Topological side-chain classification of beta-turns: ideal motifs for peptidomimetic development.
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.
Tran
Tran Trung
TT
Protagonist Pty Ltd, Level 7, Queensland Bioscience Precinct, 306 Carmody Road, 4072, Brisbane, St Lucia, Australia.
McKie
Jim
J
Meutermans
Wim D F
WD
Bourne
Gregory T
GT
Andrews
Peter R
PR
Smythe
Mark L
ML
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*
D010455:Peptides; D011506:Proteins