ArticlePLoS computational biology2021
Deep template-based protein structure prediction.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 40 citations in OpenAlex.
- Master of Metals2: a graph neural network based architecture for the prediction of zinc binding sites in protein structures.Briefings in bioinformatics · 2026Article
- The Blobulator: A Toolkit for Identification and Visual Exploration of Hydrophobic Modularity in Protein Sequences.Journal of chemical information and modeling · 2026Article
- The blobulator: a toolkit for identification and visual exploration of hydrophobic modularity in protein sequences.bioRxiv : the preprint server for biology · 2025Article
- Chemosensory Receptors in Vertebrates: Structure and Computational Modeling Insights.International journal of molecular sciences · 2025Review
- Functional Characterization of an Aldol Condensation Synthase PheG for the Formation of Hispidin from Phellinus Igniarius.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Protein structure prediction via deep learning: an in-depth review.Frontiers in pharmacology · 2025Review
- Advances in Computational Intelligence-Based Methods of Structure and Function Prediction of Proteins.Biomolecules · 2024Article
- Artificial intelligence in fusion protein three-dimensional structure prediction: Review and perspective.Clinical and translational medicine · 2024Review
- Article
- Structural biology: A golden era.PLoS biology · 2023Article
- New biochemistry in the Rhodanese-phosphatase superfamily: emerging roles in diverse metabolic processes, nucleic acid modifications, and biological conflicts.NAR genomics and bioinformatics · 2023Article
- Contact-Assisted Threading in Low-Homology Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Improving protein structure prediction using templates and sequence embedding.Bioinformatics (Oxford, England) · 2023Article
- Protein Function Analysis through Machine Learning.Biomolecules · 2022Review
- AlphaFold, Artificial Intelligence (AI), and Allostery.The journal of physical chemistry. B · 2022Review
- Review
- DisCovER: distance- and orientation-based covariational threading for weakly homologous proteins.Proteins · 2022Article
- A tale of solving two computational challenges in protein science: neoantigen prediction and protein structure prediction.Briefings in bioinformatics · 2022Review
- Improved Protein Structure Prediction Using a New Multi-Scale Network and Homologous Templates.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021Article
- Current Approaches in Supersecondary Structures Investigation.International journal of molecular sciences · 2021Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 2 countries.
Funding
Abstract
motivationProtein structure prediction has been greatly improved by deep learning, but most efforts are devoted to template-free modeling. But very few deep learning methods are developed for TBM (template-based modeling), a popular technique for protein structure prediction. TBM has been studied extensively in the past, but its accuracy is not satisfactory when highly similar templates are not available.
resultsThis paper presents a new method NDThreader (New Deep-learning Threader) to address the challenges of TBM. NDThreader first employs DRNF (deep convolutional residual neural fields), which is an integration of deep ResNet (convolutional residue neural networks) and CRF (conditional random fields), to align a query protein to templates without using any distance information. Then NDThreader uses ADMM (alternating direction method of multipliers) and DRNF to further improve sequence-template alignments by making use of predicted distance potential. Finally, NDThreader builds 3D models from a sequence-template alignment by feeding it and sequence coevolution information into a deep ResNet to predict inter-atom distance distribution, which is then fed into PyRosetta for 3D model construction. Our experimental results show that NDThreader greatly outperforms existing methods such as CNFpred, HHpred, DeepThreader and CEthreader. NDThreader was blindly tested in CASP14 as a part of RaptorX server, which obtained the best average GDT score among all CASP14 servers on the 58 TBM targets.
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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.