ArticleBioinformatics (Oxford, England)2021
GraphQA: protein model quality assessment using graph convolutional networks.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- Edge-aware GAT-based protein binding sites prediction.PLoS computational biology · 2026Article
- InversePep: Diffusion-driven structure-based inverse folding for functional peptides.Briefings in bioinformatics · 2026Article
- Spectral Graph Features for Reference-free RNA 3D Quality Assessment.bioRxiv : the preprint server for biology · 2026Article
- Graph Learning in Bioinformatics: A Survey of Graph Neural Network Architectures, Biological Graph Construction and Bioinformatics Applications.Biomolecules · 2026Review
- Molecular Motif Learning as a pretraining objective for molecular property prediction.Nature communications · 2025Article
- Protein Structural Model Selection Informed by Comparison of Predicted Ligand Binding Poses.Journal of chemical information and modeling · 2025Article
- BridgeNet: a high-efficiency framework integrating sequence and structure for protein and enzyme function prediction.Briefings in bioinformatics · 2025Article
- AlphaFold model quality self-assessment improvement via deep graph learning.Protein science : a publication of the Protein Society · 2025Article
- Integrating ESM‑2 and Graph Neural Networks with AlphaFold‑2 Structures for Enhanced Protein Function Prediction.ACS omega · 2025Article
- SST-ResNet: A Sequence and Structure Information Integration Model for Protein Property Prediction.International journal of molecular sciences · 2025Article
- $\mathcal{S}$ able: bridging the gap in protein structure understanding with an empowering and versatile pre-training paradigm.Briefings in bioinformatics · 2025Article
- TopoQA: a topological deep learning-based approach for protein complex structure interface quality assessment.Briefings in bioinformatics · 2025Article
- Post-processing enhances protein secondary structure prediction with second order deep learning and embeddings.Computational and structural biotechnology journal · 2025Article
- Novel Insights into Enzymatic Thermostability: The "Short Board" Theory and Zero-Shot Hamiltonian Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Recent advances and challenges in protein complex model accuracy estimation.Computational and structural biotechnology journal · 2024Review
- ProteinF3S: boosting enzyme function prediction by fusing protein sequence, structure, and surface.Briefings in bioinformatics · 2024Article
- Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering.Cell research · 2024Article
- EGG: Accuracy Estimation of Individual Multimeric Protein Models Using Deep Energy-Based Models and Graph Neural Networks.International journal of molecular sciences · 2024Article
- Protein structure accuracy estimation using geometry-complete perceptron networks.Protein science : a publication of the Protein Society · 2024Article
- Geometry-complete perceptron networks for 3D molecular graphs.Bioinformatics (Oxford, England) · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationProteins are ubiquitous molecules whose function in biological processes is determined by their 3D structure. Experimental identification of a protein's structure can be time-consuming, prohibitively expensive and not always possible. Alternatively, protein folding can be modeled using computational methods, which however are not guaranteed to always produce optimal results. GraphQA is a graph-based method to estimate the quality of protein models, that possesses favorable properties such as representation learning, explicit modeling of both sequential and 3D structure, geometric invariance and computational efficiency.
resultsGraphQA performs similarly to state-of-the-art methods despite using a relatively low number of input features. In addition, the graph network structure provides an improvement over the architecture used in ProQ4 operating on the same input features. Finally, the individual contributions of GraphQA components are carefully evaluated. AVAILABILITY AND IMPLEMENTATION: PyTorch implementation, datasets, experiments and link to an evaluation server are available through this GitHub repository: github.com/baldassarreFe/graphqa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.