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Computational Biology, Part C

Family Pairwise Search and

Cobbling

Robert F. Murphy

Overall Goals

 Find previously unrecognized members of a family

 Develop a model of a family

PSSMs

 Motifs can be summarized and searched for using Position-Specific Scoring Matrices

 Calculated from a multiple alignment of a conserved region for members of a family

Learning PSSMs

 Unsupervised learning methods can be used to find motifs in unaligned sequences

 Best characterized algorithm is MEME  T.L. Bailey & C. Elkan (1995) Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization. Machine Learning J. 21 :51-

Cobbling

 Pick “most representative” protein sequence from a family

 Convert it to a profile by replacing each amino acid by the corresponding column from a similarity matrix

Cobbling

 For each recognized “motif” in the family, replace the corresponding section of the profile with the profile of the motif

Cobbler Illustration

scores from profiles of conserved motifs

similarity scores for sequence from “most representative” family member

sequence of “most representative” family member

Family Pairwise Search

 For all known members of family, calculate (pairwise) homology to each sequence in database (using BLAST) and sum those scores

Which method is best?

 Compare BLAST using a randomly chosen family member, BLAST FPS, MEME, HMMER

 W.N. Gundy (1998) Homology Detection via Family Pairwise Search. J. Comput. Biol. 5 :479-

Comparison Protocol

 For each method For each known protein family Train with family members Search database for matches Rank by score from search Determine how many known family members are ranked highly