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

Hamidreza Ghafouri , Tamas Lazar , Alessio del Conte , Luiggi G Tenorio Ku , Maria Aspromonte , Pau Bernadó , Belén Chaves-Arquero , Lucia Beatriz Chemes , Damiano Clementel , Tiago Cordeiro , Carlos Elena-Real , Michael Feig , Isabella Felli , Carlo Ferrari , Julie Forman-Kay , Tiago Gomes , Frank Gondelaud , Claudiu Gradinaru , Tâp Ha-Duong , Teresa Head-Gordon , Pétur Heidarsson , Giacomo Janson , Gunnar Jeschke , Emanuela Leonardi , Zi Hao Liu , Sonia Longhi , Xamuel Lund , Maria Macias , Pau Martin-Malpartida , Davide Mercadante , Assia Mouhand , Gabor Nagy , María Victoria Nugnes , José Manuel Pérez-Cañadillas , Giulia Pesce , Roberta Pierattelli , Damiano Piovesan , Federica Quaglia , Sylvie Ricard-Blum , Paul Robustelli , Amin Sagar , Edoardo Salladini , Lucile Sénicourt , Nathalie Sibille (1) , João Teixeira , Thomas Tsangaris , Mihaly Varadi , Pet Er Tompa , Silvio C E Tosatto , Alexander Miguel Monzon

1 CBS - Centre de Biologie Structurale [Montpellier]

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.

Domaines: Sciences du Vivant [q-bio]

Hamidreza Ghafouri, Tamas Lazar, Alessio del Conte, Luiggi G Tenorio Ku, Maria Aspromonte, et al.. PED in 2024: improving the community deposition of structural ensembles for intrinsically disordered proteins. Nucleic Acids Research, 2023, ⟨10.1093/nar/gkad947⟩. ⟨hal-04266884⟩