Bottom-Up Coarse-Grained Modeling of DNA

Kinetics and dynamics of oligonucleotide hybridization

The hybridization of short nucleic acid strands is a remarkable spontaneous process that is foundational to biotechnology and nanotechnology and plays a crucial role in gene expression, editing and DNA repair. Decades of research into the mechanism of hybridization have resulted in a deep understanding of its thermodynamics, but many questions remain regarding its kinetics and dynamics. Recent advances in experiments and molecular dynamics simulations of nucleic acids are enabling more direct insight into the structural dynamics of hybridization, which can test long-standing assumptions regarding its mechanism. In this Review, we summarize the current state of knowledge of hybridization kinetics, discuss the barriers to a molecular description of hybridization dynamics, and highlight the new approaches that have begun uncovering the dynamics of hybridization and the duplex ensemble. The kinetics and dynamics of hybridization are highly sensitive to the composition of nucleic acids, and we emphasize recent discoveries and open questions on the role of nucleobase sequence and chemical modifications.

Toward Predictive Coarse-Grained Simulations of Biomolecular Condensates

Phase separation is a fundamental process that enables cellular organization by forming biomolecular condensates. These assemblies regulate diverse functions by creating distinct environments, influencing reaction kinetics, and facilitating processes such as genome organization, signal transduction, and RNA metabolism. Recent studies highlight the complexity of condensate properties, shaped by intrinsic molecular features and external factors such as temperature and pH. Molecular simulations serve as an effective approach to establishing a comprehensive framework for analyzing these influences, offering high-resolution insights into condensate stability, dynamics, and material properties. This review evaluates recent advancements in biomolecular condensate simulations, with a particular focus on coarse-grained 1-bead-per-amino-acid (1BPA) protein models, and emphasizes OpenABC, a tool designed to simplify and streamline condensate simulations. OpenABC supports the implementation of various coarse-grained force fields, enabling their performance evaluation. Our benchmarking identifies inconsistencies in phase behavior predictions across force fields, even though these models accurately capture single-chain statistics. This finding underscores the need for enhanced force field accuracy, achievable through enriched training data sets, many-body potentials, and advanced optimization techniques. Such refinements could significantly improve the predictive capacity of coarse-grained models, bridging molecular details with emergent condensate behaviors.

Computational Methods for Modeling Lipid-Mediated Active Pharmaceutical Ingredient Delivery

Lipid-mediated delivery of active pharmaceutical ingredients (API) opened new possibilities in advanced therapies. By encapsulating an API into a lipid nanocarrier (LNC), one can safely deliver APIs not soluble in water, those with otherwise strong adverse effects, or very fragile ones such as nucleic acids. However, for the rational design of LNCs, a detailed understanding of the composition-structure-function relationships is missing. This review presents currently available computational methods for LNC investigation, screening, and design. The state-of-the-art physics-based approaches are described, with the focus on molecular dynamics simulations in all-atom and coarse-grained resolution. Their strengths and weaknesses are discussed, highlighting the aspects necessary for obtaining reliable results in the simulations. Furthermore, a machine learning, i.e., data-based learning, approach to the design of lipid-mediated API delivery is introduced. The data produced by the experimental and theoretical approaches provide valuable insights. Processing these data can help optimize the design of LNCs for better performance. In the final section of this Review, state-of-the-art of computer simulations of LNCs are reviewed, specifically addressing the compatibility of experimental and computational insights.

Bayesian Analysis Reveals the Key to Extracting Pair Potentials from Neutron Scattering Data

Learning interaction potentials from the structure factor is frequently seen as impractical due to accuracy constraints of neutron and X-ray scattering experiments. This study reexamines this historic inverse problem using Bayesian inference and probabilistic machine learning on a Mie fluid to elucidate how measurement noise impacts the accuracy of recovered potentials. To perform reliable potential reconstruction, we recommend that scattering data must have noise smaller than 0.005 up to ∼30 Å-1 at a standard bin width 0.05 Å-1. At uncertainties below this threshold, Mie potentials can be determined within approximately ±1.3 for the repulsive exponent, ±0.068 Å for atomic size, and ±0.024 kcal/mol in well-depth with 95% confidence. These findings highlight the potential of uniting scattering and machine learning to overcome a century-old physics problem, infer local atomic forces to serve as a vital benchmark for model validation, and enhance the accuracy of molecular simulations.

