Energy Landscapes and Hybridization Pathways for DNA Hexamer Duplexes

Abstract

Strand hybridization is not only a fundamental molecular mechanism underlying the biological functions of nucleic acids, but is also a key step in the design of efficient nanodevices. Despite recent efforts, the microscopic rules governing the hybridization mechanisms remain largely unknown. In this study, we exploit the energy landscape framework, to assess how sequence-specificity modulates the hybridization mechanisms in DNA. We find that GG-tracts hybridize much more rapidly compared to GC-tracts, via either zippering or slithering pathways. For the hybridization of GG-tracts, both zippering and slithering mechanisms appear to be kinetically relevant. In contrast, for the GC-tracts, the zippering mechanism is dominant. Our work reveals that even for the relatively small systems considered, the energy landscapes feature multiple metastable states and kinetic traps, which is at odds with the conventional 'all-or-none' model of DNA hybridization formulated on the basis of thermodynamic arguments alone. Inter

Insights into the Structure and Energy of DNA Nanoassemblies

“…This simplification greatly helps in the analysis and interpretation of experimental data but is inadequate to explain the strong deviations from ideality that have been observed in more complex systems. For example, as recently reported by Wales and coauthors [17], a six-nucleotide-long sequence might present a level of complexity that cannot be easily recapitulated by a two-state model and requires the introduction of sequence-specific parameters, such as modulators of DNA-hybridization mechanisms [18]. The general picture is that of a global downhill process that starts with the formation of a critical nucleation seed of few base pairs and proceeds with the propagation of the remaining base pairing interactions according to a zippering or slithering mechanism [19].…”

“…This simplification greatly helps in the analysis and interpretation of experimental data but is inadequate to explain the strong deviations from ideality that have been observed in more complex systems. For example, as recently reported by Wales and coauthors [17], a six-nucleotide-long sequence might present a level of complexity that cannot be easily recapitulated by a two-state model and requires The DNA molecule in its most common B-form is a right-handed helical structure, which is 2-nm-wide and rises ca. 3.4 nm every helical turn (corresponding to ca.…”

“…The probability of visiting those metastable states, i.e., the kinetic route travelled by the system, is strongly dictated by the sequence content and length of the interacting strands and by the experimental conditions used. For example, dissociation rate coefficients may span more than eight orders of magnitude when going from a 10-mer to a 20-mer [20], whereas GG tracts have been found to hybridize much more rapidly than GC tracts of the same length [17]. This fact indicates that, in addition to the total GC content, the order of nucleobases along the sequence (i.e., the entropic contribution to the system) may greatly affect the thermodynamic stability of competing conformational ensembles and, thus, determine the preferred hybridization pathway.…”

The Energy Landscape Perspective: Encoding Structure and Function for Biomolecules

“…However, the structural plasticity is even more pronounced in nucleic acids (Tinoco and Bustamante, 1999;Li et al, 2008;Solomatin et al, 2010), and represents a key challenge in the description of RNA structures (Schroeder, 2018). Nonetheless, the explicit exploration of energy landscapes for nucleic acids is possible, and provides important insight into the structural variation compatible with a given sequence (Cragnolini et al, 2017;Xiao et al, 2019;Röder et al, 2020).…”

The DNA conformational energy landscape: sequence-dependent conformational equilibria of duplex DNA

“…There have been several attempts to explore the conformation and energetics of DNA, using a variety of both backbone torsion angles and helicoidal parameters as effective degrees of freedom [1][2][3][4][5][6]. Several approximations were made to overcome the problem of dealing with a large number of degrees of freedom.…”