Method

Functionally important segments in proteins dissected using Gene Ontology and geometric clustering of peptide fragments

Karuppasamy Manikandan^1,2 , Debnath Pal^1,3,5 , Suryanarayanarao Ramakumar^1,2,3 , Nathan E Brener⁴ , Sitharama S Iyengar⁴ and Guna Seetharaman⁴

¹Bioinformatics Centre, Indian Institute of Science, Bangalore 560012, India

²Department of Physics, Indian Institute of Science, Bangalore 560012, India

³Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore 560012, India

⁴Department of Computer Science, Louisiana State University, Baton Rouge, LA 70803, USA

⁵Main correspondence

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Genome Biology 2008, 9:R52doi:10.1186/gb-2008-9-3-r52

Published:	10 March 2008

Subject areas: Bioinformatics, Biochemistry and structural biology

Abstract

We have developed a geometric clustering algorithm using backbone φ,ψ 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 ≥20 and p-values ≤0.05 can dissect fragments within a protein linked to the molecular function.