title: Functionally important segments in proteins dissected using Gene Ontology and geometric clustering of peptide fragments.
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.
authors.affiliation: Bioinformatics Centre, Indian Institute of Science, Bangalore 560012, India. kmani@embl-hamburg.de
authors.forename: Karuppasamy
authors.initials: K
authors.lastname: Manikandan
authors.forename: Debnath
authors.initials: D
authors.lastname: Pal
authors.forename: Suryanarayanarao
authors.initials: S
authors.lastname: Ramakumar
authors.forename: Nathan E
authors.initials: NE
authors.lastname: Brener
authors.forename: Sitharama S
authors.initials: SS
authors.lastname: Iyengar
authors.forename: Guna
authors.initials: G
authors.lastname: Seetharaman
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D006860:Hydrogen Bonding; D010446:Peptide Fragments / Q000737:chemistry*; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D010446:Peptide Fragments; D011506:Proteins