Force Field X: A computational microscope to study genetic variation and organic crystals using theory and experiment.

Force Field X (FFX) is an open-source software package for atomic resolution modeling of genetic variants and organic crystals that leverages advanced potential energy functions and experimental data. FFX currently consists of nine modular packages with novel algorithms that include global optimization via a many-body expansion, acid-base chemistry using polarizable constant-pH molecular dynamics, estimation of free energy differences, generalized Kirkwood implicit solvent models, and many more. Applications of FFX focus on the use and development of a crystal structure prediction pipeline, biomolecular structure refinement against experimental datasets, and estimation of the thermodynamic effects of genetic variants on both proteins and nucleic acids. The use of Parallel Java and OpenMM combines to offer shared memory, message passing, and graphics processing unit parallelization for high performance simulations. Overall, the FFX platform serves as a computational microscope to study systems ranging from organic crystals to solvated biomolecular systems.

Recent advances in gene delivery nanoplatforms based on spherical nucleic acids.

Gene therapy is a therapeutic option for mitigating diseases that do not respond well to pharmacological therapy. This type of therapy allows for correcting altered and defective genes by transferring nucleic acids to target cells. Notably, achieving a desirable outcome is possible by successfully delivering genetic materials into the cell. In-vivo gene transfer strategies use two major classes of vectors, namely viral and nonviral. Both of these systems have distinct pros and cons, and the choice of a delivery system depends on therapeutic objectives and other considerations. Safe and efficient gene transfer is the main feature of any delivery system. Spherical nucleic acids (SNAs) are nanotechnology-based gene delivery systems (i.e., non-viral vectors). They are three-dimensional structures consisting of a hollow or solid spherical core nanoparticle that is functionalized with a dense and highly organized layer of oligonucleotides. The unique structural features of SNAs confer them a high potency in internalization into various types of tissue and cells, a high stability against nucleases, and efficay in penetrating through various biological barriers (such as the skin, blood-brain barrier, and blood-tumor barrier). SNAs also show negligible toxicity and trigger minimal immune response reactions. During the last two decades, all these favorable physicochemical and biological attributes have made them attractive vehicles for drug and nucleic acid delivery. This article discusses the unique structural properties, types of SNAs, and also optimization mechanisms of SNAs. We also focus on recent advances in the synthesis of gene delivery nanoplatforms based on the SNAs.

Nucleic acid mediated activation of a short prokaryotic Argonaute immune system.

A short prokaryotic Argonaute (pAgo) TIR-APAZ (SPARTA) defense system, activated by invading DNA to unleash its TIR domain for NAD(P)+ hydrolysis, was recently identified in bacteria. We report the crystal structure of SPARTA heterodimer in the absence of guide-RNA/target-ssDNA (2.66 Å) and a cryo-EM structure of the SPARTA oligomer (tetramer of heterodimers) bound to guide-RNA/target-ssDNA at nominal 3.15-3.35 Å resolution. The crystal structure provides a high-resolution view of SPARTA, revealing the APAZ domain as equivalent to the N, L1, and L2 regions of long pAgos and the MID domain containing a unique insertion (insert57). Cryo-EM structure reveals regions of the PIWI (loop10-9) and APAZ (helix αN) domains that reconfigure for nucleic-acid binding and decrypts regions/residues that reorganize to expose a positively charged pocket for higher-order assembly. The TIR domains amass in a parallel-strands arrangement for catalysis. We visualize SPARTA before and after RNA/ssDNA binding and uncover the basis of its active assembly leading to abortive infection.

SOFB is a comprehensive ensemble deep learning approach for elucidating and characterizing protein-nucleic-acid-binding residues.

Proteins and nucleic-acids are essential components of living organisms that interact in critical cellular processes. Accurate prediction of nucleic acid-binding residues in proteins can contribute to a better understanding of protein function. However, the discrepancy between protein sequence information and obtained structural and functional data renders most current computational models ineffective. Therefore, it is vital to design computational models based on protein sequence information to identify nucleic acid binding sites in proteins. Here, we implement an ensemble deep learning model-based nucleic-acid-binding residues on proteins identification method, called SOFB, which characterizes protein sequences by learning the semantics of biological dynamics contexts, and then develop an ensemble deep learning-based sequence network to learn feature representation and classification by explicitly modeling dynamic semantic information. Among them, the language learning model, which is constructed from natural language to biological language, captures the underlying relationships of protein sequences, and the ensemble deep learning-based sequence network consisting of different convolutional layers together with Bi-LSTM refines various features for optimal performance. Meanwhile, to address the imbalanced issue, we adopt ensemble learning to train multiple models and then incorporate them. Our experimental results on several DNA/RNA nucleic-acid-binding residue datasets demonstrate that our proposed model outperforms other state-of-the-art methods. In addition, we conduct an interpretability analysis of the identified nucleic acid binding residue sequences based on the attention weights of the language learning model, revealing novel insights into the dynamic semantic information that supports the identified nucleic acid binding residues. SOFB is available at https://github.com/Encryptional/SOFB and https://figshare.com/articles/online_resource/SOFB_figshare_rar/25499452 .

