The Protein Ensemble Database (PED) (URL: https://proteinensemble.org) is the primary resource for depositing structural ensembles of intrinsically disordered proteins. This updated version of PED reflects advancements in the field, denoting a continual expansion with a total of 461 entries and 538 ensembles, including those generated without explicit experimental data through novel machine learning (ML) techniques. With this significant increment in the number of ensembles, a few yet-unprecedented new entries entered the database, including those also determined or refined by electron paramagnetic resonance or circular dichroism data. In addition, PED was enriched with several new features, including a novel deposition service, improved user interface, new database cross-referencing options and integration with the 3D-Beacons network-all representing efforts to improve the FAIRness of the database. Foreseeably, PED will keep growing in size and expanding with new types of ensembles generated by accurate and fast ML-based generative models and coarse-grained simulations. Therefore, among future efforts, priority will be given to further develop the database to be compatible with ensembles modeled at a coarse-grained level.
PED in 2024: improving the community deposition of structural ensembles for intrinsically disordered proteins / Ghafouri H, Lazar T, Del Conte A, Ku LGT, Aspromonte MC, Bernadó P, Chaves-Arquero B, Chemes LB, Clementel D, Cordeiro TN, Elena-Real CA, Feig M, Felli IC, Ferrari C, Forman-Kay JD, Gomes T, Gondelaud F, Gradinaru CC, Ha-Duong T, Head-Gordon T, Heidarsson PO, Janson G, Jeschke G, Leonardi E, Liu ZH, Longhi S, Lund XL, Macias MJ, Martin-Malpartida P, Mercadante D, Mouhand A, Nagy G, Nugnes MV, Pérez-Cañadillas JM, Pesce G, Pierattelli R, Piovesan D, Quaglia F, Ricard-Blum S, Robustelli P, Sagar A, Salladini E, Sénicourt L, Sibille N, Teixeira JMC, Tsangaris TE, Varadi M, Tompa P, Tosatto SCE, Monzon AM. - In: NUCLEIC ACIDS RESEARCH. - ISSN 0305-1048. - ELETTRONICO. - 52:(2024), pp. 536-544. [10.1093/nar/gkad947]
PED in 2024: improving the community deposition of structural ensembles for intrinsically disordered proteins
Felli IC;Pierattelli R;
2024
Abstract
The Protein Ensemble Database (PED) (URL: https://proteinensemble.org) is the primary resource for depositing structural ensembles of intrinsically disordered proteins. This updated version of PED reflects advancements in the field, denoting a continual expansion with a total of 461 entries and 538 ensembles, including those generated without explicit experimental data through novel machine learning (ML) techniques. With this significant increment in the number of ensembles, a few yet-unprecedented new entries entered the database, including those also determined or refined by electron paramagnetic resonance or circular dichroism data. In addition, PED was enriched with several new features, including a novel deposition service, improved user interface, new database cross-referencing options and integration with the 3D-Beacons network-all representing efforts to improve the FAIRness of the database. Foreseeably, PED will keep growing in size and expanding with new types of ensembles generated by accurate and fast ML-based generative models and coarse-grained simulations. Therefore, among future efforts, priority will be given to further develop the database to be compatible with ensembles modeled at a coarse-grained level.
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