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Enumerating and ranking discrete motifs

C G Nevill-Manning1, K S Sethi, T D Wu

  • 1Biochemistry Department, Stanford University, CA 94305-5307, USA. cnevill@stanford.edu

Proceedings. International Conference on Intelligent Systems for Molecular Biology
|January 1, 1997
PubMed
Summary

Researchers developed a method to identify protein sequence motifs for functional classification. This technique efficiently searches motif spaces, enabling accurate classification of new protein sequences and identification of subclasses.

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Automated construction of structural motifs for predicting functional sites on protein structures.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2003

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • Discrete motifs are crucial for understanding protein function, structure, and classification.
  • The potential number of motifs can grow exponentially, posing computational challenges.

Purpose of the Study:

  • To develop an efficient technique for inferring protein sequence motifs.
  • To accurately classify new protein sequences and identify functional subclasses.

Main Methods:

  • Exhaustive search of the motif space from aligned protein sequences.
  • Generation of motifs across a range of recall and precision values.
  • Selection of representative motifs using a score from statistical and information-theoretic frameworks.

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Main Results:

  • The method successfully infers protein sequence motifs.
  • Selected motifs demonstrate high precision in classifying unseen protein sequences.
  • Inferred protein subclasses align with known biochemical classifications.

Conclusions:

  • The developed technique efficiently identifies functional protein sequence motifs.
  • This approach aids in accurate protein classification and subclass discovery.
  • The method has practical applications in bioinformatics and structural biology.