Thermodynamics and Kinetics-Directed Regulation of Nucleic Acid-Based Molecular Recognition

Nucleic acid-based molecular recognition plays crucial roles in various fields like biosensing and disease diagnostics. To achieve optimal detection and analysis, it is essential to regulate the response performance of nucleic acid probes or switches to match specific application requirements by regulating thermodynamics and kinetics properties. However, the impacts of thermodynamics and kinetics theories on recognition performance are sometimes obscure and the relative conclusions are not intuitive. To promote the thorough understanding and rational utilization of thermodynamics and kinetics theories, this review focuses on the landmarks and recent advances of nucleic acid thermodynamics and kinetics and summarizes the nucleic acid thermodynamics and kinetics-based strategies for regulation of nucleic acid-based molecular recognition. This work hopes such a review can provide reference and guidance for the development and optimization of nucleic acid probes and switches in the future, as well as for advancements in other nucleic acid-related fields.

Multiscale modeling reveals the ion-mediated phase separation of nucleosome core particles

Due to the vast length scale inside the cell nucleus, multiscale models are required to understand chromatin folding, structure, and dynamics and how they regulate genomic activities such as DNA transcription, replication, and repair. We study the interactions and structure of condensed phases formed by the universal building block of chromatin, the nucleosome core particle (NCP), using bottom-up multiscale coarse-grained (CG) simulations with a model extracted from all-atom MD simulations. In the presence of the multivalent cations Mg(H2O)62+ or CoHex3+, we analyze the internal structures of the NCP aggregates and the contributions of histone tails and ions to the aggregation patterns. We then derive a "super" coarse-grained (SCG) NCP model to study the macroscopic scale phase separation of NCPs. The SCG simulations show the formation of NCP aggregates with Mg(H2O)62+ concentration-dependent densities and sizes. Variation of the CoHex3+ concentrations results in highly ordered lamellocolumnar and hexagonal columnar phases in agreement with experimental data. The results give detailed insights into nucleosome interactions and for understanding chromatin folding in the cell nucleus.

Rigorous Progress in Coarse-Graining

Low-resolution coarse-grained (CG) models provide remarkable computational and conceptual advantages for simulating soft materials. In principle, bottom-up CG models can reproduce all structural and thermodynamic properties of atomically detailed models that can be observed at the resolution of the CG model. This review discusses recent progress in developing theory and computational methods for achieving this promise. We first briefly review variational approaches for parameterizing interaction potentials and their relationship to machine learning methods. We then discuss recent approaches for simultaneously improving both the transferability and thermodynamic properties of bottom-up models by rigorously addressing the density and temperature dependence of these potentials. We also briefly discuss exciting progress in modeling high-resolution observables with low-resolution CG models. More generally, we highlight the essential role of the bottom-up framework not only for fundamentally understanding the limitations of prior CG models but also for developing robust computational methods that resolve these limitations in practice.

Coarse-grained modeling of DNA-RNA hybrids

We introduce oxNA, a new model for the simulation of DNA-RNA hybrids that is based on two previously developed coarse-grained models-oxDNA and oxRNA. The model naturally reproduces the physical properties of hybrid duplexes, including their structure, persistence length, and force-extension characteristics. By parameterizing the DNA-RNA hydrogen bonding interaction, we fit the model's thermodynamic properties to experimental data using both average-sequence and sequence-dependent parameters. To demonstrate the model's applicability, we provide three examples of its use-calculating the free energy profiles of hybrid strand displacement reactions, studying the resolution of a short R-loop, and simulating RNA-scaffolded wireframe origami.

Prediction and Control in DNA Nanotechnology

DNA nanotechnology is a rapidly developing field that uses DNA as a building material for nanoscale structures. Key to the field's development has been the ability to accurately describe the behavior of DNA nanostructures using simulations and other modeling techniques. In this Review, we present various aspects of prediction and control in DNA nanotechnology, including the various scales of molecular simulation, statistical mechanics, kinetic modeling, continuum mechanics, and other prediction methods. We also address the current uses of artificial intelligence and machine learning in DNA nanotechnology. We discuss how experiments and modeling are synergistically combined to provide control over device behavior, allowing scientists to design molecular structures and dynamic devices with confidence that they will function as intended. Finally, we identify processes and scenarios where DNA nanotechnology lacks sufficient prediction ability and suggest possible solutions to these weak areas.

