PED in 2024: improving the community deposition of structural ensembles for intrinsically disordered proteins
Author:
Ghafouri Hamidreza1ORCID, Lazar Tamas23ORCID, Del Conte Alessio1ORCID, Tenorio Ku Luiggi G1, Aspromonte Maria C, Bernadó Pau, Chaves-Arquero Belén, Chemes Lucia Beatriz, Clementel Damiano, Cordeiro Tiago N, Elena-Real Carlos A, Feig Michael, Felli Isabella C, Ferrari Carlo, Forman-Kay Julie D, Gomes Tiago, Gondelaud Frank, Gradinaru Claudiu C, Ha-Duong Tâp, Head-Gordon Teresa, Heidarsson Pétur O, Janson Giacomo, Jeschke Gunnar, Leonardi Emanuela, Liu Zi Hao, Longhi Sonia, Lund Xamuel L, Macias Maria J, Martin-Malpartida Pau, Mercadante Davide, Mouhand Assia, Nagy Gabor, Nugnes María Victoria, Pérez-Cañadillas José Manuel, Pesce Giulia, Pierattelli Roberta, Piovesan Damiano, Quaglia Federica, Ricard-Blum Sylvie, Robustelli Paul, Sagar Amin, Salladini Edoardo, Sénicourt Lucile, Sibille Nathalie, Teixeira João M C, Tsangaris Thomas E, Varadi Mihaly, Tompa Peter234ORCID, Tosatto Silvio C E1ORCID, Monzon Alexander Miguel5ORCID,
Affiliation:
1. Department of Biomedical Sciences, University of Padova , Padova , Italy 2. VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie (VIB) , Brussels , Belgium 3. Structural Biology Brussels, Department of Bioengineering, Vrije Universiteit Brussel (VUB) , Brussels , Belgium 4. Institute of Enzymology, Research Centre for Natural Sciences (RCNS) , Budapest , Hungary 5. Department of Information Engineering, University of Padova , Padova , Italy
Abstract
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.
Funder
European Union's Horizon 2020 European Cooperation in Science and Technology Horizon Europe European Union Italiadomani—PNRR National Centre for HPC, Big Data and Quantum Computing National Center for Gene Therapy and Drugs based on RNA Technology ELIXIR Tuscany Health Ecosystem Flanders Innovation & Entrepreneurship Agency
Publisher
Oxford University Press (OUP)
Cited by
3 articles.
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