Decoding conformational heterogeneity across disordered proteomes

Intrinsically disordered proteins (IDPs) comprise nearly one-third of the human proteome and play key roles in regulation, signaling, and disease, yet their dynamic nature has resisted accurate structural prediction. Here we introduce AI-IDP, a deep-learning framework that transforms sequence information into experiment-consistent conformational ensembles of disordered proteins. By combining deep-learning-driven fragment prediction with flexible physical assembly, AI-IDP reproduces experimental observables across local, medium-range, and global scales, including transient secondary structure, mutation sensitivity, and overall chain dimensions. Applied to more than 3,000 disordered regions across human and non-human proteomes, AI-IDP reveals that transient -helices and polyproline-II conformations are pervasive and evolutionarily tuned features of disorder. By uncovering how sequence encodes conformational heterogeneity, AI-IDP provides a practical framework for understanding the structural and functional logic of disordered proteomes and enables rationally targeting the dynamic states that underlie health and disease.