title: Protein fragment clustering and canonical local shapes.
abstract: A novel clustering method is used to cluster protein fragments by shape. The centroids (mean fragments from each cluster) form a basis set of structural motifs. A database of 156,643 seven-residue fragments is used, and eight different basis sets with varying levels of resolution are generated. Coarse basis sets contain tens of centroids and provide meaningful local shapes, which are more detailed than the traditional secondary structure categories. High-resolution basis sets contain thousands of centroids and can be used to model tertiary structure of longer segments. The basis sets generated fit nontraining set proteins with the expected accuracy.
authors.affiliation: Center for Biophysics and Computational Biology, University of Illinois at Urbana-Champaign, Illinois, USA.
authors.forename: Cornelius G
authors.initials: CG
authors.lastname: Hunter
authors.forename: Shankar
authors.initials: S
authors.lastname: Subramaniam
mesh_terms: D000818:Animals; D016000:Cluster Analysis; D030562:Databases, Protein; D008958:Models, Molecular; D015394:Molecular Structure; D010446:Peptide Fragments / Q000737:chemistry*; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results; D040681:Structural Homology, Protein
chemical_list: D010446:Peptide Fragments; D011506:Proteins