Topological side-chain classification of beta-turns: Ideal motifs for peptidomimetic development

Tran, TT, McKie, J, Meutermans, WDF, Bourne, GT, Andrews, PR and Smythe, ML (2005) Topological side-chain classification of beta-turns: Ideal motifs for peptidomimetic development. Journal of Computer-aided Molecular Design, 19 8: 551-566. doi:10.1007/s10822-005-9006-2

Author Tran, TT
McKie, J
Meutermans, WDF
Bourne, GT
Andrews, PR
Smythe, ML
Title Topological side-chain classification of beta-turns: Ideal motifs for peptidomimetic development
Journal name Journal of Computer-aided Molecular Design   Check publisher's open access policy
ISSN 0920-654X
Publication date 2005-01-01
Sub-type Article (original research)
DOI 10.1007/s10822-005-9006-2
Volume 19
Issue 8
Start page 551
End page 566
Total pages 15
Place of publication Netherlands
Publisher Springer
Language eng
Subject C1
250302 Biological and Medical Chemistry
780103 Chemical sciences
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 (phi 2, psi 2, phi 3 and psi 3). 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.
Keyword Biochemistry & Molecular Biology
Computer Science, Interdisciplinary Applications
Beta-turn Classification
Beta-turn Mimetics
Drug Design
Protein-protein Interfaces
Hormone-releasing Hormone
Molecular Mimics
Design Criteria
Q-Index Code C1

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Created: Wed, 15 Aug 2007, 17:14:59 EST