Thermodynamics of DNA Hybridization from Atomistic Simulations

Optimizing On-the-Fly Probability Enhanced Sampling for Complex RNA Systems: Sampling Free Energy Surfaces of an H-Type Pseudoknot

All-atom molecular dynamics (MD) simulations offer crucial insights into biomolecular dynamics, but inherent time scale constraints often limit their effectiveness. Advanced sampling techniques help overcome these limitations, enabling predictions of deeply rugged folding free energy surfaces (FES) of RNA at atomistic resolution. The Multithermal-Multiumbrella On-the-Fly Probability Enhanced Sampling (MM-OPES) method, which combines temperature and collective variables (CVs) to accelerate sampling, has shown promise and cost-effectiveness. However, the applications have so far been limited to simpler RNA systems, such as stem-loops. In this study, we optimized the MM-OPES method to explore the FES of an H-type RNA pseudoknot, a more complex fundamental RNA folding unit. Through systematic exploration of CV combinations and temperature ranges, we identified an optimal strategy for both sampling and analysis. Our findings demonstrate that treating the native-like contacts in two stems as independent CVs and using a temperature range of 300-480 K provides the most effective sampling, while projections onto native Watson-Crick-type hydrogen bond CVs yield the best resolution FES prediction. Additionally, our sampling scheme also revealed various folding/unfolding pathways. This study provides practical insights and detailed decision-making strategies for adopting the MM-OPES method, facilitating its application to complex RNA structures at atomistic resolution.

Thermodynamic Parameter Estimation for Modified Oligonucleotides Using Molecular Dynamics Simulations

This study investigates the thermodynamic parameters of 1300 RNA/DNA hybrid duplexes, including both natural and chemically modified forms, using molecular dynamics (MD) simulations. Modified duplexes consist of phosphorothioate (PS) backbones and 2'-O-methoxyethyl (MOE) modifications, both commonly used in therapeutic oligonucleotides. Hybridization enthalpy and entropy were calculated from MD trajectories using molecular mechanics Poisson-Boltzmann surface area (MMPBSA) and molecular mechanics generalized Born surface area (MMGBSA) approaches. To address discrepancies with experimental data, we established empirical relationships by comparing calculated values with known experimental results of natural hybrid duplexes, then extended these relationships to the entire data set. The corrected parameters were subsequently used to generate nearest-neighbor (NN) models, allowing for experimentally reliable melting temperature predictions. In this process, MMGBSA demonstrated superior predictive performance with high convergence and consistency for both natural and modified duplexes. Specifically, MMGBSA captured the stabilizing effects of the MOE modification with minimal bias, while MMPBSA exhibited greater variability and limited reliability. These findings highlight the potential of MMGBSA for accurate thermodynamic modeling of both natural and modified nucleic acids, providing a robust framework and experimentally meaningful insights for applications in nucleic acid-based therapeutic design and biotechnology.

A Deep Learning-Driven Sampling Technique to Explore the Phase Space of an RNA Stem-Loop

The folding and unfolding of RNA stem-loops are critical biological processes; however, their computational studies are often hampered by the ruggedness of their folding landscape, necessitating long simulation times at the atomistic scale. Here, we adapted DeepDriveMD (DDMD), an advanced deep learning-driven sampling technique originally developed for protein folding, to address the challenges of RNA stem-loop folding. Although tempering- and order parameter-based techniques are commonly used for similar rare-event problems, the computational costs or the need for a priori knowledge about the system often present a challenge in their effective use. DDMD overcomes these challenges by adaptively learning from an ensemble of running MD simulations using generic contact maps as the raw input. DeepDriveMD enables on-the-fly learning of a low-dimensional latent representation and guides the simulation toward the undersampled regions while optimizing the resources to explore the relevant parts of the phase space. We showed that DDMD estimates the free energy landscape of the RNA stem-loop reasonably well at room temperature. Our simulation framework runs at a constant temperature without external biasing potential, hence preserving the information on transition rates, with a computational cost much lower than that of the simulations performed with external biasing potentials. We also introduced a reweighting strategy for obtaining unbiased free energy surfaces and presented a qualitative analysis of the latent space. This analysis showed that the latent space captures the relevant slow degrees of freedom for the RNA folding problem of interest. Finally, throughout the manuscript, we outlined how different parameters are selected and optimized to adapt DDMD for this system. We believe this compendium of decision-making processes will help new users adapt this technique for the rare-event sampling problems of their interest.

