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Computational investigation of RNA CUG repeats responsible for myotonic dystrophy 1.


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Authors

Yildirim, Ilyas 
Chakraborty, Debayan 
Disney, Matthew D 
Wales, David J 
Schatz, George C 

Abstract

Despite the importance of the knowledge of molecular hydration entropy (ΔShyd) in chemical and biological processes, the exact calculation of ΔShyd is very difficult, because of the complexity in solute–water interactions. Although free-energy perturbation (FEP) methods have been employed quite widely in the literature, the poor convergent behavior of the van der Waals interaction term in the potential function limited the accuracy and robustness. In this study, we propose a new method for estimating ΔShyd by means of combining the FEP approach and the scaled particle theory (or information theory) to separately calculate the electrostatic solute–water interaction term (ΔSelec) and the hydrophobic contribution approximated by the cavity formation entropy (ΔScav), respectively. Decomposition of ΔShyd into ΔScav and ΔSelec terms is found to be very effective with a substantial accuracy enhancement in ΔShyd estimation, when compared to the conventional full FEP calculations. ΔScav appears to dominate over ΔSelec in magnitude, even in the case of polar solutes, implying that the major contribution to the entropic cost for hydration comes from the formation of a solvent-excluded volume. Our hybrid scaled particle theory and FEP method is thus found to enhance the accuracy of ΔShyd prediction by effectively complementing the conventional full FEP method.

Description

Keywords

Base Pairing, Humans, Molecular Dynamics Simulation, Myotonic Dystrophy, RNA, RNA-Binding Proteins, Thermodynamics, Trinucleotide Repeats

Journal Title

J Chem Theory Comput

Conference Name

Journal ISSN

1549-9618
1549-9626

Volume Title

11

Publisher

American Chemical Society (ACS)
Sponsorship
European Research Council (267369)
Engineering and Physical Sciences Research Council (EP/I001352/1)
Computations were done in Advanced Research Computing (QUEST) at the Northwestern University, and Theory Group Computing Clusters at the University of Cambridge. This work was supported by the National Science Foundation Grant CHE-1147335) (GCS), PS-OC Center of the NIH/NCI Grant 1U54CA143869-01 (GCS), NIH Grant R01GM097455 (MDD), Muscular Dystrophy Association Grant 254929 (MDD), and the EPSRC Grant EP/I001352/1 (DJW), and the ERC Grant RG59508 (DJW). DC acknowledges financial support from the Cambridge Commonwealth European and International Trust.