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Accelerating Information Retrieval from Profile Hidden Markov Model Databases.

Ahmad Tamimi1, Yaqoub Ashhab2, Hashem Tamimi1,2

  • 1College of Information Technology and Computer Engineering, Palestine Polytechnic University, Hebron, Palestine.

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Clustering protein families represented by Profile Hidden Markov Models (Profile-HMMs) significantly accelerates database searches. This approach reduces search time by 41% while maintaining high recall, improving protein homology detection efficiency.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • Profile Hidden Markov Models (Profile-HMMs) are crucial for representing protein families in biological databases.
  • Existing methods for searching Profile-HMM databases primarily focus on optimizing alignment algorithms, which are becoming insufficient due to database growth and batch query demands.
  • There is a need for enhanced information retrieval strategies to improve the efficiency of homology searches in large Profile-HMM databases.

Purpose of the Study:

  • To develop and evaluate a heuristic method for accelerating homology searches in Profile-HMM databases.
  • To reduce the search space by employing a cluster-based remodeling of the database.
  • To improve the efficiency of detecting sequence-profile and profile-profile homology.

Main Methods:

  • Clustered 4284 TIGRFAMs profiles based on similarity using hierarchical, k-means, and connected component techniques.
  • Assigned a representative profile for each cluster.
  • Implemented an extended step with overlapping clusters to enhance sensitivity.
  • Validated the approach using a benchmark of 6000 randomly selected protein sequences.

Main Results:

  • Achieved an average reduction in search time of 41% compared to sequential search.
  • Maintained an average recall of 96%, demonstrating high sensitivity.
  • The clustering-based approach significantly accelerated data retrieval.

Conclusions:

  • Representing Profile-HMMs through clustering is an effective strategy for accelerating database searches.
  • This method offers a significant improvement in speed and efficiency for protein homology detection.
  • The findings highlight the potential of database remodeling for enhancing information retrieval in bioinformatics.