The clinical potential of l-oligonucleotides: challenges and opportunities

Chemically modified nucleotides are central to the development of biostable research tools and oligonucleotide therapeutics. In this context, l-oligonucleotides, the synthetic enantiomer of native d-nucleic acids, hold great promise. As enantiomers, l-oligonucleotides share the same physical and chemical properties as their native counterparts, yet their inverted l-(deoxy)ribose sugars afford them orthogonality towards the stereospecific environment of biology. Notably, l-oligonucleotides are highly resistant to degradation by cellular nucleases, providing them with superior biostability. As a result, l-oligonucleotides are being increasingly utilized for the development of diverse biomedical technologies, including molecular imaging tools, diagnostic biosensors, and aptamer-based therapeutics. Herein, we present recent such examples that highlight the clinical potential of l-oligonucleotides. Additionally, we provide our perspective on the remaining challenges and practical considerations currently associated with the use of l-oligonucleotides and explore potential solutions that will lead to the broader adoption of l-oligonucleotides in clinical applications.

Atomistic simulations of RNA duplex thermal denaturation: Sequence- and forcefield-dependence

Double-stranded RNA is the end-product of template-based replication, and is also the functional state of some biological RNAs. Similarly to proteins and DNA, they can be denatured by temperature, with important physiological and technological implications. Here, we use an in silico strategy to probe the thermal denaturation of RNA duplexes. Following previous results that were obtained on a few different duplexes, and which nuanced the canonical 2-state picture of nucleic acid denaturation, we here specifically address three different aspects that greatly improve our description of the temperature-induced dsRNA separation. First, we investigate the effect of the spatial distribution of weak and strong base-pairs among the duplex sequence. We show that the deviations from the two-state dehybridization mechanism are more pronounced when a strong core is flanked with weak extremities, while duplexes with a weak core but strong extremities exhibit a two-state behavior, which can be explained by the key role played by base fraying. This was later verified by generating artificial hairpin or circular states containing one or two locked duplex extremities, which results in an important reinforcement of the entire HB structure of the duplex and higher melting temperatures. Finally, we demonstrate that our results are little sensitive to the employed combination of RNA and water forcefields. The trends in thermal stability among the different sequences as well as the observed unfolding mechanisms (and the deviations from a two-state scenario) remain the same regardless of the employed atomistic models. However, our study points to possible limitations of recent reparametrizations of the Amber RNA forcefield, which sometimes results in duplexes that readily denature under ambient conditions, in contradiction with available experimental results.

Controlled destabilization of caged circularized DNA oligonucleotides predicted by replica exchange molecular dynamics simulations

Spatiotemporal control is a critical issue in the design of strategies for the photoregulation of oligonucleotide activity. Efficient uncaging, i.e., activation by removal of photolabile protecting groups (PPGs), often necessitates multiple PPGs. An alternative approach is based on circularization strategies, exemplified by intrasequential circularization, also denoted photo-tethering, as introduced in [Seyfried et al., Angew. Chem., Int. Ed., 2017, 56, 359]. Here, we develop a computational protocol, relying on replica exchange molecular dynamics (REMD), in order to characterize the destabilization of a series of circularized, caged DNA oligonucleotides addressed in the aforementioned study. For these medium-sized (32 nt) oligonucleotides, melting temperatures are computed, whose trend is in good agreement with experiment, exhibiting a large destabilization and, hence, reduction of the melting temperature of the order of ΔTm ∼ 30 K as compared with the native species. The analysis of free energy landscapes confirms the destabilization pattern experienced by the circularized oligonucleotides. The present study underscores that computational protocols that capture controlled destabilization and uncaging of oligonucleotides are promising as predictive tools in the tailored photocontrol of nucleic acids.

Toward a Molecular Mechanism of Complementary RNA Duplexes Denaturation

RNA duplexes are relatively rare but play very important biological roles. As an end-product of template-based RNA replication, they also have key implications for hypothetical primitive forms of life. Unless they are specifically separated by enzymes, these duplexes denature upon a temperature increase. However, mechanistic and kinetic aspects of RNA (and DNA) duplex thermal denaturation remain unclear at the microscopic level. We propose an in silico strategy that probes the thermal denaturation of RNA duplexes and allows for an extensive conformational space exploration along a wide temperature range with atomistic precision. We show that this approach first accounts for the strong sequence and length dependence of the duplexes melting temperature, reproducing the trends seen in the experiments and predicted by nearest-neighbor models. The simulations are then instrumental at providing a molecular picture of the temperature-induced strand separation. The textbook canonical "all-or-nothing" two-state model, very much inspired by the protein folding mechanism, can be nuanced. We demonstrate that a temperature increase leads to significantly distorted but stable structures with extensive base-fraying at the extremities, and that the fully formed duplexes typically do not form around melting. The duplex separation therefore appears as much more gradual than commonly thought.

