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MEROPS: the peptidase database

N D Rawlings1, A J Barrett

  • 1MRC Molecular Enzymology Laboratory, The Babraham Institute, Babraham, Cambridgeshire CB2 4AT, UK. neil.rawlings@bbsrc.ac.uk

Nucleic Acids Research
|December 10, 1998
PubMed
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The MEROPS database offers a comprehensive classification of peptidases, essential enzymes for medicine and biotechnology. It organizes these proteins into families and clans, facilitating access to related data for researchers.

Area of Science:

  • Biochemistry and Bioinformatics
  • Enzymology
  • Proteomics

Background:

  • Peptidases, or proteolytic enzymes, represent a significant protein group (approx. 2% of gene products) crucial in medicine and biotechnology.
  • A structured classification system for peptidases is essential for organizing and accessing vast amounts of related biological data.

Purpose of the Study:

  • To introduce and describe the MEROPS database, a resource for the catalogue and structure-based classification of peptidases.
  • To highlight MEROPS's utility in providing access to peptidase information, classification, and links to other biological databases.

Main Methods:

  • Development of a database (MEROPS) for cataloging and classifying peptidases.
  • Classification based on sequence similarities within the 'peptidase unit' to define families and clans.

Related Experiment Videos

  • Integration of hypertext links to external databases for genetics, sequences, and structures.

    • Main Results:

    • MEROPS provides indexed access to individual peptidase information (PepCards) and organism-specific retrievals.
    • Peptidases are classified into 153 families and 22 clans based on evolutionary and structural relationships.
    • Release 3.03 includes 758 peptidases, with detailed information and cross-links to other biological resources.

    Conclusions:

    • The MEROPS database serves as a valuable organizational framework for peptidase information.
    • It provides a model for developing classification systems for functional protein groups, integrating diverse biological data.