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Indexing protein structures using suffix trees.

Feng Gao1, Mohammed J Zaki

  • 1Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, USA.

Methods in Molecular Biology (Clifton, N.J.)
|December 14, 2007
PubMed
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This study introduces a novel protein structure indexing method using local feature vectors and suffix trees. The approach significantly improves protein classification accuracy and retrieval of similar protein structures.

Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Bioinformatics

Background:

  • Efficient protein structure searching is crucial for predicting protein function, classification, and drug discovery.
  • Existing methods for protein structure indexing and similarity searching have limitations in speed and scalability.

Purpose of the Study:

  • To develop a new, fast, and scalable method for indexing protein structures.
  • To improve the accuracy and efficiency of searching for proteins with similar structures.

Main Methods:

  • Representing protein residues as triangles and calculating local feature vectors based on Calpha atom distances and plane normal angles.
  • Indexing these local feature vectors using suffix trees for efficient retrieval of maximal matches.
  • Chaining maximal matches to align query structures with database proteins and selecting similar proteins based on alignment scores.

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Main Results:

  • Achieved high classification accuracy: up to 97.8% at the superfamily level and 99.4% at the class level (SCOP classification).
  • Successfully retrieved an average of 7.49 out of 10 proteins from the same superfamily within the top 10 matches.
  • Demonstrated superior performance compared to existing state-of-the-art methods.

Conclusions:

  • The proposed local feature vector extraction and suffix tree indexing method offers a significant advancement in protein structure analysis.
  • This method enhances the ability to accurately classify and identify similar protein structures, benefiting downstream applications like function prediction and drug discovery.