Comparison of On-the-Fly Probability Enhanced Sampling and Parallel Tempering Combined with Metadynamics for Atomistic Simulations of RNA Tetraloop Folding

Atomistic simulations with reliable models are extremely useful in providing exquisitely detailed pictures of biomolecular phenomena that are not always accessible to experiments. One such biomolecular phenomenon is RNA folding, which often requires exhaustive simulations with combined advanced sampling techniques. In this work, we employed the multithermal-multiumbrella on-the-fly probability enhanced sampling (MM-OPES) technique and compared it against combined parallel tempering and metadynamics simulations. We found that MM-OPES simulations were successful in reproducing the free energy surfaces from combined parallel tempering and metadynamics simulations. Importantly, we also investigated a wide range of temperature sets (minimum and maximum temperatures) for MM-OPES simulations in order to identify some guidelines for deciding the temperature limits for an accurate and efficient exploration of the free energy landscapes. We found that most temperature sets yielded almost the same accuracy in reproducing the free energy surface at the ambient conditions as long as (i) the maximum temperature is reasonably high, (ii) the temperature at which we run the simulation is reasonably high (in our simulations, running temperature is defined as [minimum temperature + maximum temperature]/2), and (iii) the effective sample size at the temperature of interest is statistically reasonable. In terms of the computational cost, all MM-OPES simulations were nearly 4 times less costly than the combined parallel tempering and metadynamics simulations. We concluded that the demanding combined parallel tempering and metadynamics simulations can safely be replaced with approximately 4 times less costly MM-OPES simulations (with carefully selected temperature limits) to obtain the same information.

Beneficial and detrimental effects of non-specific binding during DNA hybridization

DNA strands have to sample numerous states to find the alignment that maximizes Watson-Crick-Franklin base pairing. This process depends strongly on sequence, which affects the stability of the native duplex as well as the prevalence of non-native inter- and intramolecular helices. We present a theory that describes DNA hybridization as a three-stage process: diffusion, registry search, and zipping. We find that non-specific binding affects each of these stages in different ways. Mis-registered intermolecular binding in the registry search stage helps DNA strands sample different alignments and accelerates the hybridization rate. Non-native intramolecular structure affects all three stages by rendering portions of the molecule inert to intermolecular association, limiting mis-registered alignments to be sampled, and impeding the zipping process. Once in-register base pairs are formed, the stability of the native structure is important to hold the molecules together long enough for non-native contacts to break.

DNA as grabbers and steerers of quantum emitters

The chemically synthesizable quantum emitters such as quantum dots (QDs), fluorescent nanodiamonds (FNDs), and organic fluorescent dyes can be integrated with an easy-to-craft quantum nanophotonic device, which would be readily developed by non-lithographic solution process. As a representative example, the solution dipping or casting of such soft quantum emitters on a flat metal layer and subsequent drop-casting of plasmonic nanoparticles can afford the quantum emitter-coupled plasmonic nanocavity (referred to as a nanoparticle-on-mirror (NPoM) cavity), allowing us for exploiting various quantum mechanical behaviors of light-matter interactions such as quantum electrodynamics (QED), strong coupling (e.g., Rabi splitting), and quantum mirage. This versatile, yet effective soft quantum nanophotonics would be further benefitted from a deterministic control over the positions and orientations of each individual quantum emitter, particularly at the molecule level of resolution. In this review, we will argue that DNA nanotechnology can provide a gold vista toward this end. A collective set of exotic characteristics of DNA molecules, including Watson-Crick complementarity and helical morphology, enables reliable grabbing of quantum emitters at the on-demand position and steering of their directors at the single molecular level. More critically, the recent advances in large-scale integration of DNA origami have pushed the reliance on the distinctly well-formed single device to the regime of the ultra-scale device arrays, which is critical for promoting the practically immediate applications of such soft quantum nanophotonics.

