Protein Model Building Using Structural Similarity

1996 ◽  
pp. 65-119
Author(s):  
Raúl E. Cachau
Keyword(s):  
Model Building ◽  
Structural Similarity ◽  
Protein Model

scholarly journals The use of local structural similarity of distant homologues for crystallographic model building from a molecular-replacement solution

2020 ◽  
Vol 76 (3) ◽  
pp. 248-260 ◽  
Author(s):  
Grzegorz Chojnowski ◽  
Koushik Choudhury ◽  
Philipp Heuser ◽  
Egor Sobolev ◽  
Joana Pereira ◽  
...  
Keyword(s):  
Model Building ◽  
Structural Information ◽  
Structural Similarity ◽  
Molecular Replacement ◽  
Sequence Coverage ◽  
Protein Model ◽  
Model Extension ◽  
Replacement Solution ◽  
Almost All ◽  
Protein Models

The performance of automated protein model building usually decreases with resolution, mainly owing to the lower information content of the experimental data. This calls for a more elaborate use of the available structural information about macromolecules. Here, a new method is presented that uses structural homologues to improve the quality of protein models automatically constructed using ARP/wARP. The method uses local structural similarity between deposited models and the model being built, and results in longer main-chain fragments that in turn can be more reliably docked to the protein sequence. The application of the homology-based model extension method to the example of a CFA synthase at 2.7 Å resolution resulted in a more complete model with almost all of the residues correctly built and docked to the sequence. The method was also evaluated on 1493 molecular-replacement solutions at a resolution of 4.0 Å and better that were submitted to the ARP/wARP web service for model building. A significant improvement in the completeness and sequence coverage of the built models has been observed.

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 Structure-Based Discovery and Synthesis of Potential Transketolase Inhibitors

Molecules ◽  
2018 ◽  
Vol 23 (9) ◽  
pp. 2116 ◽  
Author(s):  
Jingqian Huo ◽  
Bin Zhao ◽  
Zhe Zhang ◽  
Jihong Xing ◽  
Jinlin Zhang ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Structural Similarity ◽  
Biological Evaluation ◽  
Dynamics Simulation ◽  
Antifungal Activities ◽  
Protein Model ◽  
Zinc Database ◽  
Amide Derivatives ◽  
Plant Photosynthesis ◽  
New Compounds

Transketolase (TKL) plays a key role in plant photosynthesis and has been predicted to be a potent herbicide target. Homology modeling and molecular dynamics simulation were used to construct a target protein model. A target-based virtual screening was developed to discover novel potential transketolase inhibitors. Based on the receptor transketolase 1 and a target-based virtual screening combined with structural similarity, six new compounds were selected from the ZINC database. Among the structural leads, a new compound ZINC12007063 was identified as a novel inhibitor of weeds. Two novel series of carboxylic amide derivatives were synthesized, and their structures were rationally identified by NMR and HRMS. Biological evaluation of the herbicidal and antifungal activities indicated that the compounds 4u and 8h were the most potent herbicidal agents, and they also showed potent fungicidal activity with a relatively broad-spectrum. ZINC12007063 was identified as a lead compound of potential transketolase inhibitors, 4u and 8h which has the herbicidal and antifungal activities were synthesized based on ZINC12007063. This study lays a foundation for the discovery of new pesticides.

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 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 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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