ArticleBioinformatics (Oxford, England)2018
Protein threading using residue co-variation and deep learning.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Protein Structure Prediction Methods.Advances in experimental medicine and biology · 2026Review
- From CASP13 to the Nobel Prize: DeepMind's AlphaFold Journey in Revolutionizing Protein Structure Prediction and Beyond.Current protein & peptide science · 2026Review
- Drug Discovery in the Age of Artificial Intelligence: Transformative Target-Based Approaches.International journal of molecular sciences · 2024Review
- Artificial intelligence in fusion protein three-dimensional structure prediction: Review and perspective.Clinical and translational medicine · 2024Review
- Recent Progress of Protein Tertiary Structure Prediction.Molecules (Basel, Switzerland) · 2024Review
- Protein Structure Prediction: Challenges, Advances, and the Shift of Research Paradigms.Genomics, proteomics & bioinformatics · 2023Review
- Protein structure and folding pathway prediction based on remote homologs recognition using PAthreader.Communications biology · 2023Article
- Contact-Assisted Threading in Low-Homology Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2023Article
- DisCovER: distance- and orientation-based covariational threading for weakly homologous proteins.Proteins · 2022Article
- ProALIGN: Directly Learning Alignments for Protein Structure Prediction via Exploiting Context-Specific Alignment Motifs.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- Enhancing protein inter-residue real distance prediction by scrutinising deep learning models.Scientific reports · 2022Article
- A tale of solving two computational challenges in protein science: neoantigen prediction and protein structure prediction.Briefings in bioinformatics · 2022Review
- Transposable Prophages in Leptospira: An Ancient, Now Diverse, Group Predominant in Causative Agents of Weil's Disease.International journal of molecular sciences · 2021Article
- FALCON2: a web server for high-quality prediction of protein tertiary structures.BMC bioinformatics · 2021Article
- The Protective A673T Mutation of Amyloid Precursor Protein (APP) in Alzheimer's Disease.Molecular neurobiology · 2021Review
- Improved protein structure prediction by deep learning irrespective of co-evolution information.Nature machine intelligence · 2021Article
- Toward the solution of the protein structure prediction problem.The Journal of biological chemistry · 2021Review
- CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction.Nature communications · 2021Article
- Deep template-based protein structure prediction.PLoS computational biology · 2021Article
- Evaluation of residue-residue contact prediction methods: From retrospective to prospective.PLoS computational biology · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
Motivation: Template-based modeling, including homology modeling and protein threading, is a popular method for protein 3D structure prediction. However, alignment generation and template selection for protein sequences without close templates remain very challenging. Results: We present a new method called DeepThreader to improve protein threading, including both alignment generation and template selection, by making use of deep learning (DL) and residue co-variation information. Our method first employs DL to predict inter-residue distance distribution from residue co-variation and sequential information (e.g. sequence profile and predicted secondary structure), and then builds sequence-template alignment by integrating predicted distance information and sequential features through an ADMM algorithm. Experimental results suggest that predicted inter-residue distance is helpful to both protein alignment and template selection especially for protein sequences without very close templates, and that our method outperforms currently popular homology modeling method HHpred and threading method CNFpred by a large margin and greatly outperforms the latest contact-assisted protein threading method EigenTHREADER. Availability and implementation: http://raptorx.uchicago.edu/. 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.