Detecting 2'-5'-adenosine linked nucleic acids via acylation of secondary hydroxy functionality.

2'-5'-Adenosine linked nucleic acids are crucial components in living cells that play significant roles, including participating in antiviral defense mechanisms by facilitating the breakdown of viral genetic material. In this report, we present a chemical derivatization method employing 5-fluoro-2-pyridinoyl-imidazole as the acylation agent, a strategy that can be effectively combined with advanced analytical tools, including Nuclear Magnetic Resonance spectroscopy and Liquid Chromatography-Mass Spectrometry, to enhance the characterization and detection capabilities. This marks the first instance of a simple method designed to detect 2'-5'-adenosine linked nucleic acids. The new method is characterized by its time-saving nature, simplicity, and relative accuracy compared to previous methods.

High-content tailoring strategy to improve the multifunctionality of functional nucleic acids.

Functional nucleic acids (FNAs) have attracted increasing attention in recent years due to their diverse physiological functions. The understanding of their conformational recognition mechanisms has advanced through nucleic acid tailoring strategies and sequence optimization. With the development of the FNA tailoring techniques, they have become a methodological guide for nucleic acid repurposing. Therefore, it is necessary to systematize the relationship between FNA tailoring strategies and the development of nucleic acid multifunctionality. This review systematically categorizes eight types of FNA multifunctionality, and introduces the traditional FNA tailoring strategy from five aspects, including deletion, substitution, splitting, fusion and elongation. Based on the current state of FNA modification, a new generation of FNA tailoring strategy, called the high-content tailoring strategy, was unprecedentedly proposed to improve FNA multifunctionality. In addition, the multiple applications of rational tailoring-driven FNA performance enhancement in various fields were comprehensively summarized. The limitations and potential of FNA tailoring and repurposing in the future are also explored in this review. In summary, this review introduces a novel tailoring theory, systematically summarizes eight FNA performance enhancements, and provides a systematic overview of tailoring applications across all categories of FNAs. The high-content tailoring strategy is expected to expand the application scenarios of FNAs in biosensing, biomedicine and materials science, thus promoting the synergistic development of various fields.

Split T7 switch-mediated cell-free protein synthesis system for detecting target nucleic acids.

Cell-free protein synthesis (CFPS) reactions can be used to detect nucleic acids. However, most CFPS systems rely on a toehold switch and exhibit the following critical limitations: (i) off-target signals due to leaky translation in the absence of target nucleic acids, (ii) a suboptimal detection limit of approximately 30 nM without pre-amplification, and (iii) labor-intensive screening processes due to sequence constraints for the target nucleic acids. To overcome these shortcomings, we developed a new split T7 switch-mediated CFPS system in which the split T7 promoter was applied to a three-way junction structure to selectively initiate transcription-translation only in the presence of target nucleic acids. Both fluorescence and colorimetric detection systems were constructed by employing different reporter proteins. Notably, we introduced the self-complementation of split fluorescent proteins to streamline preparation of the proposed system, enabling versatile applications. Operation of this one-pot approach under isothermal conditions enabled the detection of target nucleic acids at concentrations as low as 10 pM, representing more than a thousand times improvement over previous toehold switch-based approaches. Furthermore, the proposed system demonstrated high specificity in detecting target nucleic acids and compatibility with various reporter proteins encoded in the expression region. By eliminating issues associated with the previous toehold switch system, our split T7 switch-mediated CFPS system could become a core platform for detecting various target nucleic acids.

DNA Anchoring Strength Directly Correlates with Spherical Nucleic Acid-Based HPV E7 Cancer Vaccine Potency.

