Characterizing energy landscapes of peptides using a combination of stochastic algorithms

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Devaurs, Didier and Molloy, Kevin and Vaisset, Marc and Shehu, Amarda and Siméon, Thierry and Cortés, Juan (2015) Characterizing energy landscapes of peptides using a combination of stochastic algorithms. IEEE Transactions on NanoBioscience, 14 (5). pp. 545-552. ISSN 1536-1241 (https://doi.org/10.1109/TNB.2015.2424597)

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Abstract

Obtaining accurate representations of energy landscapes of biomolecules such as proteins and peptides is central to the study of their physicochemical properties and biological functions. Peptides are particularly interesting, as they exploit structural flexibility to modulate their biological function. Despite their small size, peptide modeling remains challenging due to the complexity of the energy landscape of such highly-flexible dynamic systems. Currently, only stochastic sampling-based methods can efficiently explore the conformational space of a peptide. In this paper, we suggest to combine two such methods to obtain a full characterization of energy landscapes of small yet flexible peptides. First, we propose a simplified version of the classical Basin Hopping algorithm to reveal low-energy regions in the landscape, and thus to identify the corresponding meta-stable structural states of a peptide. Then, we present several variants of a robotics-inspired algorithm, the Transition-based Rapidly-exploring Random Tree, to quickly determine transition path ensembles, as well as transition probabilities between meta-stable states. We demonstrate this combined approach on met-enkephalin.

ORCID iDs

Devaurs, Didier ORCID logoORCID: https://orcid.org/0000-0002-3415-9816, Molloy, Kevin, Vaisset, Marc, Shehu, Amarda, Siméon, Thierry and Cortés, Juan;
  • Item type:Article
    ID code:90243
    Dates:
    Date
    Event
    1 July 2015
    Published
    29 April 2015
    Published Online
    31 March 2015
    Accepted
    Notes:© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Science > Mathematics > Electronic computers. Computer science
    Department:Faculty of Science > Computer and Information Sciences
    Depositing user: Pure Administrator
    Date deposited:14 Aug 2024 09:42
    Last modified:11 Nov 2024 14:25
    URI:https://strathprints.strath.ac.uk/id/eprint/90243
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