Abstract
A robust understanding of the sequence-dependent thermodynamics of DNA hybridization has enabled rapid advances in DNA nanotechnology. A fundamental understanding of the sequence-dependent kinetics and mechanisms of hybridization and dehybridization remains comparatively underdeveloped. In this work, we establish new understanding of the sequence-dependent hybridization/dehybridization kinetics and mechanism within a family of self-complementary pairs of 10-mer DNA oligomers by integrating coarse-grained molecular simulation, machine learning of the slow dynamical modes, data-driven inference of long-time kinetic models, and experimental temperature-jump infrared spectroscopy. For a repetitive ATATATATAT sequence, we resolve a rugged dynamical landscape comprising multiple metastable states, numerous competing hybridization/dehybridization pathways, and a spectrum of dynamical relaxations. Introduction of a G:C pair at the terminus (GATATATATC) or center (ATATGCATAT) of the sequence reduces the ruggedness of the dynamics landscape by eliminating a number of metastable states and reducing the number of competing dynamical pathways. Only by introducing a G:C pair midway between the terminus and the center to maximally disrupt the repetitive nature of the sequence (ATGATATCAT) do we recover a canonical “all-or-nothing” two-state model of hybridization/dehybridization with no intermediate metastable states. Our results establish new understanding of the dynamical richness of sequence-dependent kinetics and mechanisms of DNA hybridization/dehybridization by furnishing quantitative and predictive kinetic models of the dynamical transition network between metastable states, present a molecular basis with which to understand experimental temperature jump data, and furnish foundational design rules by which to rationally engineer the kinetics and pathways of DNA association and dissociation for DNA nanotechnology applications.
Highlights
Over the last couple of decades, DNA has proven to be much more than a vessel for genetic information
From sensing to computing to directed self-assembly, the programmable and predictable nature of DNA has unlocked numerous unforeseen nanotechnology applications.[1−4] Recently, single molecule localization techniques have exploited the rapid and transient binding of short DNA oligomers in order to achieve superresolution microscopy and optical multiplexing.[5−7] Predictive understanding of the sequence-dependent thermodynamics of DNA hybridization/dehybridization the assembly/disassembly of a DNA duplex from two single strands has underpinned the rational design of DNA oligomer sequences for nanotechnology applications, where sequence-dependent nearest-neighbor models can accurately account for mismatched pairs, dangling ends, and other non-native bonding effects.[8,9]
Quadruplexes have been studied in depth and leveraged for nanotechnology applications.[10−12] Predictive models of the dynamical, as opposed to purely thermodynamical, behaviors of DNA have become increasingly important in developing technologies such as DNA-PAINT (DNA Points Accumulation for Imaging in Nanoscale Topography), but these technologies have outpaced our fundamental understanding of the dynamics themselves.[13−15] Many experimental and computational studies have investigated DNA dynamical phenomena over picosecond to millisecond time scales.[16−20]
Summary
Over the last couple of decades, DNA has proven to be much more than a vessel for genetic information. Kinetic models have been developed for particular DNA processes such as toehold exchanges and optical barcoding[21,22] and supervised machine learning techniques have been combined with experimental measurements to predict the on/off rates as a function of sequence.[6,23,24] A comprehensive understanding of the full dynamical landscape of hybridization/dehybridization accounting for the sequence-dependent metastable states and association/dissociation pathways remains lacking and fundamental questions remain unresolved. The development of predictive models and design rules with which to engineer DNA strands with tailored hybridization/dehybridization kinetics and pathways is vital to advancing rational design of DNA strands for nanotechnology applications and is of importance in understanding fundamental biological processes such as transcription and gene regulation
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