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Exhaustive Exploration of the Conformational Landscape of Small Cyclic Peptides Using a Robotics Approach.

Maud JusotDirk StratmannMarc VaissetJacques ChomilierJuan Cortes
Published in: Journal of chemical information and modeling (2018)
Small cyclic peptides represent a promising class of therapeutic molecules with unique chemical properties. However, the poor knowledge of their structural characteristics makes their computational design and structure prediction a real challenge. In order to better describe their conformational space, we developed a method, named EGSCyP, for the exhaustive exploration of the energy landscape of small head-to-tail cyclic peptides. The method can be summarized by (i) a global exploration of the conformational space based on a mechanistic representation of the peptide and the use of robotics-based algorithms to deal with the closure constraint and (ii) an all-atom refinement of the obtained conformations. EGSCyP can handle D-form residues and N-methylations. Two strategies for the side-chains placement were implemented and compared. To validate our approach, we applied it to a set of three variants of cyclic RGDFV pentapeptides, including the drug candidate Cilengitide. A comparative analysis was made with respect to replica exchange molecular dynamics simulations in implicit solvent. Its results show that the EGSCyP method provides a very complete characterization of the conformational space of small cyclic pentapeptides.
Keyphrases
  • molecular dynamics simulations
  • molecular dynamics
  • molecular docking
  • single molecule
  • healthcare
  • machine learning
  • amino acid
  • single cell
  • deep learning
  • ultrasound guided
  • dna methylation
  • optic nerve