ArticleNature computational science2021
Fast and effective protein model refinement using deep graph neural networks.
Article in Nature computational science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Evaluation of the structural models of the human reference proteome: AlphaFold2 versus ESMFold.Current research in structural biology · 2025Article
- Beyond AlphaFold2: The Impact of AI for the Further Improvement of Protein Structure Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Development of multi-epitope Cathepsin L driven short peptide vaccine againstFrontiers in veterinary science · 2025Article
- EquiRank: Improved protein-protein interface quality estimation using protein language-model-informed equivariant graph neural networks.Computational and structural biotechnology journal · 2025Article
- Recent advances and challenges in protein complex model accuracy estimation.Computational and structural biotechnology journal · 2024Review
- DGCPPISP: a PPI site prediction model based on dynamic graph convolutional network and two-stage transfer learning.BMC bioinformatics · 2024Article
- Selection among site-dependent structurally constrained substitution models of protein evolution by approximate Bayesian computation.Bioinformatics (Oxford, England) · 2024Article
- Amalgamated Pharmacoinformatics Study to Investigate the Mechanism of Xiao Jianzhong Tang against Chronic Atrophic Gastritis.Current computer-aided drug design · 2024Article
- Assessment of the Performances of the Protein Modeling Techniques Participating in CASP15 Using a Structure-Based Functional Site Prediction Approach: ResiRole.Bioengineering (Basel, Switzerland) · 2023Article
- Low-Data Drug Design with Few-Shot Generative Domain Adaptation.Bioengineering (Basel, Switzerland) · 2023Article
- iQDeep: an integrated web server for protein scoring using multiscale deep learning models.Journal of molecular biology · 2023Article
- Different Recognition of Protein Features Depending on Deep Learning Models: A Case Study of Aromatic Decarboxylase UbiD.Biology · 2023Article
- BayeStab: Predicting effects of mutations on protein stability with uncertainty quantification.Protein science : a publication of the Protein Society · 2022Article
- Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Deep graph learning of inter-protein contacts.Bioinformatics (Oxford, England) · 2022Article
- A tale of solving two computational challenges in protein science: neoantigen prediction and protein structure prediction.Briefings in bioinformatics · 2022Review
- Accurate protein function prediction via graph attention networks with predicted structure information.Briefings in bioinformatics · 2022Article
- NanoNet: Rapid and accurate end-to-end nanobody modeling by deep learning.Frontiers in immunology · 2022Article
- Article
- Fast and effective protein model refinement using deep graph neural networks.Nature computational science · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Protein model refinement is the last step applied to improve the quality of a predicted protein model. Currently the most successful refinement methods rely on extensive conformational sampling and thus, take hours or days to refine even a single protein model. Here we propose a fast and effective model refinement method that applies GNN (graph neural networks) to predict refined inter-atom distance probability distribution from an initial model and then rebuilds 3D models from the predicted distance distribution. Tested on the CASP (Critical Assessment of Structure Prediction) refinement targets, our method has comparable accuracy as two leading human groups Feig and Baker, but runs substantially faster. Our method may refine one protein model within ~11 minutes on 1 CPU while Baker needs ~30 hours on 60 CPUs and Feig needs ~16 hours on 1 GPU. Finally, our study shows that GNN outperforms ResNet (convolutional residual neural networks) for model refinement when very limited conformational sampling is allowed.
Identifiers
What OpenQuestion holds
Registered trials
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.