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Protein Sequence Motif Extraction through Clustering Analysis

Explore protein sequence motifs using clustering algorithms like K-means and Fuzzy C-means. Extract universality and relation to protein structures. Reduce time and space complexity for motif identification.

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Protein Sequence Motif Extraction through Clustering Analysis

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  1. My Research Work and Clustering Bernard Chen 2009

  2. Outline • Introduction • Experimental Setup • Clustering • Future Works

  3. Central Dogma of Molecular Biology

  4. Amino Acids, the subunit of proteins

  5. Protein Primary, Secondary, and Tertiary Structure

  6. Protein 3D Structure

  7. Protein Sequence Motif • Although there are 20 amino acids, the construction of protein primary structure is not randomly choose among those amino acids • Sequence Motif: A relatively small number of functionally or structurally conserved sequence patterns that occurs repeatedly in a group of related proteins.

  8. Protein Sequence Motif These biologically significant regions or residues are usually: • Enzyme catalytic site • Prostethic group attachment sites (heme, pyridoxal-phosphate, biotin…) • Amino acid involved in binding a metal ion • Cysteines involved in disulfide bonds • Regions involved in binding a molecule (ATP/ADP, GDP/GTP, Ca, DNA…)

  9. Goal of the our group • The main purpose is trying to obtain and extract protein sequence motifs which are universally conserved and across protein family boundaries. • Discuss the relation between Protein Primary structure and Tertiary structure

  10. Outline • Introduction • Experimental Setup • Clustering • Future Works

  11. Experiment setup: HSSP matrix: 1b25

  12. HSSP matrix: 1b25

  13. Representation of Segment • Sliding window size: 9 • Each window corresponds to a sequence segment, which is represented by a 9 × 20 matrix plus additional nine corresponding secondary structure information obtained from DSSP. • More than 560,000 segments (413MB) are generated by this method. • DSSP: Obtain 2nd Structure information

  14. Outline • Introduction • Experimental Setup • Clustering • Future Works

  15. Clustering Algorithms • There are two clustering algorithms we used in our approach: • K-means Clustering • Fuzzy C-means Clustering

  16. K-means Clustering

  17. K-means Clustering

  18. K-means Clustering

  19. K-means Clustering

  20. K-means Clustering

  21. Fuzzy C-means Clustering

  22. Fuzzy C-means Clustering

  23. Fuzzy C-means Clustering

  24. Fuzzy C-means Clustering

  25. Fuzzy C-means Clustering

  26. Fuzzy C-means Clustering

  27. Fuzzy C-means Clustering

  28. Outline • Introduction • Experimental Setup • Clustering • Future Works

  29. Original dataset Fuzzy C-Means Clustering Information Granule 1 ... Information Granule M K-means Clustering ... K-means Clustering Join Information Final Sequence Motifs Information Granular Computing Model

  30. Motivation

  31. Reduce Space-complexity Table 1 summary of results obtained by FCM

  32. Reduce Time-complexity Wei’s method: 1285968 sec (15 days) * 6 = 7715568 sec (90 days) Granular Model: 154899 sec + 231720 sec * 6 = 1545219 sec (18 days) (FCM exe time) (2.7 Days)

  33. HSSP-BLOSUM62 Measure

  34. FutureWorks

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