Vaccination for cancers arising from human papillomavirus (HPV) infection holds immense potential, yet clinical success has been elusive. Herein, we describe vaccination studies involving spherical nucleic acids (SNAs) incorporating a CpG adjuvant and a peptide antigen (E711-19) from the HPV-E7 oncoprotein. Administering the vaccine to humanized mice induced immunity-dependent on the oligonucleotide anchor chemistry (cholesterol vs (C12)9). SNAs containing a (C12)9-anchor enhanced IFN-γ production >200-fold, doubled memory CD8+ T-cell formation, and delivered more than twice the amount of oligonucleotide to lymph nodes in vivo compared to a simple admixture. Importantly, the analogous construct with a weaker cholesterol anchor performed similar to admix. Moreover, (C12)9-SNAs activated 50% more dendritic cells and generated T-cells cytotoxic toward an HPV+ cancer cell line, UM-SCC-104, with near 2-fold greater efficiency. These observations highlight the pivotal role of structural design, and specifically oligonucleotide anchoring strength (which correlates with overall construct stability), in developing efficacious therapeutic vaccines.

Molecular Docking Approach for Biological Interaction of Green Synthesized Nanoparticles.

In recent years, significant progress has been made in the subject of nanotechnology, with a range of methods developed to synthesize precise-sized and shaped nanoparticles according to particular requirements. Often, the nanoparticles are created by employing dangerous reducing chemicals to reduce metal ions into uncharged nanoparticles. Green synthesis or biological approaches have been used recently to circumvent this issue because biological techniques are simple, inexpensive, safe, clean, and extremely productive. Nowadays, much research is being conducted on how different kinds of nanoparticles connect to proteins and nucleic acids using molecular docking models. Therefore, this review discusses the most recent advancements in molecular docking capacity to predict the interactions between various nanoparticles (NPs), such as ZnO, CuO, Ag, Au, and Fe3O4, and biological macromolecules.

Evaluating Cationic Nanoparticles as Carriers of Antiviral Nucleic Acids.

Nanoparticle carriers enable the multivalent delivery of nucleic acids to cells and protect them from degradation. In this chapter, we present a comprehensive overview of four methodologies: electrophoretic mobility shift assay (EMSA), alamarBlue/CFDA-AM cell viability dyes, fluorescence microscopy, and antiviral assays, which collectively are tools to explore interactions between nucleic acids and nanoparticles, and their biological efficacy. These assays provide insights into binding potential, cytotoxicity, and antiviral efficacy of nucleic acid-based nanoparticle treatments furthering the development of effective antiviral therapeutics.

Probing of nucleic acid compaction using low-frequency Raman spectroscopy.

Compaction of nucleic acids, namely DNA and RNA, determines their functions and involvement in vital cell processes including transcription, replication, DNA repair and translation. However, experimental probing of the compaction of nucleic acids is not straightforward. In this study, we suggest an approach for this probing using low-frequency Raman spectroscopy. Specifically, we show theoretically, computationally and experimentally the quantifiable correlation between the low-frequency Raman intensity from nucleic acids, magnitude of thermal fluctuations of atomic positions, and the compaction state of biomolecules. Noteworthily, we highlight that the LF Raman intensity differs by an order of magnitude for different samples of DNA, and even for the same sample in the course of long-term storage. The feasibility of the approach is further shown by assessment of the DNA compaction in the nuclei of plant cells. We anticipate that the suggested approach will enlighten compaction of nucleic acids and their dynamics during the key processes of the cell life cycle and under various factors, facilitating advancement of molecular biology and medicine.

Lipoplexes' Structure, Preparation, and Role in Managing Different Diseases.

Lipid-based vectors are becoming promising alternatives to traditional therapies over the last 2 decades specially for managing life-threatening diseases like cancer. Cationic lipids are the most prevalent non-viral vectors utilized in gene delivery. The increasing number of clinical trials about lipoplex-based gene therapy demonstrates their potential as well-established technology that can provide robust gene transfection. In this regard, this review will summarize this important point. These vectors however have a modest transfection efficiency. This limitation can be partly addressed by using functional lipids that provide a plethora of options for investigating nucleic acid-lipid interactions as well as in vitro and in vivo nucleic acid delivery for biomedical applications. Despite their lower gene transfer efficiency, lipid-based vectors such as lipoplexes have several advantages over viral ones: they are less toxic and immunogenic, can be targeted, and are simple to produce on a large scale. Researchers are actively investigating the parameters that are essential for an effective lipoplex delivery method. These include factors that influence the structure, stability, internalization, and transfection of the lipoplex. Thorough understanding of the design principles will enable synthesis of customized lipoplex formulations for life-saving therapy.

Integrating Porous Silicon Nanoneedles within Medical Devices for Nucleic Acid Nanoinjection.

