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Article

Segmenting Proteins into Tripeptides to Enhance Conformational Sampling with Monte Carlo Methods

LAAS-CNRS, Université de Toulouse, CNRS, 31400 Toulouse, France
*
Author to whom correspondence should be addressed.
Current address: Computer Science Department, Princeton University, Princeton, NJ 08540, USA.
Molecules 2018, 23(2), 373; https://doi.org/10.3390/molecules23020373
Received: 13 December 2017 / Revised: 22 January 2018 / Accepted: 1 February 2018 / Published: 9 February 2018
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. View Full-Text
Keywords: conformational sampling; Monte Carlo; proteins; robotics-inspired approach conformational sampling; Monte Carlo; proteins; robotics-inspired approach
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MDPI and ACS Style

Denarie, L.; Al-Bluwi, I.; Vaisset, M.; Siméon, T.; Cortés, J. Segmenting Proteins into Tripeptides to Enhance Conformational Sampling with Monte Carlo Methods. Molecules 2018, 23, 373. https://doi.org/10.3390/molecules23020373

AMA Style

Denarie L, Al-Bluwi I, Vaisset M, Siméon T, Cortés J. Segmenting Proteins into Tripeptides to Enhance Conformational Sampling with Monte Carlo Methods. Molecules. 2018; 23(2):373. https://doi.org/10.3390/molecules23020373

Chicago/Turabian Style

Denarie, Laurent; Al-Bluwi, Ibrahim; Vaisset, Marc; Siméon, Thierry; Cortés, Juan. 2018. "Segmenting Proteins into Tripeptides to Enhance Conformational Sampling with Monte Carlo Methods" Molecules 23, no. 2: 373. https://doi.org/10.3390/molecules23020373

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