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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\u2010tuned 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 \u22120.06 and an RMSD of 0.65 ppm on C\u03b1 secondary chemical shifts with experimental values, significantly better than all existing generative deep learning approaches. IDPFold can be used to elucidate the sequence\u2010disorder\u2010function paradigm of IDPs.\n                  <\/jats:p>","DOI":"10.1002\/advs.202511636","type":"journal-article","created":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T02:27:30Z","timestamp":1760408850000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Accurate Generation of Conformational Ensembles for Intrinsically Disordered Proteins with IDPFold"],"prefix":"10.1002","volume":"12","author":[{"given":"Junjie","family":"Zhu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Microbial Metabolism Department of Bioinformatics and Biostatistics SJTU\u2010Yale Joint Center for Biostatistics National Experimental Teaching Center for Life Sciences and Biotechnology School of Life Sciences and Biotechnology Shanghai Jiao Tong University  Shanghai 200240 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