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A stochastic roadmap method to model protein structural transitions

Published online by Cambridge University Press:  11 December 2015

Kevin Molloy ,
Rudy Clausen  and
Amarda Shehu
Kevin Molloy
Affiliation:
Department of Computer Science, George Mason University, Fairfax, 22030 VA, USA E-mails: kmolloy1@gmu.edu, rclausen@gmu.edu
Rudy Clausen
Affiliation:
Department of Computer Science, George Mason University, Fairfax, 22030 VA, USA E-mails: kmolloy1@gmu.edu, rclausen@gmu.edu
Amarda Shehu*
Affiliation:
Department of Computer Science, George Mason University, Fairfax, 22030 VA, USA E-mails: kmolloy1@gmu.edu, rclausen@gmu.edu Department of Bioengineering, George Mason University, Fairfax, 22030 VA, USA School of Systems Biology, George Mason University, Fairfax, 20110 VA, USA
*
*Corresponding author. E-mail: amarda@gmu.edu
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Summary

Evidence is emerging that the role of protein structure in disease needs to be rethought. Sequence mutations in proteins are often found to affect the rate at which a protein switches between structures. Modeling structural transitions in wildtype and variant proteins is central to understanding the molecular basis of disease. This paper investigates an efficient algorithmic realization of the stochastic roadmap simulation framework to model structural transitions in wildtype and variants of proteins implicated in human disorders. Our results indicate that the algorithm is able to extract useful information on the impact of mutations on protein structure and function.

Keywords

Protein dynamicsStructural transitionsConformational switchingSOD1 and Ras variantsHuman disordersStochastic roadmap simulationMarkov state modelFirst-step analysisPath smoothingEnergy landscape
Type
Articles
Information
Robotica , Volume 34 , Issue 8 , August 2016 , pp. 1705 - 1733
Copyright
Copyright © Cambridge University Press 2015 

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