Analysis of DNA interactions and GC content with energy decomposition in large-scale quantum mechanical calculations
Analysis of DNA interactions and GC content with energy decomposition in large-scale quantum mechanical calculations
GC content is a contributing factor to the stability of nucleic acids due to hydrogen bonding. More hydrogen bonding generally results in greater stability. Empirical evidence, however, has suggested that the GC content of a nucleic acid is a poor predictor of its stability, implying that there are sequence-dependent interactions besides what its GC content indicates. To examine how much such sequence-dependent interactions affect the interaction energies of double-stranded DNA (dsDNA) molecules, dsDNA molecules of different sequences are generated and examined in silico for variabilities in the interaction energies within each group of dsDNA molecules of the same GC content. Since the amount of hydrogen bonding depends on the GC content, holding the GC content fixed when examining the differences in interaction energies allows sequence-dependent interactions to be isolated. The nature of sequence-dependent interactions is then dissected using energy decomposition analysis (EDA). By using EDA, the components of the interactions that depend on the neighboring base pairs help explain some of the variability in the interaction energies of the dsDNA molecules despite having the same GC content. This work provides a new paradigm and tool for the study and analysis of the distributions of interaction components in dsDNA with the same GC content using EDA within large-scale quantum chemistry calculations.
8891-8899
Chen, Han
3f0c8bfb-38cc-41ad-aa92-6f77bfa0897b
Skylaris, Chris-Kriton
8f593d13-3ace-4558-ba08-04e48211af61
14 April 2021
Chen, Han
3f0c8bfb-38cc-41ad-aa92-6f77bfa0897b
Skylaris, Chris-Kriton
8f593d13-3ace-4558-ba08-04e48211af61
Chen, Han and Skylaris, Chris-Kriton
(2021)
Analysis of DNA interactions and GC content with energy decomposition in large-scale quantum mechanical calculations.
Physical Chemistry Chemical Physics, 23 (14), .
(doi:10.1039/D0CP06630C).
Abstract
GC content is a contributing factor to the stability of nucleic acids due to hydrogen bonding. More hydrogen bonding generally results in greater stability. Empirical evidence, however, has suggested that the GC content of a nucleic acid is a poor predictor of its stability, implying that there are sequence-dependent interactions besides what its GC content indicates. To examine how much such sequence-dependent interactions affect the interaction energies of double-stranded DNA (dsDNA) molecules, dsDNA molecules of different sequences are generated and examined in silico for variabilities in the interaction energies within each group of dsDNA molecules of the same GC content. Since the amount of hydrogen bonding depends on the GC content, holding the GC content fixed when examining the differences in interaction energies allows sequence-dependent interactions to be isolated. The nature of sequence-dependent interactions is then dissected using energy decomposition analysis (EDA). By using EDA, the components of the interactions that depend on the neighboring base pairs help explain some of the variability in the interaction energies of the dsDNA molecules despite having the same GC content. This work provides a new paradigm and tool for the study and analysis of the distributions of interaction components in dsDNA with the same GC content using EDA within large-scale quantum chemistry calculations.
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d0cp06630c
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Accepted/In Press date: 16 March 2021
e-pub ahead of print date: 6 April 2021
Published date: 14 April 2021
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Funding Information:
The authors acknowledge the use of the IRIDIS 5 High-Performance Computing Facility, and associated support services at the University of Southampton. The authors are also grateful for computational support from the UK Materials and Molecular Modelling Hub, which is partially funded by EPSRC (EP/P020194 and EP/T022213/1). This work is part of the University of Southampton’s Centre for Doctoral Training (CDT) program in Next Generation Computational Modelling (NGCM).
Funding Information:
The authors acknowledge the use of the IRIDIS 5 High-Performance Computing Facility, and associated support services at the University of Southampton. The authors are also grateful for computational support from the UK Materials and Molecular Modelling Hub, which is partially funded by EPSRC (EP/P020194 and EP/T022213/1). This work is part of the University of Southampton's Centre for Doctoral Training (CDT) program in Next Generation Computational Modelling (NGCM).
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© the Owner Societies 2021.
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Copyright 2021 Elsevier B.V., All rights reserved.
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Local EPrints ID: 448660
URI: http://eprints.soton.ac.uk/id/eprint/448660
ISSN: 1463-9076
PURE UUID: b4d3c049-f822-469e-b32e-e7c2157c13dd
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Date deposited: 29 Apr 2021 16:31
Last modified: 06 Jun 2024 01:44
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Han Chen
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