3DRobot: automated generation of diverse and well-packed protein structure decoys

Deng, Haiyou; Jia, Ya; Zhang, Yang

doi:10.1093/bioinformatics/btv601

Cited by 79 publications

(106 citation statements)

References 38 publications

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“…A straightforward way to generate a pool of structures of varying accuracy is to calculate decoys (physically plausible computationally generated models). We used the 3DRobot web server 32 , which begins from a crystal or NMR structure, identifies possible structure scaffolds from a library, assembles them together, and then refines them, to produce a diverse and well-packed collection of decoys. For about half (79 of 173) of the ensembles in the CNW75 dataset (see Supplementary Information for a list of the chosen models), we calculated a group of 300 decoys.…”

Section: Decoy Vs Experimental Structuresmentioning

confidence: 99%

“…The experimental structure is indicated by an asterisk and is the best scoring model in the NMR ensemble, according to our method. The other data show decoys generated by 3DRobot 32 , and color coded by their Global Distance Test (GDT), a measure of similarity to the target 33 , as indicated by the color bar on the right. For two proteins, red boxes indicate the set of decoys used to calculate mean hydrogen bond correctness, as discussed in the text.…”

Section: Decoy Vs Experimental Structuresmentioning

confidence: 99%

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A method for validating the accuracy of NMR protein structures

Fowler

Sljoka

Williamson

2020

Preprint

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We present a method, Accuracy of NMR Structures using Random Coil Index and Rigidity (ANSURR), that measures the accuracy of NMR protein structures. It provides a residue-by-residue comparison of two measures of local rigidity: the Random Coil Index [RCI] (a measure of the extent to which backbone chemical shifts adopt random coil values); and local rigidity predicted by mathematical rigidity theory using the computational method Floppy Inclusion and Rigid Substructure Topology [FIRST], calculated from an NMR structural model. We compare RCI and FIRST using a correlation score (which assesses the location of secondary structure), and an RMSD score (which measures overall rigidity, and mainly assesses hydrogen bond correctness). We test the performance of ANSURR using: (a) structures refined in explicit solvent, which have much better RMSD score than unrefined structures, though similar correlation; (b) decoy structures generated for 89 NMR structures. The experimental NMR structures are usually better, though helical and sheet structures behave differently; (c) conventional predictors of structural accuracy such as number of restraints per residue, restraint violations, energy of structure, RMSD of the ensemble (precision of the calculation), Ramachandran distribution, and clashscore. Comparisons of NMR to crystal structures show that secondary structure is equally accurate in both, but crystal structures tend to be too rigid in loops, whereas NMR structures tend to be too floppy overall.

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Section: Decoy Vs Experimental Structuresmentioning

confidence: 99%

Section: Decoy Vs Experimental Structuresmentioning

confidence: 99%

A method for validating the accuracy of NMR protein structures

Fowler

Sljoka

Williamson

2020

Preprint

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show abstract

“…To examine how well the approach would work with actual experimental saturation mutagenesis datasets, the mutational sensitivity data available for five different proteins were analyzed. Datasets of structural decoys for PSD95 pdz3 , GB1, Pab1, and TEM1 β‐lactamase were generated using the 3DRobot server available at http://zhanglab.ccmb.med.umich.edu/3DRobot/ . A 1000 decoys were generated for each protein.…”

Section: Resultsmentioning

confidence: 99%

“…Decoy datasets for PSD95 pdz3 , GB1, Pab1, and TEM1 β‐lactamase were generated using the 3DRobot server available at http://zhanglab.ccmb.med.umich.edu/3DRobot/ . The number of decoys was specified as 1000. An RMSD cutoff of 20 Å was specified in order to sample low‐quality models along with high‐quality models.…”

Section: Methodsmentioning

confidence: 99%

Protein model discrimination attempts using mutational sensitivity, predicted secondary structure, and model quality information

et al. 2019

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Structure prediction methods often generate a large number of models for a target sequence. Even if the correct fold for the target sequence is sampled in this dataset, it is difficult to distinguish it from other decoy structures. An attempt to solve this problem using experimental mutational sensitivity data for the CcdB protein was described previously by exploiting the correlation of residue depth with mutational sensitivity (r ~ 0.6). We now show that such a correlation extends to four other proteins with localized active sites, and for which saturation mutagenesis datasets exist. We also examine whether incorporation of predicted secondary structure information and the DOPE model quality assessment score, in addition to mutational sensitivity, improves the accuracy of model discrimination using a decoy dataset of 163 targets from CASP. Although most CASP models would have been subjected to model quality assessment prior to submission, we find that the DOPE score makes a substantial contribution to the observed improvement. We therefore also applied the approach to CcdB and four other proteins for which reliable experimental mutational data exist and observe that inclusion of experimental mutational data results in a small qualitative improvement in model discrimination relative to that seen with just the DOPE score. This is largely because of our limited ability to quantitatively predict effects of point mutations on in vivo protein activity. Further improvements in the methodology are required to facilitate improved utilization of single mutant data.

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“…Rosetta 50-146 aa 58 100 (Simons et al, 1997;Deng et al, 2015) I-TASSER (b) 47-118 aa 56 400 (Zhang and Zhang, 2010; Deng et al,…”

Section: Testing Decoy Datasetsmentioning

confidence: 99%

NEPRE: a Scoring Function for Protein Structures based on Neighbourhood Preference

Liu

Xiang

Liu

2018

Preprint

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Protein structure prediction relies on two major components, a method to generate good models that are close to the native structure and a scoring function that can select the good models. Based on the statistics from known structures in the protein data bank, a statistical energy function is derived to reflect the amino acid neighbourhood preferences. The neighbourhood of one amino acid is defined by its contacting residues, and the energy function is determined by the neighbhoring residue types and relative positions. A scoring algorithm, Nepre, has been implemented and its performance was tested with several decoy sets. The results show that the Nepre program can be applied in model ranking to improve the success rate in structure predictions.

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3DRobot: automated generation of diverse and well-packed protein structure decoys

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 79 publications

References 38 publications

A method for validating the accuracy of NMR protein structures

A method for validating the accuracy of NMR protein structures

Protein model discrimination attempts using mutational sensitivity, predicted secondary structure, and model quality information

NEPRE: a Scoring Function for Protein Structures based on Neighbourhood Preference

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