Ab initio predictions for 3D structure and stability of single- and double-stranded DNAs in ion solutions

The three-dimensional (3D) structure and stability of DNA are essential to understand/control their biological functions and aid the development of novel materials. In this work, we present a coarse-grained (CG) model for DNA based on the RNA CG model proposed by us, to predict 3D structures and stability for both dsDNA and ssDNA from the sequence. Combined with a Monte Carlo simulated annealing algorithm and CG force fields involving the sequence-dependent base-pairing/stacking interactions and an implicit electrostatic potential, the present model successfully folds 20 dsDNAs (≤52nt) and 20 ssDNAs (≤74nt) into the corresponding native-like structures just from their sequences, with an overall mean RMSD of 3.4Å from the experimental structures. For DNAs with various lengths and sequences, the present model can make reliable predictions on stability, e.g., for 27 dsDNAs with/without bulge/internal loops and 24 ssDNAs including pseudoknot, the mean deviation of predicted melting temperatures from the corresponding experimental data is only ~2.0°C. Furthermore, the model also quantificationally predicts the effects of monovalent or divalent ions on the structure stability of ssDNAs/dsDNAs.

To determine 3D structures and quantify stability of single- (ss) and double-stranded (ds) DNAs is essential to unveil the mechanisms of their functions and to further guide the production and development of novel materials. Although many DNA models have been proposed to reproduce the basic structural, mechanical, or thermodynamic properties of dsDNAs based on the secondary structure information or preset constraints, there are very few models can be used to investigate the ssDNA folding or dsDNA assembly from the sequence. Furthermore, due to the polyanionic nature of DNAs, metal ions (e.g., Na⁺ and Mg²⁺) in solutions can play an essential role in DNA folding and dynamics. Nevertheless, ab initio predictions for DNA folding in ion solutions are still an unresolved problem. In this work, we developed a novel coarse-grained model to predict 3D structures and thermodynamic stabilities for both ssDNAs and dsDNAs in monovalent/divalent ion solutions from their sequences. As compared with the extensive experimental data and available existing models, we showed that the present model can successfully fold simple DNAs into their native-like structures, and can also accurately reproduce the effects of sequence and monovalent/divalent ions on structure stability for ssDNAs including pseudoknot and dsDNAs with/without bulge/internal loops.

A Computational Study of RNA Tetraloop Thermodynamics, Including Misfolded States

An important characteristic of RNA folding is the adoption of alternative configurations of similar stability, often referred to as misfolded configurations. These configurations are considered to compete with correctly folded configurations, although their rigorous thermodynamic and structural characterization remains elusive. Tetraloop motifs found in large ribozymes are ideal systems for an atomistically detailed computational quantification of folding free energy landscapes and the structural characterization of their constituent free energy basins, including nonnative states. In this work, we studied a group of closely related 10-mer tetraloops using a combined parallel tempering and metadynamics technique that allows a reliable sampling of the free energy landscapes, requiring only knowledge that the stem folds into a canonical A-RNA configuration. We isolated and analyzed unfolded, folded, and misfolded populations that correspond to different free energy basins. We identified a distinct misfolded state that has a stability very close to that of the correctly folded state. This misfolded state contains a predominant population that shares the same structural features across all tetraloops studied here and lacks the noncanonical A-G base pair in its loop portion. Further analysis performed with biased trajectories showed that although this competitive misfolded state is not an essential intermediate, it is visited in most of the transitions from unfolded to correctly folded states. Moreover, the tetraloops can transition from this misfolded state to the correctly folded state without requiring extensive unfolding.

Determining Sequence-Dependent DNA Oligonucleotide Hybridization and Dehybridization Mechanisms Using Coarse-Grained Molecular Simulation, Markov State Models, and Infrared Spectroscopy

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.

Coarse-Grained Simulations of DNA Reveal Angular Dependence of Sticky-End Binding

Annealing between sticky ends of DNA is an intermediate step in ligation. It can also be utilized to program specific binding sites for DNA tile and origami assembly. This reaction is generally understood as a bimolecular reaction dictated by the local concentration of the sticky ends. Its dependence on the relative orientation between the sticky ends, however, is less understood. Here we report on the interactions between DNA sticky ends using the coarse-grained oxDNA model; specifically, we consider how the orientational alignment of the double-stranded DNA (dsDNA) segments affects the time required for the sticky ends to bind, τ_b. We specify the orientation of the dsDNA segments with three parameters: θ, which measures the angle between the helical axes, and ϕ₁ and ϕ₂, which measure rotations of each strand around the helical axis. We find that the binding time depends strongly on both θ and ϕ₂: ∼20-fold change with θ and 10-fold change with ϕ₂. The binding time is the fastest when the helical axes of duplexes are pointing toward each other and the sticky ends protrude from the farthest two points. Our result is relevant for predicting hybridization efficiency of sticky ends that are rotationally restricted.