Machine learning for DNA self-assembly: a numerical case study

ORAL

Abstract

We study the spontaneous self-assembly of two single-stranded DNA (ssDNA) fragments using the coarse-grained oxDNA2 implementation [1]. Successful assembly is a rare event that requires crossing free energy barriers of several kBT. To accurately determine different states and transition rates, we use trajectories from molecular dynamics simulations to construct a Markov state model. To this end, one needs one or more order parameters (OP) that faithfully describe the transition towards an assembled state. We formulate these OP based on structural information, which we map onto structural descriptors. Specifically, we investigate the latent space of EncoderMap [2] and how it changes with the amount of information contained in the descriptor.
With a proper OP, we investigate the stochastics of the self-assembly of two ssDNA molecules in detail.

[1]-Snodin et al., J. Chem. Phys.(2015), 142, 234901
[2]-T. Lemke and C. Peter,J.Chem.TheoryComput.(2019), 15, 1209-1215

*This work was funded by the German Research Foundation (DFG) through project number 233630050 - TRR 146. AN further acknowledges financial support from the DFG through project number NI 1487/2-1and NI 1487/2-2. Computing time was granted on the supercomputer Mogon at Johannes Gutenberg University Mainz (www.hpc.uni-mainz.de).

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Presenters

  • Jörn Appeldorn

    • Institute of Physics, Johannes Gutenberg University Mainz

Authors

  • Jörn Appeldorn

    • Institute of Physics, Johannes Gutenberg University Mainz

  • Arash Nikoubashman

    • University of Mainz
    • Department of Physics, University of Mainz
    • Johannes Gutenberg University
    • Institute of Physics, Johannes Gutenberg University Mainz

  • Thomas Speck

    • Institute of Physics, Johannes Gutenberg University Mainz