title: Topological side-chain classification of beta-turns: ideal motifs for peptidomimetic development.
abstract: Beta-turns are important topological motifs for biological recognition of proteins and peptides. Organic molecules that sample the side chain positions of beta-turns have shown broad binding capacity to multiple different receptors, for example benzodiazepines. Beta-turns have traditionally been classified into various types based on the backbone dihedral angles (phi2, psi2, phi3 and psi3). Indeed, 57-68% of beta-turns are currently classified into 8 different backbone families (Type I, Type II, Type I', Type II', Type VIII, Type VIa1, Type VIa2 and Type VIb and Type IV which represents unclassified beta-turns). Although this classification of beta-turns has been useful, the resulting beta-turn types are not ideal for the design of beta-turn mimetics as they do not reflect topological features of the recognition elements, the side chains. To overcome this, we have extracted beta-turns from a data set of non-homologous and high-resolution protein crystal structures. The side chain positions, as defined by C(alpha)-C(beta) vectors, of these turns have been clustered using the kth nearest neighbor clustering and filtered nearest centroid sorting algorithms. Nine clusters were obtained that cluster 90% of the data, and the average intra-cluster RMSD of the four C(alpha)-C(beta) vectors is 0.36. The nine clusters therefore represent the topology of the side chain scaffold architecture of the vast majority of beta-turns. The mean structures of the nine clusters are useful for the development of beta-turn mimetics and as biological descriptors for focusing combinatorial chemistry towards biologically relevant topological space.
authors.affiliation: Protagonist Pty Ltd, Level 7, Queensland Bioscience Precinct, 306 Carmody Road, 4072, Brisbane, St Lucia, Australia.
authors.forename: Tran Trung
authors.initials: TT
authors.lastname: Tran
authors.forename: Jim
authors.initials: J
authors.lastname: McKie
authors.forename: Wim D F
authors.initials: WD
authors.lastname: Meutermans
authors.forename: Gregory T
authors.initials: GT
authors.lastname: Bourne
authors.forename: Peter R
authors.initials: PR
authors.lastname: Andrews
authors.forename: Mark L
authors.initials: ML
authors.lastname: Smythe
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D030562:Databases, Protein; D015195:Drug Design; D008958:Models, Molecular; D018716:Molecular Mimicry; D010455:Peptides / Q000737:chemistry*; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*
chemical_list: D010455:Peptides; D011506:Proteins