PED in 2024: improving the community deposition of structural ensembles for intrinsically disordered proteins

GHAFOURI H; LAZAR, TAMAS; DEL CONTE ALESSIO; TENORIO L; CHEMES L.B.; TOMPA, PETER; TOSATTO, SILVIO C E; MONZON, ALEXANDER MIGUEL

Nucleic Acids Research

Oxford University Press

Año: 2024 vol. 52 p. 536 - 544

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.