ArticleNature biotechnology2026
Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER.
Article in Nature biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 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
52 citing papers in PubMed.
- MultilevelCurrent research in structural biology · 2026Article
- The MscS channel frombioRxiv : the preprint server for biology · 2026Article
- Cross-protective human antibodies against the Mpox virus discovered through structure-guided screening.Cell discovery · 2026Article
- In Silico Discovery and in vitro Evaluation of the Antifungal and Cytotoxicity Properties of Zeaxanthin and β-Carotene.Current microbiology · 2026Article
- Immunization with the Tc85-Derived Peptide Displayed on Virus-like Particles Confers Protection againstACS omega · 2026Article
- Anticancer effects of alpha-helical peptide epinecidin-1 and its variants in combination with doxorubicin.Medical oncology (Northwood, London, England) · 2026Article
- Pipettes and Pipelines: The Weapons of Omics Sciences for a New Age of Clinical Studies.Current issues in molecular biology · 2026Review
- Benchmarking protein sequence and structure search methods for remote homology detection.Genome biology · 2026Article
- Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.BioData mining · 2026Review
- Whole-proteome structure/function prediction in UropathogenicbioRxiv : the preprint server for biology · 2026Article
- Innovative multi-epitope vaccine engineering for dual protection against Salmonella and Shigella leveraging FliC flagellin protein adjuvant properties: an immunoinformatics-based approach.Journal of translational medicine · 2026Article
- Tools For Building Artificial Biological Nanostructures.ACS nano · 2026Review
- The DdD protein confers intracellular and extracellular immunity to the leaderless bacteriocin enterocin DD14.PNAS nexus · 2026Article
- In situ structure of the human gap junction.Science advances · 2026Article
- Artificial Intelligence Powers Protein Functional Annotation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Advances and opportunities for computational interrogation of plant proteins.The Plant journal : for cell and molecular biology · 2026Review
- Fluorescence Detection of Pyrene-StainedInternational journal of molecular sciences · 2026Article
- Article
- Associations of MC1R Substitutions With Body Color Variation in a Desert Lizard.Ecology and evolution · 2026Article
- Engineered cationic Interleukin-1 receptor antagonist outperforms anakinra at joint retention and preventing IL-1-induced cartilage inflammation.Osteoarthritis and cartilage · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
The dominant success of deep learning techniques on protein structure prediction has challenged the necessity and usefulness of traditional force field-based folding simulations. We proposed a hybrid approach, deep-learning-based iterative threading assembly refinement (D-I-TASSER), which constructs atomic-level protein structural models by integrating multisource deep learning potentials with iterative threading fragment assembly simulations. D-I-TASSER introduces a domain splitting and assembly protocol for the automated modeling of large multidomain protein structures. Benchmark tests and the most recent critical assessment of protein structure prediction, 15 experiments demonstrate that D-I-TASSER outperforms AlphaFold2 and AlphaFold3 on both single-domain and multidomain proteins. Large-scale folding experiments further show that D-I-TASSER could fold 81% of protein domains and 73% of full-chain sequences in the human proteome with results highly complementary to recently released models by AlphaFold2. These results highlight a new avenue to integrate deep learning with classical physics-based folding simulations for high-accuracy protein structure and function predictions that are usable in genome-wide applications.
Indexed as
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.