ArticleBioinformatics (Oxford, England)2020
QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- ORIGAMI: Orientation-Aware Graph Neural Network for Assessing Multimeric Interfaces of Protein Complex Structures.Journal of chemical information and modeling · 2026Article
- ORIGAMI: Orientation-Aware Graph Neural Network for Assessing Multimeric Interfaces of Protein Complex Structures.bioRxiv : the preprint server for biology · 2026Article
- EquiRank: Improved protein-protein interface quality estimation using protein language-model-informed equivariant graph neural networks.Computational and structural biotechnology journal · 2025Article
- Estimating protein complex model accuracy using graph transformers and pairwise similarity graphs.Bioinformatics advances · 2025Article
- Recent advances and challenges in protein complex model accuracy estimation.Computational and structural biotechnology journal · 2024Review
- DeepUMQA3: a web server for accurate assessment of interface residue accuracy in protein complexes.Bioinformatics (Oxford, England) · 2023Article
- iQDeep: an integrated web server for protein scoring using multiscale deep learning models.Journal of molecular biology · 2023Article
- Contact-Assisted Threading in Low-Homology Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2023Article
- PIQLE: protein-protein interface quality estimation by deep graph learning of multimeric interaction geometries.Bioinformatics advances · 2023Article
- Article
- Predicting residue-specific qualities of individual protein models using residual neural networks and graph neural networks.Proteins · 2022Article
- Estimation of model accuracy by a unique set of features and tree-based regressor.Scientific reports · 2022Article
- Article
- DISTEMA: distance map-based estimation of single protein model accuracy with attentive 2D convolutional neural network.BMC bioinformatics · 2022Article
- A Benchmark Dataset for Evaluating Practical Performance of Model Quality Assessment of Homology Models.Bioengineering (Basel, Switzerland) · 2022Article
- DeepRefiner: high-accuracy protein structure refinement by deep network calibration.Nucleic acids research · 2021Article
- Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14.Scientific reports · 2021Article
- Deep Learning-Based Advances in Protein Structure Prediction.International journal of molecular sciences · 2021Review
- Recent Advances in Protein Homology Detection Propelled by Inter-Residue Interaction Map Threading.Frontiers in molecular biosciences · 2021Review
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3 authors.
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Abstract
motivationProtein 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 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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