Physical Chemistry Chemical PhysicsBiophysicsPeer-reviewed journal article
SerraNA: a program to determine nucleic acids elasticity from simulation data
V. Velasco-Berrelleza, M. Burman, J. W. Shepherd, M. C. Leake, R. Golestanian, A. Noy
Abstract
The resistance of DNA to stretch, twist and bend is broadly well estimated by experiments and is important for gene regulation and chromosome packing. However, their sequence-dependence and how bulk elastic constants emerge from local fluctuations is less understood. Here, we present SerraNA, which is an open software that calculates elastic parameters of double-stranded nucleic acids from dinucleotide length up to the whole molecule using ensembles from numerical simulations. The program reveals that global bendability emerge from local periodic bending angles in phase with the DNA helicoidal shape. We also apply SerraNA to the whole set of 136 tetra-bp combinations and we observe a high degree of sequence-dependence for all elastic parameters with differences over 200%. Tetramers with TA and CA base-pair steps are especially flexible, while tetramers containing AA and AT tend to be the most rigid. Our results thus suggest AT-rich motifs generate extreme mechanical properties depending of the exact sequence ordering, which seems critical for creating strong global bendability on longer sequences when phased properly. SerraNA is a tool to be applied in the next generation of interdisciplinary investigations to further understand what determines the elasticity of DNA. Graphical TOC Entry O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/004945v2_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@18aa2ddorg.highwire.dtl.DTLVardef@ca5e02org.highwire.dtl.DTLVardef@1e1febeorg.highwire.dtl.DTLVardef@1b772b_HPS_FORMAT_FIGEXP M_FIG C_FIG
- research focus: software / tool
- research focus: benchmarking
- approach: deep learning
- approach: statistical model
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