Exhaustive exploration of the conformational landscape of small cyclic peptides using a robotics approach - Archive ouverte HAL Access content directly
Journal Articles Journal of Chemical Information and Modeling Year : 2018

Exhaustive exploration of the conformational landscape of small cyclic peptides using a robotics approach

(1, 2) , (1) , (3) , (1) , (2)
1
2
3

Abstract

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, (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 1 analysis was made with respect to replica exchange molecular dynamics simulations in implicit solvent. It results that the EGSCyP method provides a very complete characterization of the conformational space of small cyclic pentapeptides.
Fichier principal
Vignette du fichier
manuscript.pdf (2 Mo) Télécharger le fichier
Vignette du fichier
supporting_informations.pdf (4.89 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01893751 , version 1 (11-10-2018)

Identifiers

Cite

Maud Jusot, Dirk Stratmann, Marc Vaisset, Jacques Chomilier, Juan Cortés. Exhaustive exploration of the conformational landscape of small cyclic peptides using a robotics approach. Journal of Chemical Information and Modeling, 2018, 58 (11), pp.2355-2368. ⟨10.1021/acs.jcim.8b00375⟩. ⟨hal-01893751⟩
215 View
0 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More