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Related Experiment Videos

HMM-based databases in InterPro.

Alex Bateman1, Daniel H Haft

  • 1Pfam Group, The Wellcome Trust Sanger Institute, Hinxton, UK.

Briefings in Bioinformatics
|September 17, 2002
PubMed
Summary
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Protein family databases like Pfam, TIGRFAMs, and SMART are crucial for understanding protein evolution. They utilize hidden Markov models (HMMs) for accurate protein family member identification.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Evolution

Background:

  • Protein family databases are essential resources for annotating protein functions and tracing evolutionary relationships.
  • Hidden Markov Models (HMMs) have emerged as a primary technology for identifying protein family members.
  • Accurate protein annotation is critical for advancing biological research and drug discovery.

Purpose of the Study:

  • To review prominent protein family databases: Pfam, TIGRFAMs, and SMART.
  • To highlight the role of profile-Hidden Markov Models (HMMs) in these databases.
  • To provide an overview of HMMER package's contribution to protein family analysis.

Main Methods:

  • Review of existing literature and database documentation.

Related Experiment Videos

  • Comparative analysis of Pfam, TIGRFAMs, and SMART database methodologies.
  • Focus on the application of profile-HMMs from the HMMER package.

    • Main Results:

    • Pfam, TIGRFAMs, and SMART are key databases utilizing profile-HMMs for protein family classification.
    • The HMMER package provides the foundational profile-HMMs for these databases.
    • These databases offer valuable tools for researchers in protein annotation and evolutionary studies.

    Conclusions:

    • Profile-HMM-based databases are indispensable tools in modern bioinformatics.
    • The reviewed databases (Pfam, TIGRFAMs, SMART) significantly aid in protein annotation and evolutionary analysis.
    • Continued development and utilization of HMMs will enhance our understanding of proteomes.