Developing an Implicit Solvation Machine Learning Model for Molecular Simulations of Ionic Media

Molecular dynamics (MD) simulations of biophysical systems require accurate modeling of their native environment, i.e., aqueous ionic solution, as it critically impacts the structure and function of biomolecules. On the other hand, the models should be computationally efficient to enable simulations of large spatiotemporal scales. Here, we present the deep implicit solvation model for sodium chloride solutions that satisfies both requirements. Owing to the use of the neural network potential, the model can capture the many-body potential of mean force, while the implicit water treatment renders the model inexpensive. We demonstrate our approach first for pure ionic solutions with concentrations ranging from physiological to 2 M. We then extend the model to capture the effective ion interactions in the vicinity and far away from a DNA molecule. In both cases, the structural properties are in good agreement with all-atom MD, showcasing a general methodology for the efficient and accurate modeling of ionic media.

Extension of the iSoLF implicit-solvent coarse-grained model for multicomponent lipid bilayers

iSoLF is a coarse-grained (CG) model for lipid molecules with the implicit-solvent approximation used in molecular dynamics (MD) simulations of biological membranes. Using the original iSoLF (iSoLFv1), MD simulations of lipid bilayers consisting of either POPC or DPPC and these bilayers, including membrane proteins, can be performed. Here, we improve the original model, explicitly treating the electrostatic interactions between different lipid molecules and adding CG particle types. As a result, the available lipid types increase to 30. To parameterize the potential functions of the new model, we performed all-atom MD simulations of each lipid at three different temperatures using the CHARMM36 force field and the modified TIP3P model. Then, we parameterized both the bonded and non-bonded interactions to fit the area per lipid and the membrane thickness of each lipid bilayer by using the multistate Boltzmann Inversion method. The final model reproduces the area per lipid and the membrane thickness of each lipid bilayer at the three temperatures. We also examined the applicability of the new model, iSoLFv2, to simulate the phase behaviors of mixtures of DOPC and DPPC at different concentrations. The simulation results with iSoLFv2 are consistent with those using Dry Martini and Martini 3, although iSoLFv2 requires much fewer computations. iSoLFv2 has been implemented in the GENESIS MD software and is publicly available.

Computational Modeling of DNA 3D Structures: From Dynamics and Mechanics to Folding

DNA carries the genetic information required for the synthesis of RNA and proteins and plays an important role in many processes of biological development. Understanding the three-dimensional (3D) structures and dynamics of DNA is crucial for understanding their biological functions and guiding the development of novel materials. In this review, we discuss the recent advancements in computer methods for studying DNA 3D structures. This includes molecular dynamics simulations to analyze DNA dynamics, flexibility, and ion binding. We also explore various coarse-grained models used for DNA structure prediction or folding, along with fragment assembly methods for constructing DNA 3D structures. Furthermore, we also discuss the advantages and disadvantages of these methods and highlight their differences.

Atomistic Picture of Opening-Closing Dynamics of DNA Holliday Junction Obtained by Molecular Simulations

Holliday junction (HJ) is a noncanonical four-way DNA structure with a prominent role in DNA repair, recombination, and DNA nanotechnology. By rearranging its four arms, HJ can adopt either closed or open state. With enzymes typically recognizing only a single state, acquiring detailed knowledge of the rearrangement process is an important step toward fully understanding the biological function of HJs. Here, we carried out standard all-atom molecular dynamics (MD) simulations of the spontaneous opening-closing transitions, which revealed complex conformational transitions of HJs with an involvement of previously unconsidered "half-closed" intermediates. Detailed free-energy landscapes of the transitions were obtained by sophisticated enhanced sampling simulations. Because the force field overstabilizes the closed conformation of HJs, we developed a system-specific modification which for the first time allows the observation of spontaneous opening-closing HJ transitions in unbiased MD simulations and opens the possibilities for more accurate HJ computational studies of biological processes and nanomaterials.

