PED in 2027: integrating experimental and predicted protein conformational ensembles at scale
19 affiliations
- 1Department of Biomedical Sciences, University of Padova , 35131 Padova ,
- 2Structural Biology and NMR Laboratory, Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen , DK-2200Copenhagen ,
- 3Department of Chemistry, University of Massachusetts , Amherst, MA 01003 ,
- 4Centre de Biologie Structurale (CBS), University of Montpellier, INSERM, CNRS , 34090
- 5Institut Pasteur, Université Paris Cité, CNRS UMR3528, Computational Structural Biology Unit , F-75015 Paris ,
- 6Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa , Avenida da República, 2780-157 Oeiras ,
- 7Université de Toulouse, CNRS, LAAS , 31400Toulouse ,
- 8Pitzer Theory Center and Department of Chemistry, University of California , Berkeley, CA 94720 ,
- 9Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine , St. Louis, MO 63110 ,
- 10Center for Biomolecular Condensates, Washington University in St. Louis , St. Louis, MO ,
- 11Department of Biochemistry and Molecular Biology, Michigan State University , East Lansing, MI48824 ,
- 12Molecular Medicine Program, The Hospital for Sick Children , M5G 1X8Toronto, Ontario ,
- 13Department of Biochemistry, University of Toronto , M5S 1A8Toronto, Ontario ,
- 14Pitzer Theory Center and Departments of Chemistry, Bioengineering, and Chemical and Biomolecular Engineering, University of California , Berkeley, CA 94720 ,
- 15Laboratoire Architecture et Fonction des Macromolécules Biologiques (AFMB), UMR 7257, Aix-Marseille Université and CNRS , 13288Marseille ,
- 16CNRS, LAAS , 31400Toulouse ,
- 17Department of Chemistry, Dartmouth College , Hanover, NH 03755 ,
- 18University of Montpellier, CNRS , 34090Montpellier ,
- 19Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge , Cambridge CB2 1EW ,
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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BibTeX
@article{monzon20262027,
title = {{PED in 2027: integrating experimental and predicted protein conformational ensembles at scale}},
author = {Alexander Miguel Monzon and Hamidreza Ghafouri and Ximena Aixa Castro and Sina Khezrian and Maria Cristina Aspromonte and Kavita Singla and Shrishti Barethiya and Pau Bernadó and Massimiliano Bonomi and Jianhan Chen and Tiago Cordeiro and Juan Cortés and Stefano De Castro and Ryan Emenecker and Michael Feig and Julie D Forman-Kay and Teresa Head-Gordon and Alex Holehouse and Giacomo Janson and Kresten Lindorff-Larsen and Zi Hao Liu and Sonia Longhi and Matyas Pajkos and Paul Robustelli and Santiago Rodriguez and Vincent Schnapka and Nathalie Sibille and Michele Vendruscolo and Silvio C E Tosatto},
journal = {Nucleic Acids Research},
year = {2026},
month = oct,
doi = {10.1093/nar/gkag1048},
url = {https://hal.science/hal-05843399v1},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - PED in 2027: integrating experimental and predicted protein conformational ensembles at scale
AU - Alexander Miguel Monzon
AU - Hamidreza Ghafouri
AU - Ximena Aixa Castro
AU - Sina Khezrian
AU - Maria Cristina Aspromonte
AU - Kavita Singla
AU - Shrishti Barethiya
AU - Pau Bernadó
AU - Massimiliano Bonomi
AU - Jianhan Chen
AU - Tiago Cordeiro
AU - Juan Cortés
AU - Stefano De Castro
AU - Ryan Emenecker
AU - Michael Feig
AU - Julie D Forman-Kay
AU - Teresa Head-Gordon
AU - Alex Holehouse
AU - Giacomo Janson
AU - Kresten Lindorff-Larsen
AU - Zi Hao Liu
AU - Sonia Longhi
AU - Matyas Pajkos
AU - Paul Robustelli
AU - Santiago Rodriguez
AU - Vincent Schnapka
AU - Nathalie Sibille
AU - Michele Vendruscolo
AU - Silvio C E Tosatto
PY - 2026
DA - 2026/10/27
JO - Nucleic Acids Research
DO - 10.1093/nar/gkag1048
UR - https://hal.science/hal-05843399v1
AB - 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.
SN - 0305-1048
SN - 1362-4962
ER -