scholarly journals Cryo‐EM map interpretation and protein model‐building using iterative map segmentation

2019 ◽  
Vol 29 (1) ◽  
pp. 87-99 ◽  
Author(s):  
Thomas C. Terwilliger ◽  
Paul D. Adams ◽  
Pavel V. Afonine ◽  
Oleg V. Sobolev
Keyword(s):  
Model Building ◽  
Map Interpretation ◽  
Protein Model ◽  
Map Segmentation

scholarly journals Pairwise running of automated crystallographic model-building pipelines

2020 ◽  
Vol 76 (9) ◽  
pp. 814-823 ◽  
Author(s):  
Emad Alharbi ◽  
Radu Calinescu ◽  
Kevin Cowtan
Keyword(s):  
Model Building ◽  
Protein Structures ◽  
Data Sets ◽  
Protein Model

For the last two decades, researchers have worked independently to automate protein model building, and four widely used software pipelines have been developed for this purpose: ARP/wARP, Buccaneer, Phenix AutoBuild and SHELXE. Here, the usefulness of combining these pipelines to improve the built protein structures by running them in pairwise combinations is examined. The results show that integrating these pipelines can lead to significant improvements in structure completeness and R free. In particular, running Phenix AutoBuild after Buccaneer improved structure completeness for 29% and 75% of the data sets that were examined at the original resolution and at a simulated lower resolution, respectively, compared with running Phenix AutoBuild on its own. In contrast, Phenix AutoBuild alone produced better structure completeness than the two pipelines combined for only 7% and 3% of these data sets.

scholarly journals Map segmentation, automated model-building and their application to the Cryo-EM Model Challenge

2018 ◽  
Author(s):  
Thomas C. Terwilliger ◽  
Paul D. Adams ◽  
Pavel V. Afonine ◽  
Oleg V. Sobolev
Keyword(s):  
Model Building ◽  
Map Segmentation ◽  
Segmentation Approach ◽  
Density Map ◽  
Automated Model Building ◽  
Unique Part

AbstractA recently-developed method for identifying a compact, contiguous region representing the unique part of a density map was applied to 218 cryo-EM maps with resolutions of 4.5 Å or better. The key elements of the segmentation procedure are (1) identification of all regions of density above a threshold and (2) choice of a unique set of these regions, taking symmetry into consideration, that maximize connectivity and compactness. This segmentation approach was then combined with tools for automated map sharpening and model-building to generate models for the 12 maps in the 2016 cryo-EM model challenge in a fully automated manner. The resulting models have completeness from 24% to 82% and RMS distances from reference interpretations of 0.6 Å to 2.1 Å.

scholarly journals Crystallographic protein model-building on the web

2006 ◽  
Vol 23 (3) ◽  
pp. 375-377 ◽  
Author(s):  
K. Gopal ◽  
E. McKee ◽  
T. Romo ◽  
R. Pai ◽  
J. Smith ◽  
...  
Keyword(s):  
Model Building ◽  
Protein Model ◽  
The Web

scholarly journals Comparison of automated crystallographic model-building pipelines

2019 ◽  
Vol 75 (12) ◽  
pp. 1119-1128 ◽  
Author(s):  
Emad Alharbi ◽  
Paul S. Bond ◽  
Radu Calinescu ◽  
Kevin Cowtan
Keyword(s):  
Model Building ◽  
Protein Structures ◽  
Data Sets ◽  
Protein Model ◽  
Using Data ◽  
Complete Protein

A comparison of four protein model-building pipelines (ARP/wARP, Buccaneer, PHENIX AutoBuild and SHELXE) was performed using data sets from 202 experimentally phased cases, both with the data as observed and truncated to simulate lower resolutions. All pipelines were run using default parameters. Additionally, an ARP/wARP run was completed using models from Buccaneer. All pipelines achieved nearly complete protein structures and low R work/R free at resolutions between 1.2 and 1.9 Å, with PHENIX AutoBuild and ARP/wARP producing slightly lower R factors. At lower resolutions, Buccaneer leads to significantly more complete models.

