ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
Accurate Generation of Conformational Ensembles for Intrinsically Disordered Proteins with IDPFold.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. 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.
- The Advantages of AI for Computational Protein Studies and Looking Ahead at the Next Challenges: Single Structures Are Not Enough.Journal of molecular biology · 2026Review
- AF-CALVADOS: AlphaFold-guided simulations of multi-domain proteins at the proteome level.Protein science : a publication of the Protein Society · 2026Article
- HyRes: Accurate Physics-Based Simulation of Dynamic Protein Structures and Interactions in Complex Environments at Scale.bioRxiv : the preprint server for biology · 2026Article
- Advancements in single-molecule fluorescence spectroscopy for probing conformations, dynamics, and interactions in disordered protein regions.Current opinion in structural biology · 2026Review
- Transient tertiary structure in intrinsically disordered proteins revealed by multithermal enhanced sampling.Nature communications · 2026Article
- Scaffold-Lab: Critical evaluation and ranking of protein backbone generation methods in a unified framework.PLoS computational biology · 2026Article
- Article
- Experimental Data Driven AI Framework for Flexible Protein Conformational Reconstruction.bioRxiv : the preprint server for biology · 2026Article
- 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
- Resolving Conformational Heterogeneity in Intrinsically Disordered Proteins via Experimentally Guided Multi-Replica Simulations.bioRxiv : the preprint server for biology · 2026Article
- Disulfide engineering reveals unexpected pro- and anti-aggregation conformers of human α-synuclein.bioRxiv : the preprint server for biology · 2026Article
- Accurate Generation of Conformational Ensembles for Intrinsically Disordered Proteins with IDPFold.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Sequence-Dependent Conformational Landscapes of Intrinsically Disordered Proteins Reveal Asymmetric Chain Compaction.Journal of chemical theory and computation · 2025Article
- Distance-AF improves predicted protein structure models by AlphaFold2 with user-specified distance constraints.Communications biology · 2025Article
- Biological insights from integrative modeling of intrinsically disordered protein systems.Current opinion in structural biology · 2025Review
- Atomistic molecular dynamics simulations of intrinsically disordered proteins.Current opinion in structural biology · 2025Review
- Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning.Journal of chemical information and modeling · 2025Article
- Modeling biomolecular condensates across scales: Atomistic, coarse-grained, and data-driven approaches.Advances in physics: X · 2025Article
- Frontiers in integrative structural modeling of macromolecular assemblies.QRB discovery · 2025Review
- Use of AI-methods over MD simulations in the sampling of conformational ensembles in IDPs.Frontiers in molecular biosciences · 2025Review
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Authors and funding
11 authors.
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Abstract
Intrinsically disordered proteins (IDPs) play pivotal roles in various biological functions whose dynamic structures are closely associated with many human diseases, including cancer, diabetes, and Alzheimer disease. Structural investigations of IDPs typically involve a combination of molecular dynamics (MD) simulations and experimental data to mitigate intrinsic biases in simulation methods. However, the high computational cost of these simulations and the limited availability of experimental data significantly restrict their applicability. Despite the recent advancements in structure prediction for structured proteins, understanding the conformational properties of IDPs remains challenging, partly due to the poor conservation of disordered protein sequences and the scarcity of experimental characterization. Here, IDPFold is introduced as a method capable of generating conformational ensembles for IDPs directly from their sequences using fine-tuned diffusion models. IDPFold eliminates the reliance on multiple sequence alignments (MSA) or experimental data, offering a more detailed characterization of structural features in IDP ensembles. Evaluated across 27 IDP systems, IDPFold achieves Rg error of -0.06 and an RMSD of 0.65 ppm on Cα secondary chemical shifts with experimental values, significantly better than all existing generative deep learning approaches. IDPFold can be used to elucidate the sequence-disorder-function paradigm of IDPs.
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