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 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.

Using known substructures in protein model building and crystallography.

1986 ◽  
Vol 5 (4) ◽  
pp. 819-822 ◽  
Author(s):  
T.A. Jones ◽  
S. Thirup
Keyword(s):  
Model Building ◽  
Protein Model

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 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).

scholarly journals Predicting protein model correctness in Coot using machine learning

2020 ◽  
Vol 76 (8) ◽  
pp. 713-723 ◽  
Author(s):  
Paul S. Bond ◽  
Keith S. Wilson ◽  
Kevin D. Cowtan
Keyword(s):  
Neural Networks ◽  
Test Performance ◽  
Model Building ◽  
Main Chain ◽  
Side Chains ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Protein Model ◽  
Density Values ◽  
Automated Model Building

Manually identifying and correcting errors in protein models can be a slow process, but improvements in validation tools and automated model-building software can contribute to reducing this burden. This article presents a new correctness score that is produced by combining multiple sources of information using a neural network. The residues in 639 automatically built models were marked as correct or incorrect by comparing them with the coordinates deposited in the PDB. A number of features were also calculated for each residue using Coot, including map-to-model correlation, density values, B factors, clashes, Ramachandran scores, rotamer scores and resolution. Two neural networks were created using these features as inputs: one to predict the correctness of main-chain atoms and the other for side chains. The 639 structures were split into 511 that were used to train the neural networks and 128 that were used to test performance. The predicted correctness scores could correctly categorize 92.3% of the main-chain atoms and 87.6% of the side chains. A Coot ML Correctness script was written to display the scores in a graphical user interface as well as for the automatic pruning of chains, residues and side chains with low scores. The automatic pruning function was added to the CCP4i2 Buccaneer automated model-building pipeline, leading to significant improvements, especially for high-resolution structures.

scholarly journals Phasing with calcium at home

2019 ◽  
Vol 75 (5) ◽  
pp. 377-384 ◽  
Author(s):  
Shuaiqi Guo ◽  
Robert Campbell ◽  
Peter L. Davies ◽  
John S. Allingham
Keyword(s):  
Model Building ◽  
De Novo ◽  
Protein Structure Determination ◽  
Adhesion Protein ◽  
Calcium Dependent ◽  
Protein Model ◽  
First Choice ◽  
Sad Phasing ◽  
Metal Cofactors ◽  
Automated Model Building

With better tools for data processing and with synchrotron beamlines that are capable of collecting data at longer wavelengths, sulfur-based native single-wavelength anomalous dispersion (SAD) phasing has become the `first-choice' method for de novo protein structure determination. However, for many proteins native SAD phasing can be simplified by taking advantage of their interactions with natural metal cofactors that are stronger anomalous scatterers than sulfur. This is demonstrated here for four unique domains of a 1.5 MDa calcium-dependent adhesion protein using the anomalous diffraction of the chelated calcium ions. In all cases, low anomalous multiplicity X-ray data were collected on a home-source diffractometer equipped with a chromium rotating anode (λ = 2.2909 Å). In all but one case, calcium SAD phasing alone was sufficient to allow automated model building and refinement of the protein model after the calcium substructure had been determined. Given that Ca atoms will be present in a significant percentage of proteins that remain uncharacterized, many aspects of the data-collection and processing methods described here could be broadly applied for routine de novo structure elucidation.

scholarly journals Cyclic Automated Model Building (CAB) Applied to Nucleic Acids

Crystals ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 280
Author(s):  
Maria Cristina Burla ◽  
Benedetta Carrozzini ◽  
Giovanni Luca Cascarano ◽  
Carmelo Giacovazzo ◽  
Giampiero Polidori
Keyword(s):  
Nucleic Acid ◽  
Nucleic Acids ◽  
Model Building ◽  
State Of The Art ◽  
Molecular Replacement ◽  
Dna And Rna ◽  
Protein Model ◽  
Rna Fragments ◽  
Nucleic Acid Structures ◽  
Automated Model Building

Obtaining high-quality models for nucleic acid structures by automated model building programs (AMB) is still a challenge. The main reasons are the rather low resolution of the diffraction data and the large number of rotatable bonds in the main chains. The application of the most popular and documented AMB programs (e.g., PHENIX.AUTOBUILD, NAUTILUS and ARP/wARP) may provide a good assessment of the state of the art. Quite recently, a cyclic automated model building (CAB) package was described; it is a new AMB approach that makes the use of BUCCANEER for protein model building cyclic without modifying its basic algorithms. The applications showed that CAB improves the efficiency of BUCCANEER. The success suggested an extension of CAB to nucleic acids—in particular, to check if cyclically including NAUTILUS in CAB may improve its effectiveness. To accomplish this task, CAB algorithms designed for protein model building were modified to adapt them to the nucleic acid crystallochemistry. CAB was tested using 29 nucleic acids (DNA and RNA fragments). The phase estimates obtained via molecular replacement (MR) techniques were automatically submitted to phase refinement and then used as input for CAB. The experimental results from CAB were compared with those obtained by NAUTILUS, ARP/wARP and PHENIX.AUTOBUILD.

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