cgNA+min: computation of sequence-dependent dsDNA energy-minimising minicircle configurations
Abstract
DNA minicircles are closed double-stranded DNA (dsDNA) fragments that have been demonstrated to be an important experimental tool to understand supercoiled, or stressed, DNA mechanics, such as nucleosome positioning and DNA-protein interactions. Specific minicircles can be simulated using Molecular Dynamics (MD) simulation. However, the enormous sequence space makes it unfeasible to exhaustively explore the sequence-dependent mechanics of DNA minicircles using either experiment or MD. For linear fragments, the cgNA+ model, a computationally efficient sequence-dependent coarse-grained model using enhanced Curves+ internal coordinates (rigid base plus rigid phosphate) of double-stranded nucleic acids (dsNAs), predicts highly accurate nonlocal sequence-dependent equilibrium distributions for an arbitrary sequence when compared with MD simulations. This article addresses the problem of modeling sequence-dependent topologically closed and, therefore, stressed fragments of dsDNA. We introduce cgNA+min, a computational approach within the cgNA+ framework, which extends the cgNA+ model applicability to compute the sequence-dependent energy minimising configurations of covalently closed dsDNA minicircles of various lengths and linking numbers (Lk). The main idea is to derive the appropriate chain rule to express the cgNA+ energy in absolute coordinates involving quaternions where the closure condition is simple to handle. We also present a semi-analytic method for efficiently computing sequence-dependent initial minicircles having arbitrary Lk and length. For different classes and lengths of sequences, we demonstrate that the dsDNA minicircle energies computed using cgNA+min agree well with the energies approximated from experimentally measured J-factor values. Finally, we present the minicircle shape, energy, and multiplicity of Lk for more than 120K random DNA sequences of different lengths.
- Publication:
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arXiv e-prints
- Pub Date:
- November 2024
- DOI:
- 10.48550/arXiv.2411.06036
- arXiv:
- arXiv:2411.06036
- Bibcode:
- 2024arXiv241106036S
- Keywords:
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- Quantitative Biology - Biomolecules;
- Condensed Matter - Soft Condensed Matter;
- Physics - Biological Physics