Functionally important segments in proteins dissected using Gene Ontology and geometric clustering of peptide
Karuppasamy Manikandan1, Debnath Pal, Suryanarayanarao Ramakumar
1Bioinformatics Centre, Indian Institute of Science, Bangalore 560012, India. kmani@embl-hamburg.de
Abstract:
We have developed a geometric clustering algorithm using backbone phi,psi angles to group conformationally similar peptide fragments of any length. By labeling each fragment in the cluster with the level-specific Gene Ontology 'molecular function' term of its protein, we are able to compute statistics for molecular function-propensity and p-value of individual fragments in the cluster. Clustering-cum-statistical analysis for peptide fragments 8 residues in length and with only trans peptide bonds shows that molecular function propensities > or =20 and p-values < or =0.05 can dissect fragments within a protein linked to the molecular function.
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