scholarly journals A mosaic bulk-solvent model improves density maps and the fit between model and data

2021 ◽  
Author(s):  
Pavel V. Afonine ◽  
Paul D. Adams ◽  
Oleg V Sobolev ◽  
Alexandre Urzhumtsev
Keyword(s):  
Protein Data Bank ◽  
Model Building ◽  
Data Bank ◽  
Crystallographic Data ◽  
Software Packages ◽  
Map Interpretation ◽  
Density Maps ◽  
Solvent Model ◽  
Modeling Power ◽  
R Factors

Bulk solvent is a major component of bio-macromolecular crystals and therefore contributes significantly to diffraction intensities. Accurate modeling of the bulk-solvent region has been recognized as important for many crystallographic calculations, from computing of R-factors and density maps to model building and refinement. Owing to its simplicity and computational and modeling power, the flat (mask-based) bulk-solvent model introduced by Jiang & Brunger (1994) is used by most modern crystallographic software packages to account for disordered solvent. In this manuscript we describe further developments of the mask-based model that improves the fit between the model and the data and aids in map interpretation. The new algorithm, here referred to as mosaic bulk-solvent model, considers solvent variation across the unit cell. The mosaic model is implemented in the computational crystallography toolbox and can be used in Phenix in most contexts where accounting for bulk-solvent is required. It has been optimized and validated using a sufficiently large subset of the Protein Data Bank entries that have crystallographic data available.

scholarly journals Sequence assignment validation in cryo-EM models with checkMySequence

2022 ◽  
Author(s):  
Grzegorz Chojnowski
Keyword(s):  
Structural Biology ◽  
Structure Prediction ◽  
Model Building ◽  
Visual Interpretation ◽  
Interactive Tools ◽  
Map Interpretation ◽  
Map Resolution ◽  
Protein Models ◽  
Register Error ◽  
Sequence Assignment

The availability of new AI-based protein structure prediction tools radically changed the way cryo-EM maps are interpreted, but it has not eliminated the challenges of map interpretation faced by a microscopist. Models will continue to be locally rebuilt and refined using interactive tools. This inevitably results in occasional errors, among which register-shifts remain one of the most difficult to identify and correct. Here we introduce checkMySequence; a fast, fully automated and parameter-free method for detecting register-shifts in protein models built into cryo-EM maps. We show that the method can assist model building in cases where poorer map resolution hinders visual interpretation. We also show that checkMySequence could have helped avoid a widely discussed sequence register error in a model of SARS-CoV-2 RNA-dependent RNA polymerase that was originally detected thanks to a visual residue-by-residue inspection by members of the structural biology community.

scholarly journals Current approaches for automated model building into cryo-EM maps using Buccaneer with CCP-EM

2020 ◽  
Vol 76 (6) ◽  
pp. 531-541
Author(s):  
Soon Wen Hoh ◽  
Tom Burnley ◽  
Kevin Cowtan
Keyword(s):  
Model Building ◽  
Data Sets ◽  
Structural Interpretation ◽  
X Ray ◽  
X Ray Crystallography ◽  
Protein Model ◽  
Reference Map ◽  
Automated Model Building ◽  
Better Than

This work focuses on the use of the existing protein-model-building software Buccaneer to provide structural interpretation of electron cryo-microscopy (cryo-EM) maps. Originally developed for application to X-ray crystallography, the necessary steps to optimise the usage of Buccaneer with cryo-EM maps are shown. This approach has been applied to the data sets of 208 cryo-EM maps with resolutions of better than 4 Å. The results obtained also show an evident improvement in the sequencing step when the initial reference map and model used for crystallographic cases are replaced by a cryo-EM reference. All other necessary changes to settings in Buccaneer are implemented in the model-building pipeline from within the CCP-EM interface (as of version 1.4.0).

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