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PED in 2024
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Resumo(s)
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.
Descrição
Funding Information: European Union’s Horizon 2020 research and innovation programme [778247 MSCA-RISE ‘IDPfun’ and 823886 MSCA-RISE ‘REFRACT’]; ML4NGP CA21160 project supported by COST (European Cooperation in Science and Technology) under Horizon Europe; H.G. and M.C.A. are funded by the European Union—NextGenerationEU through ‘Italiadomani—PNRR’ projects ‘National Centre for HPC, Big Data and Quantum Computing’ [codice identificativo MUR CN00000013, C93C22002800006]; ‘National Center for Gene Therapy and Drugs based on RNA Technology’ [codice fiscale 92315700283, codice identificativo CN00000041]; ELIXIR, the research infrastructure for life-science data, and by the European Union—NextGenerationEU through ‘Italiadomani—PNRR project IR000010 ‘ELIXIR × NextGenerationIT: Consolidamento dell’Infrastruttura Italiana per i Dati Omici e la Bioinformatica—ElixirxNextGenIT’ and project ECS00000017 ‘THE—Tuscany Health Ecosystem’. T.L. is holder of a postdoctoral innovation mandate [HBC.2022.0194] by the Flanders Innovation & Entrepreneurship Agency (VLAIO). Funding for open access charge: European Union’s Horizon 2020 research and innovation programme [778247 MSCA-RISE “IDPfun” and 823886 MSCA-RISE “REFRACT”]. Funding Information: European Union's Horizon 2020 research and innovation programme [778247 MSCA-RISE ‘IDPfun’ and 823886 MSCA-RISE ‘REFRACT’]; ML4NGP CA21160 project supported by COST (European Cooperation in Science and Technology) under Horizon Europe; H.G. and M.C.A. are funded by the European Union—NextGenerationEU through ‘Italiadomani—PNRR’ projects ‘National Centre for HPC, Big Data and Quantum Computing’ [codice identificativo MUR CN00000013, C93C22002800006]; ‘National Center for Gene Therapy and Drugs based on RNA Technology’ [codice fiscale 92315700283, codice identificativo CN00000041]; ELIXIR, the research infrastructure for life-science data, and by the European Union—NextGenerationEU through ‘Italiadomani—PNRR project IR000010 ‘ELIXIR × NextGenerationIT: Consolidamento dell’Infrastruttura Italiana per i Dati Omici e la Bioinformatica—ElixirxNextGenIT’ and project ECS00000017 ‘THE—Tuscany Health Ecosystem’. T.L. is holder of a postdoctoral innovation mandate [HBC.2022.0194] by the Flanders Innovation & Entrepreneurship Agency (VLAIO). Funding for open access charge: European Union’s Horizon 2020 research and innovation programme [778247 MSCA-RISE “IDPfun” and 823886 MSCA-RISE “REFRACT”]. Publisher Copyright: © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.
Palavras-chave
Genetics