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Peptidic Macrocycles - Conformational Sampling and Thermodynamic Characterization.

Anna S Kamenik1, Uta Lessel2, Julian E Fuchs3

  • 1Institute of General, Inorganic and Theoretical Chemistry, Center for Molecular Biosciences Innsbruck , University of Innsbruck , 6020 Innsbruck , Austria.

Journal of Chemical Information and Modeling
|April 14, 2018
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Summary
This summary is machine-generated.

This study introduces a new molecular dynamics method for accurately predicting the 3D structures of macrocycles. This approach enhances drug discovery by efficiently identifying key conformations for peptidic macrocycles.

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Area of Science:

  • Computational Chemistry
  • Structural Biology
  • Drug Discovery

Background:

  • Macrocycles are promising drug candidates due to their specificity.
  • Standard computational methods struggle with macrocycle conformational sampling due to flexibility and restrictions.

Purpose of the Study:

  • To develop and validate a robust molecular dynamics-based routine for conformational profiling of peptidic macrocycles.
  • To overcome limitations in current conformer generation techniques for complex cyclic peptides.

Main Methods:

  • Utilized accelerated molecular dynamics simulations to generate diverse conformational ensembles.
  • Applied energetic cutoffs and geometric clustering to identify populated conformational states.
  • Benchmarked results against NMR and X-ray crystallography data.

Main Results:

  • The method successfully identified key conformational states, including bioactive conformations, for three model macrocyclic systems.
  • Demonstrated robustness and efficiency in conformational sampling and free energy landscape profiling.
  • Reproducibly generated experimentally determined structural ensembles.

Conclusions:

  • The developed molecular dynamics approach is a reliable tool for generating macrocycle conformations.
  • This method holds significant promise for advancing structure-based drug design of macrocyclic therapeutics.