Computer Aided Development of Nucleic Acid Applications in Nanotechnologies

Can We Ever Develop an Ideal RNA Force Field? Lessons Learned from Simulations of the UUCG RNA Tetraloop and Other Systems

Molecular dynamics (MD) simulations are an important and well-established tool for investigating RNA structural dynamics, but their accuracy relies heavily on the quality of the employed force field (ff). In this work, we present a comprehensive evaluation of widely used pair-additive and polarizable RNA ffs using the challenging UUCG tetraloop (TL) benchmark system. Extensive standard MD simulations, initiated from the NMR structure of the 14-mer UUCG TL, revealed that most ffs did not maintain the native state, instead favoring alternative loop conformations. Notably, three very recent variants of pair-additive ffs, OL3CP-gHBfix21, DES-Amber, and OL3R2.7, successfully preserved the native structure over a 10 × 20 μs time scale. To further assess these ffs, we performed enhanced sampling folding simulations of the shorter 8-mer UUCG TL, starting from the single-stranded conformation. Estimated folding free energies (ΔG°fold) varied significantly among these three ffs, with values of 0.0 ± 0.6, 2.4 ± 0.8, and 7.4 ± 0.2 kcal/mol for OL3CP-gHBfix21, DES-Amber, and OL3R2.7, respectively. The ΔG°fold value predicted by the OL3CP-gHBfix21 ff was closest to experimental estimates, ranging from -1.6 to -0.7 kcal/mol. In contrast, the higher ΔG°fold values obtained using DES-Amber and OL3R2.7 were unexpected, suggesting that key interactions are inaccurately described in the folded, unfolded, or misfolded ensembles. These discrepancies led us to further test DES-Amber and OL3R2.7 ffs on additional RNA and DNA systems, where further performance issues were observed. Our results emphasize the complexity of accurately modeling RNA dynamics and suggest that creating an RNA ff capable of reliably performing across a wide range of RNA systems remains extremely challenging. In conclusion, our study provides valuable insights into the capabilities of current RNA ffs and highlights key areas for future ff development.

Stretching and twisting of double-stranded RNA under forces: Unwinding mechanism and base-pair dependent elasticity

We used all-atom molecular dynamics simulations to investigate the mechanical response of double-stranded RNA (dsRNA) by applying various forces. We used the helical rise and helical twist, as well as a newly defined helical diameter, to characterize the stretching and twisting of dsRNA. The results indicate that dsRNA unwinds when stretched, accompanied by a linear increase in helical rise and helical diameter. Then, we utilized the normal modes, which are linear combinations of helical modes, to elucidate the underlying mechanism of dsRNA unwinding from an energetic perspective. On the other hand, we employed a stiffness matrix based on a rigid base pair model to examine the base-pair dependence of twist elasticity for dsRNA, as well as stretch elasticities with respect to the helical rise and helical diameter. The results show that the force induces variations in the local elasticities and their couplings of dsRNA, which are closely related to the distributions of base pairs. The mean stretch and twist elasticities can be considered as constants within the measurement uncertainties; however, their couplings demonstrate a slight linear dependency on applied force.

How Binding Site Flexibility Promotes RNA Scanning by TbRGG2 RRM: A Molecular Dynamics Simulation Study

RNA recognition motifs (RRMs) are a key class of proteins that primarily bind single-stranded RNAs. In this study, we applied standard atomistic molecular dynamics simulations to obtain insights into the intricate binding dynamics between uridine-rich RNAs and TbRGG2 RRM using the recently developed OL3-Stafix AMBER force field, which improves the description of single-stranded RNA molecules. Complementing structural experiments that unveil a primary binding mode with a single uridine bound, our simulations uncover two supplementary binding modes in which adjacent nucleotides encroach upon the binding pocket. This leads to a unique molecular mechanism through which the TbRGG2 RRM is capable of rapidly transitioning the U-rich sequence. In contrast, the presence of non-native cytidines induces stalling and destabilization of the complex. By leveraging extensive equilibrium dynamics and a large variety of binding states, TbRGG2 RRM effectively expedites diffusion along the RNA substrate while ensuring robust selectivity for U-rich sequences despite featuring a solitary binding pocket. We further substantiate our description of the complex dynamics by simulating the fully spontaneous association process of U-rich sequences to the TbRGG2 RRM. Our study highlights the critical role of dynamics and auxiliary binding states in interface dynamics employed by RNA-binding proteins, which is not readily apparent in traditional structural studies but could represent a general type of binding strategy employed by many RNA-binding proteins.

Optical Properties Prediction for Red and Near-Infrared Emitting Carbon Dots Using Machine Learning

Functional nanostructures build up a basis for the future materials and devices, providing a wide variety of functionalities, a possibility of designing bio-compatible nanoprobes, etc. However, development of new nanostructured materials via trial-and-error approach is obviously limited by laborious efforts on their syntheses, and the cost of materials and manpower. This is one of the reasons for an increasing interest in design and development of novel materials with required properties assisted by machine learning approaches. Here, the dataset on synthetic parameters and optical properties of one important class of light-emitting nanomaterials - carbon dots are collected, processed, and analyzed with optical transitions in the red and near-infrared spectral ranges. A model for prediction of spectral characteristics of these carbon dots based on multiple linear regression is established and verified by comparison of the predicted and experimentally observed optical properties of carbon dots synthesized in three different laboratories. Based on the analysis, the open-source code is provided to be used by researchers for the prediction of optical properties of carbon dots and their synthetic procedures.

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.

Decoding Biomechanical Cues Based on DNA Sensors

Biological systems perceive and respond to mechanical forces, generating mechanical cues to regulate life processes. Analyzing biomechanical forces has profound significance for understanding biological functions. Therefore, a series of molecular mechanical techniques have been developed, mainly including single-molecule force spectroscopy, traction force microscopy, and molecular tension sensor systems, which provide indispensable tools for advancing the field of mechanobiology. DNA molecules with a programmable structure and well-defined mechanical characteristics have attached much attention to molecular tension sensors as sensing elements, and are designed for the study of biomechanical forces to present biomechanical information with high sensitivity and resolution. In this work, a comprehensive overview of molecular mechanical technology is presented, with a particular focus on molecular tension sensor systems, specifically those based on DNA. Finally, the future development and challenges of DNA-based molecular tension sensor systems are looked upon.

Click and Detect: Versatile Ampicillin Aptasensor Enabled by Click Chemistry on a Graphene-Alkyne Derivative

Tackling the current problem of antimicrobial resistance (AMR) requires fast, inexpensive, and effective methods for controlling and detecting antibiotics in diverse samples at the point of interest. Cost-effective, disposable, point-of-care electrochemical biosensors are a particularly attractive option. However, there is a need for conductive and versatile carbon-based materials and inks that enable effective bioconjugation under mild conditions for the development of robust, sensitive, and selective devices. This work describes a simple and fast methodology to construct an aptasensor based on a novel graphene derivative equipped with alkyne groups prepared via fluorographene chemistry. Using click chemistry, an aptamer is immobilized and used as a successful platform for the selective determination of ampicillin in real samples in the presence of interfering molecules. The electrochemical aptasensor displayed a detection limit of 1.36 nM, high selectivity among other antibiotics, the storage stability of 4 weeks, and is effective in real samples. Additionally, structural and docking simulations of the aptamer shed light on the ampicillin binding mechanism. The versatility of this platform opens up wide possibilities for constructing a new class of aptasensor based on disposable screen-printed carbon electrodes usable in point-of-care devices.