ArticleNucleic acids research2024
PED in 2024: improving the community deposition of structural ensembles for intrinsically disordered proteins.
Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- ModelCIF Update: Supporting Emerging Classes of Computational Macromolecular Models.Journal of molecular biology · 2026Article
- Exploring proteins and protein-ligand complexes through residue interaction networks.Nature protocols · 2026Review
- Hierarchical discrete representations for coarse-to-fine protein conformation generation.Bioinformatics (Oxford, England) · 2026Article
- The major diagnostic VSG LiTat 1.3 of the human parasite Trypanosoma brucei gambiense is a trimer in solution.PLoS pathogens · 2026Article
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- In Search of Shape in the Unshaped: Constructing Ensembles of Intrinsically Disordered Proteins.Biophysics reviews · 2026Article
- XL-MS and De Novo Protein Design Identified a Common Motif for TREM2 Binding.bioRxiv : the preprint server for biology · 2026Article
- Toward a unified framework for determining conformational ensembles of disordered proteins.Nature methods · 2026Review
- Accurate conformational ensembles of intrinsically disordered proteins using reweighting based on NMR chemical shifts.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Protein language models trained on biophysical dynamics inform mutation effects.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Constraint Decoupled Latent Diffusion for Protein Backmapping.Journal of chemical theory and computation · 2026Article
- DisProt in 2026: enhancing intrinsically disordered proteins accessibility, deposition, and annotation.Nucleic acids research · 2026Article
- Conformational dynamics of fuzzy interfaces in disordered protein complexes: mapping key residues and binding modes beyond NMR models.Frontiers in bioinformatics · 2026Article
- IDPEnsembleTools: An open-source library for analysis of conformational ensembles of disordered proteins.Protein science : a publication of the Protein Society · 2026Article
- AlphaFlex: Ensembles of the human proteome representing disordered regions.bioRxiv : the preprint server for biology · 2025Article
- Mycobacterium tuberculosis uses intrinsically disordered, fast evolving proteins to interact with conserved host factors.Genome biology · 2025Article
- Deep learning-assisted tools to understand the structural biology of the synapse.Biomedical engineering letters · 2025Review
- Determining accurate conformational ensembles of intrinsically disordered proteins at atomic resolution.Nature communications · 2025Article
- Targeting protein disorder: the next hurdle in drug discovery.Nature reviews. Drug discovery · 2025Review
- Distance-AF improves predicted protein structure models by AlphaFold2 with user-specified distance constraints.Communications biology · 2025Article
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8 authors.
Funding
Abstract
The Protein Ensemble Database (PED) (URL: https://proteinensemble.org) is the primary resource for depositing structural ensembles of intrinsically disordered proteins. This updated version of PED reflects advancements in the field, denoting a continual expansion with a total of 461 entries and 538 ensembles, including those generated without explicit experimental data through novel machine learning (ML) techniques. With this significant increment in the number of ensembles, a few yet-unprecedented new entries entered the database, including those also determined or refined by electron paramagnetic resonance or circular dichroism data. In addition, PED was enriched with several new features, including a novel deposition service, improved user interface, new database cross-referencing options and integration with the 3D-Beacons network-all representing efforts to improve the FAIRness of the database. Foreseeably, PED will keep growing in size and expanding with new types of ensembles generated by accurate and fast ML-based generative models and coarse-grained simulations. Therefore, among future efforts, priority will be given to further develop the database to be compatible with ensembles modeled at a coarse-grained level.
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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 Socratic graph.