Correction to “Dried Protein Structure Revealed at the Residue Level by Liquid-Observed Vapor Exchange NMR”

Abstract Motivation With the great progress of deep learning-based inter-residue contact/distance prediction, the discrete space formed by fragment assembly cannot satisfy the distance constraint well. Thus, the optimal solution of the continuous space may not be achieved. Designing an effective closed-loop continuous dihedral angle optimization strategy that complements the discrete fragment assembly is crucial to improve the performance of the distance-assisted fragment assembly method. Results In this article, we proposed a de novo protein structure prediction method called IPTDFold based on closed-loop iterative partition sampling, topology adjustment and residue-level distance deviation optimization. First, local dihedral angle crossover and mutation operators are designed to explore the conformational space extensively and achieve information exchange between the conformations in the population. Then, the dihedral angle rotation model of loop region with partial inter-residue distance constraints is constructed, and the rotation angle satisfying the constraints is obtained by differential evolution algorithm, so as to adjust the spatial position relationship between the secondary structures. Lastly, the residue distance deviation is evaluated according to the difference between the conformation and the predicted distance, and the dihedral angle of the residue is optimized with biased probability. The final model is generated by iterating the above three steps. IPTDFold is tested on 462 benchmark proteins, 24 FM targets of CASP13, and 20 FM targets of CASP14. Results show that IPTDFold is significantly superior to the distance-assisted fragment assembly method Rosetta_D (Rosetta with distance). In particular, the prediction accuracy of IPTDFold does not decrease as the length of the protein increases. When using the same FastRelax protocol, the prediction accuracy of IPTDFold is significantly superior to that of trRosetta without orientation constraints, and is equivalent to that of the full version of trRosetta. Availability The source code and executable are freely available at https://github.com/iobio-zjut/IPTDFold. Supplementary information Supplementary data are available at Bioinformatics online.

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QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks

Bioinformatics ◽

10.1093/bioinformatics/btaa455 ◽

2020 ◽

Vol 36 (Supplement_1) ◽

pp. i285-i291 ◽

Cited By ~ 1

Author(s):

Md Hossain Shuvo ◽

Sutanu Bhattacharya ◽

Debswapna Bhattacharya

Keyword(s):

Neural Networks ◽

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Residue Level ◽

Supplementary Information ◽

Model Quality ◽

Quality Estimation ◽

Distance Information ◽

Protein Model

Abstract Motivation Protein model quality estimation, in many ways, informs protein structure prediction. Despite their tight coupling, existing model quality estimation methods do not leverage inter-residue distance information or the latest technological breakthrough in deep learning that has recently revolutionized protein structure prediction. Results We present a new distance-based single-model quality estimation method called QDeep by harnessing the power of stacked deep residual neural networks (ResNets). Our method first employs stacked deep ResNets to perform residue-level ensemble error classifications at multiple predefined error thresholds, and then combines the predictions from the individual error classifiers for estimating the quality of a protein structural model. Experimental results show that our method consistently outperforms existing state-of-the-art methods including ProQ2, ProQ3, ProQ3D, ProQ4, 3DCNN, MESHI, and VoroMQA in multiple independent test datasets across a wide-range of accuracy measures; and that predicted distance information significantly contributes to the improved performance of QDeep. Availability and implementation https://github.com/Bhattacharya-Lab/QDeep. Supplementary information Supplementary data are available at Bioinformatics online.

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QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks

10.1101/2020.01.31.928622 ◽

2020 ◽

Author(s):

Md Hossain Shuvo ◽

Sutanu Bhattacharya ◽

Debswapna Bhattacharya

Keyword(s):

Neural Networks ◽

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Residue Level ◽

Estimation Methods ◽

Model Quality ◽

Quality Estimation ◽

Distance Information ◽

Protein Model

AbstractMotivationProtein model quality estimation, in many ways, informs protein structure prediction. Despite their tight coupling, existing model quality estimation methods do not leverage inter-residue distance information or the latest technological breakthrough in deep learning that has recently revolutionized protein structure prediction.ResultsWe present a new distance-based single-model quality estimation method called QDeep by harnessing the power of stacked deep residual neural networks (ResNets). Our method first employs stacked deep ResNets to perform residue-level ensemble error classifications at multiple predefined error thresholds, and then combines the predictions from the individual error classifiers for estimating the quality of a protein structural model. Experimental results show that our method consistently out-performs existing state-of-the-art methods including ProQ2, ProQ3, ProQ3D, ProQ4, 3DCNN, MESHI, and VoroMQA in multiple independent test datasets across a wide-range of accuracy measures; and that predicted distance information significantly contributes to the improved performance of QDeep.Availabilityhttps://github.com/Bhattacharya-Lab/[email protected]

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A de novo protein structure prediction by iterative partition sampling, topology adjustment, and residue-level distance deviation optimization

10.1101/2021.05.12.443769 ◽

2021 ◽

Author(s):

Jun Liu ◽

Kailong Zhao ◽

Guangxing He ◽

Liujing Wang ◽

Xiaogen Zhou ◽

...

