bioRxivBiophysicsPreprint
SketchDNA: A GUI-Enabled Toolkit for Multiscale Modeling of Topological DNA Structures
Abstract
Computational modeling tools have enabled detailed exploration of the structural dynamics of nucleic acids at the nanoscale. Despite these developments, a unified platform for creating multiscale models of topological DNA structures, which are of fundamental importance across rapidly converging biological disciplines, is lacking. Here we present a GUI-enabled topological DNA design tool, SketchDNA (SDNA), that facilitates a user-friendly interface to create all-atom (AA) and coarse-grained (CG) models of DNA structures with tunable topological parameters. Building on the modular and open-source framework of SDNA, the software can be readily integrated with both emerging and existing DNA design platforms, such as oxDNA, MrDNA, etc. We demonstrate the utility of SDNA computational framework by simulating the conformational dynamics of three representative topological DNA structures, minicircles, catenanes, and Borromean rings. Analyzing AA and CG molecular dynamics (MD) simulations of these topological DNA systems using the AMBER and Martini DNA force fields, respectively, we characterize their equilibrium structural dynamics, fluctuations, and topological properties. While the AA simulations allow us to characterize the topology-dependent structural rearrangements at the nucleotide level, long-timescale CG simulations reveal supercoiling-induced conformational transitions. The multiscale SDNA toolkit broadens the applications of MD simulations by enabling in-situ characterization of the biophysical properties of topological DNA nanostructures. The GUI version of SDNA is available at https://sdna.biotech.iith.ac.in without registration, while the source code is accessible at GitHub.
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: protein structure prediction
- research focus: software / tool
- research focus: benchmarking
- research focus: dimensionality reduction
- approach: deep learning
- approach: statistical model
- approach: simulation
- Citations
- 0
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