Porous silicon nanoneedles can interface with cells and tissues with minimal perturbation for high-throughput intracellular delivery and biosensing. Typically, nanoneedle devices are rigid, flat, and opaque, which limits their use for topical applications in the clinic. We have developed a robust, rapid, and precise substrate transfer approach to incorporate nanoneedles within diverse substrates of arbitrary composition, flexibility, curvature, transparency, and biodegradability. With this approach, we integrated nanoneedles on medically relevant elastomers, hydrogels, plastics, medical bandages, catheter tubes, and contact lenses. The integration retains the mechanical properties and transfection efficiency of the nanoneedles. Transparent devices enable the live monitoring of cell-nanoneedle interactions. Flexible devices interface with tissues for efficient, uniform, and sustained topical delivery of nucleic acids ex vivo and in vivo. The versatility of this approach highlights the opportunity to integrate nanoneedles within existing medical devices to develop advanced platforms for topical delivery and biosensing.

[New drug discovery and prospect of small nucleic acids derived from traditional Chinese medicine and natural medicine].

Small nucleic acid drugs mainly include small interfering RNA(siRNA), antisense oligonucleotide(ASO), microRNA(miRNA), messenger RNA(mRNA), nucleic acid aptamer(aptamer), and so on. Its translation or regulation can be inhibited by binding to the RNA of the target molecule. Due to its strong specificity, persistence, and curability, small nucleic acid drugs have received considerable attention in recent years. Recent studies have shown that some miRNAs from animal and plant sources can stably exist in the blood, tissue, and organs of animals and human beings and exert pharmacological action by regulating the expression of various target proteins. This paper summarized the discovery of small nucleic acids derived from traditional Chinese medicine(TCM) and natural drugs and their cross-border regulatory mechanisms and discussed the technical challenges and regulatory issues brought by this new drug, which can provide new ideas and methods for explaining the complex mechanism of TCM, developing new drugs of small nucleic acids from TCM and natural medicine, and conducting regulatory scientific research.

Functionalizing tetrahedral framework nucleic acids-based nanostructures for tumor in situ imaging and treatment.

Timely in situ imaging and effective treatment are efficient strategies in improving the therapeutic effect and survival rate of tumor patients. In recent years, there has been rapid progress in the development of DNA nanomaterials for tumor in situ imaging and treatment, due to their unsurpassed structural stability, excellent material editability, excellent biocompatibility and individual endocytic pathway. Tetrahedral framework nucleic acids (tFNAs), are a typical example of DNA nanostructures demonstrating superior stability, biocompatibility, cell-entry performance, and flexible drug-loading ability. tFNAs have been shown to be effective in achieving timely tumor in situ imaging and precise treatment. Therefore, the progress in the fabrication, characterization, modification and cellular internalization pathway of tFNAs-based functional systems and their potential in tumor in situ imaging and treatment applications were systematically reviewed in this article. In addition, challenges and future prospects of tFNAs in tumor in situ imaging and treatment as well as potential clinical applications were discussed.

RmsdXNA: RMSD prediction of nucleic acid-ligand docking poses using machine-learning method.

Small molecule drugs can be used to target nucleic acids (NA) to regulate biological processes. Computational modeling methods, such as molecular docking or scoring functions, are commonly employed to facilitate drug design. However, the accuracy of the scoring function in predicting the closest-to-native docking pose is often suboptimal. To overcome this problem, a machine learning model, RmsdXNA, was developed to predict the root-mean-square-deviation (RMSD) of ligand docking poses in NA complexes. The versatility of RmsdXNA has been demonstrated by its successful application to various complexes involving different types of NA receptors and ligands, including metal complexes and short peptides. The predicted RMSD by RmsdXNA was strongly correlated with the actual RMSD of the docked poses. RmsdXNA also outperformed the rDock scoring function in ranking and identifying closest-to-native docking poses across different structural groups and on the testing dataset. Using experimental validated results conducted on polyadenylated nuclear element for nuclear expression triplex, RmsdXNA demonstrated better screening power for the RNA-small molecule complex compared to rDock. Molecular dynamics simulations were subsequently employed to validate the binding of top-scoring ligand candidates selected by RmsdXNA and rDock on MALAT1. The results showed that RmsdXNA has a higher success rate in identifying promising ligands that can bind well to the receptor. The development of an accurate docking score for a NA-ligand complex can aid in drug discovery and development advancements. The code to use RmsdXNA is available at the GitHub repository https://github.com/laiheng001/RmsdXNA.