Keyword(s):

Protein Structure ◽

Dihedral Angle ◽

Structure Prediction ◽

Prediction Accuracy ◽

Closed Loop ◽

De Novo ◽

Residue Level ◽

Fragment Assembly ◽

Assembly Method ◽

Distance Deviation

Motivation: With the great progress of deep learning-based inter-residue contact/distance prediction, the discrete space formed by fragment assembly cannot satisfy the distance constraint well. Thus, the optimal solution of the continuous space may not be achieved. Designing an effective closed-loop continuous dihedral angle optimization strategy that complements the discrete fragment assembly is crucial to improve the performance of the distance-assisted fragment assembly method. Results: In this article, we proposed a de novo protein structure prediction method called IPTDFold based on closed-loop iterative partition sampling, topology adjustment and residue-level distance deviation optimization. First, local dihedral angle crossover and mutation operators are designed to explore the conformational space extensively and achieve information exchange between the conformations in the population. Then, the dihedral angle rotation model of loop region with partial inter-residue distance constraints is constructed, and the rotation angle satisfying the constraints is obtained by differential evolution algorithm, so as to adjust the spatial position relationship between the secondary structures. Lastly, the residue distance deviation is evaluated according to the difference between the conformation and the predicted distance, and the dihedral angle of the residue is optimized with biased probability. The final model is generated by iterating the above three steps. IPTDFold is tested on 462 benchmark proteins, 24 FM targets of CASP13, and 20 FM targets of CASP14. Results show that IPTDFold is significantly superior to the distance-assisted fragment assembly method Rosetta_D (Rosetta with distance). In particular, the prediction accuracy of IPTDFold does not decrease as the length of the protein increases. When using the same FastRelax protocol, the prediction accuracy of IPTDFold is significantly superior to that of trRosetta without orientation constraints, and is equivalent to that of the full version of trRosetta.

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Protein structure and interactions

Food Colloids ◽

10.1039/9781847550842-00243 ◽

2007 ◽

pp. 243-244 ◽

Cited By ~ 1

Keyword(s):

Protein Structure

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Solid-state NMR study of protein structure. Methods based on the measurement of internuclear distances

Journal de Chimie Physique ◽

10.1051/jcp/1995921751 ◽

1995 ◽

Vol 92 ◽

pp. 1751-1760 ◽

Cited By ~ 12

Author(s):

M Auger

Keyword(s):

Protein Structure ◽

Solid State ◽

Solid State Nmr ◽

Nmr Study ◽

Internuclear Distances

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Sequence Specific Dihedral Angle Distribution: Application in Protein Structure Prediction and Evaluation

Plant Tissue Culture and Biotechnology ◽

10.3329/ptcb.v19i2.5439 ◽

1970 ◽

Vol 19 (2) ◽

pp. 217-226

Author(s):

S. M. Minhaz Ud-Dean ◽

Mahdi Muhammad Moosa

Keyword(s):

Protein Structure ◽

Dihedral Angle ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Protein Structures ◽

Angle Distribution ◽

Ramachandran Plot ◽

Specific Data ◽

Specific Distribution ◽

Structure Evaluation

Protein structure prediction and evaluation is one of the major fields of computational biology. Estimation of dihedral angle can provide information about the acceptability of both theoretically predicted and experimentally determined structures. Here we report on the sequence specific dihedral angle distribution of high resolution protein structures available in PDB and have developed Sasichandran, a tool for sequence specific dihedral angle prediction and structure evaluation. This tool will allow evaluation of a protein structure in pdb format from the sequence specific distribution of Ramachandran angles. Additionally, it will allow retrieval of the most probable Ramachandran angles for a given sequence along with the sequence specific data. Key words: Torsion angle, φ-ψ distribution, sequence specific ramachandran plot, Ramasekharan, protein structure appraisal D.O.I. 10.3329/ptcb.v19i2.5439 Plant Tissue Cult. & Biotech. 19(2): 217-226, 2009 (December)

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