Segmenting proteins into tripeptides to enhance conformational sampling with monte carlo

Methods Laurent Denarie, Ibrahim Al-Bluwi, Marc Vaisset, Thierry Siméon, Juan Cortés

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


This paper presents an approach to enhance conformational sampling of proteins employing stochastic algorithms such as Monte Carlo (MC) methods. The approach is based on a mechanistic representation of proteins and on the application of methods originating from robotics. We outline the general ideas of our approach and detail how it can be applied to construct several MC move classes, all operating on a shared representation of the molecule and using a single mathematical solver. We showcase these sampling techniques on several types of proteins. Results show that combining several move classes, which can be easily implemented thanks to the proposed approach, significantly improves sampling efficiency.

Original languageEnglish (US)
Article number373
Issue number2
StatePublished - 2018

All Science Journal Classification (ASJC) codes

  • Drug Discovery
  • Analytical Chemistry
  • Chemistry (miscellaneous)
  • Molecular Medicine
  • Physical and Theoretical Chemistry
  • Pharmaceutical Science
  • Organic Chemistry


  • Conformational sampling
  • Monte Carlo
  • Proteins
  • Robotics-inspired approach


Dive into the research topics of 'Segmenting proteins into tripeptides to enhance conformational sampling with monte carlo'. Together they form a unique fingerprint.

Cite this