ArticleNature communications2019
Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers.
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Who cites it
82 citing papers in PubMed.
- MultilevelCurrent research in structural biology · 2026Article
- Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.BioData mining · 2026Review
- Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER.Nature biotechnology · 2026Article
- Protein Structure Prediction Methods.Advances in experimental medicine and biology · 2026Review
- Evaluating the significance of embedding-based protein sequence alignment with clustering and double dynamic programming for remote homology.Scientific reports · 2025Article
- Exploring an Aptamer-Based Approach to Assess Canine Parvovirus Integrity After Disinfection Treatment.Viruses · 2025Article
- Modeling protein conformational ensembles by guiding AlphaFold2 with Double Electron Electron Resonance (DEER) distance distributions.Nature communications · 2025Article
- Review
- Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design.Molecular biotechnology · 2025Review
- Deep learning for the PSIPRED Protein Analysis Workbench.Nucleic acids research · 2024Article
- The State-of-the-Art Overview to Application of Deep Learning in Accurate Protein Design and Structure Prediction.Topics in current chemistry (Cham) · 2024Review
- Predicting therapeutic and side effects from drug binding affinities to human proteome structures.iScience · 2024Article
- Protein remote homology detection and structural alignment using deep learning.Nature biotechnology · 2024Article
- Hippo and PI5P4K signaling intersect to control the transcriptional activation of YAP.Science signaling · 2024Article
- Application of Computational Techniques in Antibody Fc-Fused Molecule Design for Therapeutics.Molecular biotechnology · 2024Review
- Recent Progress of Protein Tertiary Structure Prediction.Molecules (Basel, Switzerland) · 2024Review
- Agrobacteria deploy two classes of His-Me finger superfamily nuclease effectors exerting different antibacterial capacities against specific bacterial competitors.Frontiers in microbiology · 2024Article
- SeqPredNN: a neural network that generates protein sequences that fold into specified tertiary structures.BMC bioinformatics · 2023Article
- Intrinsic disorder and conformational coexistence in auxin coreceptors.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- NREP contributes to development of NAFLD by regulating one-carbon metabolism in primary human hepatocytes.Cell chemical biology · 2023Article
22 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The inapplicability of amino acid covariation methods to small protein families has limited their use for structural annotation of whole genomes. Recently, deep learning has shown promise in allowing accurate residue-residue contact prediction even for shallow sequence alignments. Here we introduce DMPfold, which uses deep learning to predict inter-atomic distance bounds, the main chain hydrogen bond network, and torsion angles, which it uses to build models in an iterative fashion. DMPfold produces more accurate models than two popular methods for a test set of CASP12 domains, and works just as well for transmembrane proteins. Applied to all Pfam domains without known structures, confident models for 25% of these so-called dark families were produced in under a week on a small 200 core cluster. DMPfold provides models for 16% of human proteome UniProt entries without structures, generates accurate models with fewer than 100 sequences in some cases, and is freely available.
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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.