identify the bound molecules. In our studies, heavy-atom-labelled phospholipids and detergents have been used to unequivocally identify these binding sites at the surfaces of test membrane proteins, the reaction centres from Rhodobacter sphaeroides and Blastochloris viridis. The generality of this method is discussed in the present article.

scholarly journals Features and development ofCoot

2010 ◽  
Vol 66 (4) ◽  
pp. 486-501 ◽  
Author(s):  
P. Emsley ◽  
B. Lohkamp ◽  
W. G. Scott ◽  
K. Cowtan
Keyword(s):  
Model Building ◽  
Rapid Development ◽  
Real Space ◽  
Biological Macromolecules ◽  
Molecular Graphics ◽  
Interface Elements ◽  
Current State ◽  
Density Maps ◽  
Recent Developments ◽  
Novice Users

Cootis a molecular-graphics application for model building and validation of biological macromolecules. The program displays electron-density maps and atomic models and allows model manipulations such as idealization, real-space refinement, manual rotation/translation, rigid-body fitting, ligand search, solvation, mutations, rotamers and Ramachandran idealization. Furthermore, tools are provided for model validation as well as interfaces to external programs for refinement, validation and graphics. The software is designed to be easy to learn for novice users, which is achieved by ensuring that tools for common tasks are `discoverable' through familiar user-interface elements (menus and toolbars) or by intuitive behaviour (mouse controls). Recent developments have focused on providing tools for expert users, with customisable key bindings, extensions and an extensive scripting interface. The software is under rapid development, but has already achieved very widespread use within the crystallographic community. The current state of the software is presented, with a description of the facilities available and of some of the underlying methods employed.

scholarly journals XGen: Real-Space Fitting of Complex Ligand Conformational Ensembles to X-ray Electron Density Maps

2020 ◽  
Vol 63 (18) ◽  
pp. 10509-10528
Author(s):  
Ajay N. Jain ◽  
Ann E. Cleves ◽  
Alexander C. Brueckner ◽  
Charles A. Lesburg ◽  
Qiaolin Deng ◽  
...  
Keyword(s):  
Electron Density ◽  
Real Space ◽  
X Ray ◽  
Conformational Ensembles ◽  
Density Maps ◽  
Complex Ligand

Removing bias from solvent atoms in electron density maps

2008 ◽  
Vol 41 (4) ◽  
pp. 761-767 ◽  
Author(s):  
Eric N. Brown
Keyword(s):  
Crystal Structures ◽  
Electron Density ◽  
Protein Crystal ◽  
Water Molecules ◽  
Data Set ◽  
Atomic Structures ◽  
Research Article ◽  
Density Maps ◽  
Atom Position

Atomic structures of proteins determinedviaprotein crystallography contain numerous solvent atoms. The experimental data for the determination of a water molecule's O-atom position is often a small contained blob of unidentified electron density. Unfortunately, the nature of crystallographic refinement lets poorly placed solvent atoms bias the future refined positions of all atoms in the crystal structure. This research article presents the technique of omit-maps applied to remove the bias introduced by poorly determined solvent atoms, enabling the identification of incorrectly placed water molecules in partially refined crystal structures. A total of 160 protein crystal structures with 45 912 distinct water molecules were processed using this technique. Most of the water molecules in the deposited structures were well justified. However, a few of the solvent atoms in this test data set changed appreciably in position, displacement parameter or electron density when fitted to the solvent omit-map, raising questions about how much experimental support exists for these solvent atoms.

scholarly journals RNA Structure Refinement using the ERRASER-Phenix pipeline

2013 ◽  
Author(s):  
Fang-Chieh Chou ◽  
Nathaniel Echols ◽  
Thomas C. Terwilliger ◽  
Rhiju Das
Keyword(s):  
Electron Density ◽  
Rna Structure ◽  
Structure Refinement ◽  
Realistic Model ◽  
Real Space ◽  
Geometrical Quality ◽  
Density Maps ◽  
On Line ◽  
Rna Crystallography ◽  
Backbone Atoms

The final step of RNA crystallography involves the fitting of coordinates into electron density maps. The large number of backbone atoms in RNA presents a difficult and tedious challenge, particularly when experimental density is poor. The ERRASER-Phenix pipeline can improve an initial set of RNA coordinates automatically based on a physically realistic model of atomic-level RNA interactions. The pipeline couples diffraction-based refinement in Phenix with the Rosetta-based real-space refinement protocol ERRASER (Enumerative Real-Space Refinement ASsisted by Electron density under Rosetta). The combination of ERRASER and Phenix can improve the geometrical quality of RNA crystallographic models while maintaining or improving the fit to the diffraction data (as measured by Rfree). Here we present a complete tutorial for running ERRASER-Phenix through the Phenix GUI, from the command-line, and via an application in the Rosetta On-line Server that Includes Everyone (ROSIE).

Sign in / Sign up

Export Citation Format

Share Document