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Computational methods for remote homolog identification.

Xiu-Feng Wan1, Dong Xu

  • 1Digital Biology Laboratory, Department of Computer Science, University of Missouri - Columbia, Columbia, MO 65211, USA. xudong@missouri.edu

Current Protein & Peptide Science
|December 31, 2005
PubMed
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Identifying remote protein homologs is crucial for understanding protein function and evolution. This review covers computational techniques, including sequence and structure comparisons, to find distantly related proteins.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology
  • Protein Science

Background:

  • The increasing availability of protein sequences necessitates robust methods for homolog identification.
  • Remote homologs, proteins with low sequence identity due to ancient divergence, pose a significant challenge.
  • Accurate identification of remote homologs is vital for functional, structural, and evolutionary protein studies.

Purpose of the Study:

  • To provide a comprehensive review of computational techniques for remote homolog identification.
  • To compare different methods, highlighting their technical approaches, strengths, and limitations.
  • To offer pointers to relevant online resources and databases for these methods.

Main Methods:

Related Experiment Videos

  • Review of sequence-sequence comparison techniques.
  • Review of sequence-structure comparison techniques.
  • Review of structure-structure comparison techniques, alongside miscellaneous approaches.

    • Main Results:

    • Significant advances in computational techniques for remote homolog identification have been achieved.
    • A comparative analysis of various methods is presented, detailing their pros and cons.
    • Online resources and databases supporting these methods are cataloged.

    Conclusions:

    • Computational methods, particularly those leveraging sequence and structure comparisons, are essential for identifying remote protein homologs.
    • The review provides a valuable resource for researchers in bioinformatics and related fields.
    • Case studies, such as those involving SARS-CoV proteins, demonstrate the practical application of these identification techniques.