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

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...

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Published inMolecules (Basel, Switzerland) Vol. 23; no. 2; p. 373
Main Authors Denarie, Laurent, Al-Bluwi, Ibrahim, Vaisset, Marc, Siméon, Thierry, Cortés, Juan
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 09.02.2018
MDPI
Subjects
Biochemistry
Biochemistry, Molecular Biology
Bioinformatics
Chemical Sciences
Computer Science
conformational sampling
Life Sciences
Models, Molecular
Monte Carlo
Monte Carlo Method
Monte Carlo simulation
Oligopeptides - chemistry
or physical chemistry
Probability
Protein Conformation
Proteins
Proteins - chemistry
Representations
Robotics
robotics-inspired approach
Sampling
Software
Stochasticity
Theoretical and
Monte Carlo
conformational sampling
robotics-inspired approach
proteins
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Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
Current address: Computer Science Department, Princeton University, Princeton, NJ 08540, USA.
ISSN:1420-3049
1420-3049
DOI:10.3390/molecules23020373