PED in 2027: integrating experimental and predicted protein conformational ensembles at scale

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ID: 337375
2026
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Abstract
The Protein Ensemble Database (PED; https://proteinensemble.org) is the primary deposition resource for structural ensembles of intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs). PED 2027 (released in September 2026) substantially expands its scope by integrating ensembles determined or refined using experimental data with clearly identified collections of computational predictions. The release introduces a multi-method collection targeting 3319 DisProt proteins and 2452 manually curated disordered regions, together with AlphaFlex ensembles for 10 022 human proteins containing IDRs. Alternative predictions for the same sequence are retained as independent records with method-specific provenance, enabling comparison across ensemble-generation approaches. A scalable deposition workflow accommodates individual experimental studies and proteome-scale collections while preserving quality control and curator oversight. The PED controlled vocabulary standardizes experimental data acquisition, computational generation, selection, and refinement. Precomputed IDPEnsembleTools and CIDER analyses provide structural and sequence-derived descriptors directly on ensemble pages. A redesigned interface and extended REST API support discovery, visualization, download and programmatic access. Interoperability with UniProt, DisProt, MobiDB, and experimental-data repositories connects ensembles with sequences, annotations, and supporting measurements. PED 2027 therefore provides an interoperable, analysis-ready platform for studying IDP/IDR conformational behaviour.
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Authors Alexander Miguel Monzón, Hamidreza Ghafouri, Ximena Aixa Castro, Sina Khezrian, Maria Cristina Aspromonte, Kavita Singla, Shrishti Barethiya, Pau Bernadó, Massimiliano Bonomi, Jianhan Chen, T. Cordeiro, Juan Cortés, Stefano De Castro, Ryan J. Emenecker, Michael Feig, Julie Deborah Forman-Kay, Teresa L. Head-Gordon, Alex S. Holehouse, Giacomo Janson, Kresten Lindorff‐Larsen, Zi Hao Liu, Sonia Longhi, Mátyás Pajkos, Paul Robustelli, Santiago Rodriguez, Vincent Schnapka, Nathalie Sibille, Michele Vendruscolo, Silvio C. E. Tosatto
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Journal Nucleic Acids Research
Year 2026
DOI
10.1093/nar/gkag1048
URL
https://academic.oup.com/nar/advance-article-pdf/doi/10.1093/nar/gkag1048/72528700/gkag1048.pdf
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