Understanding sequence-dependent DNA dynamics through self-associative machine learning and temperature-jump spectroscopy

Despite rapid advances in DNA nanotechnology and a robust understanding of the associated thermodynamics, the sequence-dependent mechanisms of DNA hybridization are not fully understood. In this work, we investigate these dynamics by performing equilibrium coarse-grained simulations of oligonucleotide sequences with varied G:C placement. We employ State-Free Reversible VAMPnets to directly learn the slowest dynamical modes of each sequence and to optimize Markov State Models (MSMs) construction. Furthermore, we perform elevated temperature simulations to recapitulate temperature-jump IR and FTIR data collected on the oligonucleotides. For repetitive sequences, we find a spectrum of slow dynamics associated with out-of-register base pairing and kinetically relevant transitions between these states. In contrast, G:C pairs near the center of the duplex induce more rapid fraying dynamics. In both cases, hybridization/dissociation mechanisms deviate from an “all-or-nothing” model. Our computational predictions are in excellent accord with experiment, and provide new fundamental understanding of the sequence-dependent kinetics and mechanisms of DNA hybridization.