PDBe-KB - enhancing impact of structural knowledgebase in basic and translational research with focus on Pathogenic Mutations
- Funded by Wellcome Trust
- Total publications:5 publications
Grant number: 223739/Z/21/Z
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Key facts
Disease
COVID-19Start & end year
20222027Known Financial Commitments (USD)
$1,447,684.3Funder
Wellcome TrustPrincipal Investigator
Dr. Sameer VelankarResearch Location
United KingdomLead Research Institution
European Bioinformatics InstituteResearch 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.
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