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PRRDB 2.0: a comprehensive database of pattern-recognition receptors and their ligands
Author(s) -
Dilraj Kaur,
Sumeet Patiyal,
Neelam Sharma,
Salman Sadullah Usmani,
Gajendra P. S. Raghava
Publication year - 2019
Publication title -
database
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.406
H-Index - 62
ISSN - 1758-0463
DOI - 10.1093/database/baz076
Subject(s) - pubchem , computer science , similarity (geometry) , database , pattern recognition receptor , function (biology) , information retrieval , similitude , receptor , artificial intelligence , computational biology , innate immune system , biology , biochemistry , evolutionary biology , image (mathematics)
PRRDB 2.0 is an updated version of PRRDB that maintains comprehensive information about pattern-recognition receptors (PRRs) and their ligands. The current version of the database has ~2700 entries, which are nearly five times of the previous version. It contains extensive information about 467 unique PRRs and 827 pathogens-associated molecular patterns (PAMPs), manually extracted from ~600 research articles. It possesses information about PRRs and PAMPs that has been extracted manually from research articles and public databases. Each entry provides comprehensive details about PRRs and PAMPs that includes their name, sequence, origin, source, type, etc. We have provided internal and external links to various databases/resources (like Swiss-Prot, PubChem) to obtain further information about PRRs and their ligands. This database also provides links to ~4500 experimentally determined structures in the protein data bank of various PRRs and their complexes. In addition, 110 PRRs with unknown structures have also been predicted, which are important in order to understand the structure-function relationship between receptors and their ligands. Numerous web-based tools have been integrated into PRRDB 2.0 to facilitate users to perform different tasks like (i) extensive searching of the database; (ii) browsing or categorization of data based on receptors, ligands, source, etc. and (iii) similarity search using BLAST and Smith-Waterman algorithm.

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