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

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 k_BT. 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