Computer Aided Development of Nucleic Acid Applications in Nanotechnologies

Utilization of nucleic acids (NAs) in nanotechnologies and nanotechnology-related applications is a growing field with broad application potential, ranging from biosensing up to targeted cell delivery. Computer simulations are useful techniques that can aid design and speed up development in this field. This review focuses on computer simulations of hybrid nanomaterials composed of NAs and other components. Current state-of-the-art molecular dynamics simulations, empirical force fields (FFs), and coarse-grained approaches for the description of deoxyribonucleic acid and ribonucleic acid are critically discussed. Challenges in combining biomacromolecular and nanomaterial FFs are emphasized. Recent applications of simulations for modeling NAs and their interactions with nano- and biomaterials are overviewed in the fields of sensing applications, targeted delivery, and NA templated materials. Future perspectives of development are also highlighted.

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.

Single-molecule biophysics experiments in silico: Toward a physical model of a replisome

The interpretation of single-molecule experiments is frequently aided by computational modeling of biomolecular dynamics. The growth of computing power and ongoing validation of computational models suggest that it soon may be possible to replace some experiments outright with computational mimics. Here, we offer a blueprint for performing single-molecule studies in silico using a DNA-binding protein as a test bed. We demonstrate how atomistic simulations, typically limited to sub-millisecond durations and zeptoliter volumes, can guide development of a coarse-grained model for use in simulations that mimic single-molecule experiments. We apply the model to recapitulate, in silico, force-extension characterization of protein binding to single-stranded DNA and protein and DNA replacement assays, providing a detailed portrait of the underlying mechanics. Finally, we use the model to simulate the trombone loop of a replication fork, a large complex of proteins and DNA.

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Coarse-grained model derived from all-atom simulation recapitulates experiments

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Model reproduces the elastic response to force and exchange dynamics

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Model reveals structure of intermediate states usually inaccessible to experiment

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Model applied to viral replisome with trombone loop containing tens of SSB proteins

Predicting the configuration and energy of DNA in a nucleosome by coarse-grain modelling

We present a novel algorithm which uses a coarse-grained model and an energy minimisation procedure to predict the sequence-dependent DNA configuration in a nucleosome together with its energetic cost.

Theory and Practice of Coarse-Grained Molecular Dynamics of Biologically Important Systems

Molecular dynamics with coarse-grained models is nowadays extensively used to simulate biomolecular systems at large time and size scales, compared to those accessible to all-atom molecular dynamics. In this review article, we describe the physical basis of coarse-grained molecular dynamics, the coarse-grained force fields, the equations of motion and the respective numerical integration algorithms, and selected practical applications of coarse-grained molecular dynamics. We demonstrate that the motion of coarse-grained sites is governed by the potential of mean force and the friction and stochastic forces, resulting from integrating out the secondary degrees of freedom. Consequently, Langevin dynamics is a natural means of describing the motion of a system at the coarse-grained level and the potential of mean force is the physical basis of the coarse-grained force fields. Moreover, the choice of coarse-grained variables and the fact that coarse-grained sites often do not have spherical symmetry implies a non-diagonal inertia tensor. We describe selected coarse-grained models used in molecular dynamics simulations, including the most popular MARTINI model developed by Marrink's group and the UNICORN model of biological macromolecules developed in our laboratory. We conclude by discussing examples of the application of coarse-grained molecular dynamics to study biologically important processes.

A journey through mapping space: characterising the statistical and metric properties of reduced representations of macromolecules

ABSTRACT

A mapping of a macromolecule is a prescription to construct a simplified representation of the system in which only a subset of its constituent atoms is retained. As the specific choice of the mapping affects the analysis of all-atom simulations as well as the construction of coarse-grained models, the characterisation of the mapping space has recently attracted increasing attention. We here introduce a notion of scalar product and distance between reduced representations, which allows the study of the metric and topological properties of their space in a quantitative manner. Making use of a Wang-Landau enhanced sampling algorithm, we exhaustively explore such space, and examine the qualitative features of mappings in terms of their squared norm. A one-to-one correspondence with an interacting lattice gas on a finite volume leads to the emergence of discontinuous phase transitions in mapping space, which mark the boundaries between qualitatively different reduced representations of the same molecule.