Functionally important segments in proteins dissected using Gene Ontology and geometric clustering of peptide fragments.
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.
Manikandan
Karuppasamy
K
Bioinformatics Centre, Indian Institute of Science, Bangalore 560012, India. kmani@embl-hamburg.de
Pal
Debnath
D
Ramakumar
Suryanarayanarao
S
Brener
Nathan E
NE
Iyengar
Sitharama S
SS
Seetharaman
Guna
G
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*
D010446:Peptide Fragments; D011506:Proteins