Statistical analyses and computational prediction of helical kinks in membrane proteins

Y. H. Huang, C. M. Chen

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

We have carried out statistical analyses and computer simulations of helical kinks for TM helices in the PDBTM database. About 59 % of 1562 TM helices showed a significant kink, and 38 % of these kinks are associated with prolines in a range of ±4 residues. Our analyses show that helical kinks are more populated in the central region of helices, particularly in the range of 1-3 residues away from the helix center. Among 1,053 helical kinks analyzed, 88 % of kinks are bends (change in helix axis without loss of helical character) and 12 % are disruptions (change in helix axis and loss of helical character). It is found that proline residues tend to cause larger kink angles in helical bends, while this effect is not observed in helical disruptions. A further analysis of these kinked helices suggests that a kinked helix usually has 1-2 broken backbone hydrogen bonds with the corresponding N-O distance in the range of 4.2-8.7 Å , whose distribution is sharply peaked at 4.9 Å followed by an exponential decay with increasing distance. Our main aims of this study are to understand the formation of helical kinks and to predict their structural features. Therefore we further performed molecular dynamics (MD) simulations under four simulation scenarios to investigate kink formation in 37 kinked TM helices and 5 unkinked TM helices. The representative models of these kinked helices are predicted by a clustering algorithm, SPICKER, from numerous decoy structures possessing the above generic features of kinked helices. Our results show an accuracy of 95 % in predicting the kink position of kinked TM helices and an error less than 10- in the angle prediction of 71.4 % kinked helices. For unkinked helices, based on various structure similarity tests, our predicted models are highly consistent with their crystal structure. These results provide strong supports for the validity of our method in predicting the structure of TM helices.

Original languageEnglish
Pages (from-to)1171-1185
Number of pages15
JournalJournal of Computer-Aided Molecular Design
Volume26
Issue number10
DOIs
Publication statusPublished - 2012 Oct 1

Keywords

  • Computational prediction
  • Helical kinks
  • Molecular dynamics simulations

ASJC Scopus subject areas

  • Drug Discovery
  • Computer Science Applications
  • Physical and Theoretical Chemistry

Fingerprint Dive into the research topics of 'Statistical analyses and computational prediction of helical kinks in membrane proteins'. Together they form a unique fingerprint.

  • Cite this