Plasmonic Imaging of Multivalent NTD-Nucleic Acid Interactions for Broad-Spectrum Antiviral Drug Analysis.

The discovery and identification of broad-spectrum antiviral drugs are of great significance for blocking the spread of pathogenic viruses and corresponding variants of concern. Herein, we proposed a plasmonic imaging-based strategy for assessing the efficacy of potential broad-spectrum antiviral drugs targeting the N-terminal domain of a nucleocapsid protein (NTD) and nucleic acid (NA) interactions. With NTD and NA conjugated gold nanoparticles as core and satellite nanoprobes, respectively, we found that the multivalent binding interactions could drive the formation of core-satellite nanostructures with enhanced scattering brightness due to the plasmonic coupling effect. The core-satellite assembly can be suppressed in the presence of antiviral drugs targeting the NTD-NA interactions, allowing the drug efficacy analysis by detecting the dose-dependent changes in the scattering brightness by plasmonic imaging. By quantifying the changes in the scattering brightness of plasmonic nanoprobes, we uncovered that the constructed multivalent weak interactions displayed a 500-fold enhancement in affinity as compared with the monovalent NTD-NA interactions. We demonstrated the plasmonic imaging-based strategy for evaluating the efficacy of a potential broad-spectrum drug, PJ34, that can target the NTD-NA interactions, with the IC50 as 24.35 and 14.64 µM for SARS-CoV-2 and SARS-CoV, respectively. Moreover, we discovered that ceftazidime holds the potential as a candidate drug to inhibit the NTD-NA interactions with an IC50 of 22.08 µM from molecular docking and plasmonic imaging-based drug analysis. Finally, we validated that the potential antiviral drug, 5-benzyloxygramine, which can induce the abnormal dimerization of nucleocapsid proteins, is effective for SARS-CoV-2, but not effective against SARS-CoV. All these demonstrations indicated that the plasmonic imaging-based strategy is robust and can be used as a powerful strategy for the discovery and identification of broad-spectrum drugs targeting the evolutionarily conserved viral proteins.

NETosis-Inspired Cell Surface-Constrained Framework Nucleic Acids Traps (FNATs) for Cascaded Extracellular Recognition and Cellular Behavior Modulation.

Upon pathogenic stimulation, activated neutrophils release nuclear DNA into the extracellular environment, forming web-like DNA structures known as neutrophil extracellular traps (NETs), which capture and kill bacteria, fungi, and cancer cells. This phenomenon is commonly referred to as NETosis. Inspired by this, we introduce a cell surface-constrained web-like framework nucleic acids traps (FNATs) with programmable extracellular recognition capability and cellular behavior modulation. This approach facilitates dynamic key chemical signaling molecule recognition such as adenosine triphosphate (ATP), which is elevated in the extracellular microenvironment, and triggers FNA self-assembly. This, in turn, leads to in situ tightly interwoven FNAs formation on the cell surface, thereby inhibiting target cell migration. Furthermore, it activates a photosensitizer-capturing switch, chlorin e6 (Ce6), and induces cell self-destruction. This cascade platform provides new potential tools for visualizing dynamic extracellular activities and manipulating cellular behaviors using programmable in situ self-assembling DNA molecular devices.

A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.

Proteins interact with diverse ligands to perform a large number of biological functions, such as gene expression and signal transduction. Accurate identification of these protein-ligand interactions is crucial to the understanding of molecular mechanisms and the development of new drugs. However, traditional biological experiments are time-consuming and expensive. With the development of high-throughput technologies, an increasing amount of protein data is available. In the past decades, many computational methods have been developed to predict protein-ligand interactions. Here, we review a comprehensive set of over 160 protein-ligand interaction predictors, which cover protein-protein, protein-nucleic acid, protein-peptide and protein-other ligands (nucleotide, heme, ion) interactions. We have carried out a comprehensive analysis of the above four types of predictors from several significant perspectives, including their inputs, feature profiles, models, availability, etc. The current methods primarily rely on protein sequences, especially utilizing evolutionary information. The significant improvement in predictions is attributed to deep learning methods. Additionally, sequence-based pretrained models and structure-based approaches are emerging as new trends.

Accurate structure prediction of biomolecular interactions with AlphaFold 3.

The introduction of AlphaFold 21 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design2-6. Here we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. The new AlphaFold model demonstrates substantially improved accuracy over many previous specialized tools: far greater accuracy for protein-ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein-nucleic acid interactions compared with nucleic-acid-specific predictors and substantially higher antibody-antigen prediction accuracy compared with AlphaFold-Multimer v.2.37,8. Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework.