Automated refinement of macromolecular structures at low resolution using prior information

Kovalevskiy, Oleg; Nicholls, Robert A.; Murshudov, Garib N.

doi:10.1107/s2059798316014534

Cited by 65 publications

(55 citation statements)

References 29 publications

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“…Indeed, the MR method accounts for almost 80% of the solved structures deposited in the PDB. Apart from assisting in structure solution using MR, homology has been exploited for crystallographic model building and refinement when a sequence assignment is available ( van Beusekom, Touw et al, 2018;Kovalevskiy et al, 2016;Nicholls et al, 2012;Schrö der et al, 2010;Smart et al, 2012;Headd et al, 2012). It is intriguing that the majority of model-building tasks that have recently been submitted to the ARP/wARP web service had a homologous structure with a sequence identity of 35% (or greater) already available in the PDB.…”

Section: Discussionmentioning

confidence: 99%

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

Chojnowski¹,

Choudhury²,

Heuser³

et al. 2020

Acta Crystallogr D Struct Biol

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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.

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Section: Discussionmentioning

confidence: 99%

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

Chojnowski¹,

Choudhury²,

Heuser³

et al. 2020

Acta Crystallogr D Struct Biol

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“…Thus the crystallographer is faced with the often difficult and inevitably subjective decision of selecting the "best" model from a group of protein structure models as a reference. 12 Recently, this process was partly automated in the LORESTR pipeline, 13 which uses a series of different refinement protocols and reference restraints from ProS-MART, 6,8 to ultimately return the best result using restraints from the optimal reference model.…”

Section: Introductionmentioning

confidence: 99%

Homology‐based hydrogen bond information improves crystallographic structures in the PDB

et al. 2017

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The Protein Data Bank (PDB) is the global archive for structural information on macromolecules, and a popular resource for researchers, teachers, and students, amassing more than one million unique users each year. Crystallographic structure models in the PDB (more than 100,000 entries) are optimized against the crystal diffraction data and geometrical restraints. This process of crystallographic refinement typically ignored hydrogen bond (H‐bond) distances as a source of information. However, H‐bond restraints can improve structures at low resolution where diffraction data are limited. To improve low‐resolution structure refinement, we present methods for deriving H‐bond information either globally from well‐refined high‐resolution structures from the PDB‐REDO databank, or specifically from on‐the‐fly constructed sets of homologous high‐resolution structures. Refinement incorporating HOmology DErived Restraints (HODER), improves geometrical quality and the fit to the diffraction data for many low‐resolution structures. To make these improvements readily available to the general public, we applied our new algorithms to all crystallographic structures in the PDB: using massively parallel computing, we constructed a new instance of the PDB‐REDO databank (https://pdb-redo.eu). This resource is useful for researchers to gain insight on individual structures, on specific protein families (as we demonstrate with examples), and on general features of protein structure using data mining approaches on a uniformly treated dataset.

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“…In our tests, LORESTR was able to produce substantially better quality models in the vast majority of cases, improving both the R factors and the model geometry for 94% of the test cases (Kovalevskiy et al , 2016). The dramatic improvement in R factors and stereochemical quality of low-resolution models observed when using the fully automated mode of the pipeline demonstrates its potential utility in low-resolution cases, especially during the initial stages of refinement, or when the refinement process has stalled.…”

Section: External Restraintsmentioning

confidence: 84%

“…We have recently tested various refinement strategies and different REFMAC 5 and ProSMART parameters on a test set of more than 100 structures with resolution below 3.0 Å taken from the PDB. The best-performing refinement protocols and strategies have been implemented in LORESTR , an automated and easy-to-use pipeline for structure refinement at low resolution, which is distributed as part of the CCP 4 suite v.7.0 (Kovalevskiy et al , 2016; Winn et al , 2011). …”

Section: External Restraintsmentioning

confidence: 99%

“…The pipeline supports multitasking and can run several jobs in parallel, should the user so wish. After running all jobs, LORESTR selects the best-performing protocol according to a quality indicator (Kovalevskiy et al , 2016) that depends on both R free and the MolProbity score (Chen et al , 2010), or just simply R free if MolProbity is not available from a local PHENIX installation (Adams et al , 2010). The refined PDB and MTZ files corresponding to the best protocol are returned.…”

Section: External Restraintsmentioning

confidence: 99%

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Overview of refinement procedures withinREFMAC5: utilizing data from different sources

Kovalevskiy

Nicholls

Long

et al. 2018

Acta Crystallogr D Struct Biol

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224

194

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Here, a macromolecule-centred approach to three-dimensional structure determination as implemented in REFMAC5 is considered. The use of restraints to transfer chemical and structural information during macromolecular refinement, and how different sources of information can be combined in order to achieve models that are more consistent with data derived from a variety of experimental techniques, including macromolecular crystallography, cryo-EM and NMR spectroscopy, are discussed.

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Automated refinement of macromolecular structures at low resolution using prior information

Cited by 65 publications

References 29 publications

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

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

Homology‐based hydrogen bond information improves crystallographic structures in the PDB

Overview of refinement procedures withinREFMAC5: utilizing data from different sources

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