PDBe-KB - enhancing impact of structural knowledgebase in basic and translational research with focus on Pathogenic Mutations

Grant number: 223739/Z/21/Z

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Key facts

  • Disease

    COVID-19
  • Start & end year

    2022
    2027
  • Known Financial Commitments (USD)

    $1,447,684.3
  • Funder

    Wellcome Trust
  • Principal Investigator

    Dr. Sameer Velankar
  • Research Location

    United Kingdom
  • Lead Research Institution

    European Bioinformatics Institute
  • Research Priority Alignment

    N/A
  • Research Category

    Pathogen: natural history, transmission and diagnostics

  • Research Subcategory

    Pathogen genomics, mutations and adaptations

  • Special Interest Tags

    Data Management and Data Sharing

  • Study Type

    Non-Clinical

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Not Applicable

  • Vulnerable Population

    Not applicable

  • Occupations of Interest

    Not applicable

Abstract

Established in 2018, the PDBe Knowledge Base (PDBe-KB, pdbe-kb.org) is a community-driven resource providing FAIR access to experimental and predicted 3D structure models and enhanced structural and functional annotations e.g. predicted functional sites. PDBe-KB supports fundamental biology, biomedicine, biotechnology and bioenergy by enabling atomic-level understanding of macromolecular function through its novel, consolidated presentation of all the available structural data and enriched annotations by means of "aggregated views". The PDBe-KB consortium has established common data standards for structural and functional annotations to improve data interoperability extending the impact of structural data e.g. to rationalise the impacts of disease-associated mutations, and thereby assist diagnostic and therapeutic strategies. In this project we will: Grow the PDBe-KB consortium to broaden the structural and functional annotations with specific emphasis on integrating residue mutation data in human and pathogen genomes. Establish a new and comprehensive 3D-fold structure domain library (3D-SCAfold) through integration of CATH and SCOP domain data. Ten-fold increase in the structural coverage of the sequence space using 3D-SCAfold for enhanced structure-based annotations with a specific focus on disease genotype data from human disease and pathogen proteins (e.g. SARS-CoV-2, Mycobacterium tuberculosis). Develop novel visualisation tools, and a collection of aggregated views.

Publicationslinked via Europe PMC

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R2DT: a comprehensive platform for visualizing RNA secondary structure.

Harnessing the 3D-Beacons Network: A Comprehensive Guide to Accessing and Displaying Protein Structure Data.

Annotating Macromolecular Complexes in the Protein Data Bank: Improving the FAIRness of Structure Data.

The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors.

PDBe and PDBe-KB: Providing high-quality, up-to-date and integrated resources of macromolecular structures to support basic and applied research and education.