: 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.; Tenorio Ku, L. G.; Tompa, P.; Tosatto, S. C. E.; Monzon, A. M.; Aspromonte, M. C.; Bernado, P.; Chaves-Arquero, B.; Chemes, L. B.; Clementel, D.; Cordeiro, T. N.; Elena-Real, C. A.; Feig, M.; Felli, I. C.; Ferrari, C.; Forman-Kay, J. D.; Gomes, T.; Gondelaud, F.; Gradinaru, C. C.; Ha-Duong, T.; Head-Gordon, T.; Heidarsson, P. O.; Janson, G.; Jeschke, G.; Leonardi, E.; Liu, Z. H.; Longhi, S.; Lund, X. L.; Macias, M. J.; Martin-Malpartida, P.; Mercadante, D.; Mouhand, A.; Nagy, G.; Nugnes, M. V.; Perez-Canadillas, J. M.; Pesce, G.; Pierattelli, R.; Piovesan, D.; Quaglia, F.; Ricard-Blum, S.; Robustelli, P.; Sagar, A.; Salladini, E.; Senicourt, L.; Sibille, N.; Teixeira, J. M. C.; Tsangaris, T. E.; Varadi, M.. - In: NUCLEIC ACIDS RESEARCH. - ISSN 1362-4962. - 52:D1(2024), pp. 536-544. [10.1093/nar/gkad947]
PED in 2024: improving the community deposition of structural ensembles for intrinsically disordered proteins
Mercadante D.;
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
