NLDock: a Fast Nucleic Acid-Ligand Docking Algorithm for Modeling RNA/DNA-Ligand Complexes

Stacking Interactions of Druglike Heterocycles with Nucleobases

Stacking interactions contribute significantly to the interaction of small molecules with RNA, and harnessing the power of these interactions will likely prove important in the development of RNA-targeting inhibitors. To this end, we present a comprehensive computational analysis of stacking interactions between a set of 54 druglike heterocycles and the natural nucleobases. We first show that heterocycle choice can tune the strength of stacking interactions with nucleobases over a large range and that heterocycles favor stacked geometries that cluster around a discrete set of stacking loci characteristic of each nucleobase. Symmetry-adapted perturbation theory results indicate that the strengths of these interactions are modulated primarily by electrostatic and dispersion effects. Based on this, we present a multivariate predictive model of the maximum strength of stacking interactions between a given heterocycle and nucleobase that depends on molecular descriptors derived from the electrostatic potential. These descriptors can be readily computed using density functional theory or predicted directly from atom connectivity (e.g., SMILES). This model is used to predict the maximum possible stacking interactions of a set of 1854 druglike heterocycles with the natural nucleobases. Finally, we show that trivial modifications of standard (fixed-charge) molecular mechanics force fields reduce errors in predicted stacking interaction energies from around 2 kcal/mol to below 1 kcal/mol, providing a pragmatic means of predicting more reliable stacking interaction energies using existing computational workflows. We also analyze the stacking interactions between ribocil and a bacterial riboswitch, showing that two of the three aromatic heterocyclic components engage in near-optimal stacking interactions with binding site nucleobases.

The prediction of RNA-small-molecule ligand binding affinity based on geometric deep learning

Small molecule-targeted RNA is an emerging technology that plays a pivotal role in drug discovery and inhibitor design, with widespread applications in disease treatment. Consequently, predicting RNA-small-molecule ligand interactions is crucial. With advancements in computer science and the availability of extensive biological data, deep learning methods have shown great promise in this area, particularly in efficiently predicting RNA-small molecule binding sites. However, few computational methods have been developed to predict RNA-small molecule binding affinities. Meanwhile, most of these approaches rely primarily on sequence or structural representations. Molecular surface information, vital for RNA and small molecule interactions, has been largely overlooked. To address these gaps, we propose a geometric deep learning method for predicting RNA-small molecule binding affinity, named RNA-ligand Surface Interaction Fingerprinting (RLASIF). In this study, we create RNA-ligand interaction fingerprints from the geometrical and chemical features present on molecular surface to characterize binding affinity. RLASIF outperformed other computational methods across ten different test sets from PDBbind NL2020. Compared to the second-best method, our approach improves performance by 10.01 %, 6.67 %, 2.01 % and 1.70 % on four evaluation metrics, indicating its effectiveness in capturing key features influencing RNA-ligand binding strength. Additionally, RLASIF holds potential for virtual screening of potential ligands for RNA and predicting small molecule binding nucleotides within RNA structures.

DRLiPS: a novel method for prediction of druggable RNA-small molecule binding pockets using machine learning

Ribonucleic Acid (RNA) is the central conduit for information transfer in the cell. Identifying potential RNA targets in disease conditions is a challenging task, given the vast repertoire of functional non-coding RNAs in a human cell. A potential druggable target must satisfy several criteria, including disease association, cellular accessibility, binding pockets for drug-like molecules, and minimal cross-reactivity. While several methods exist for prediction of druggable proteins, they cannot be repurposed for RNAs due to fundamental differences in their binding modality. Taking all these constraints into account, a new structure-based model, Druggable RNA-Ligand binding Pocket Selector (DRLiPS), is developed here to predict binding site-level druggability of any given RNA target. A novel strategy for sampling negative binding sites in RNA structures using three parallel approaches is demonstrated here to improve model specificity: backbone motif search, exhaustive pocket prediction, and blind docking. An external blind test dataset has also been curated to showcase the model's generalizability to both experimental and modelled apo state RNA structures. DRLiPS has achieved an F1-score of 0.70, precision of 0.61, specificity of 0.89, and recall of 0.73 on this external test dataset, outperforming two existing methods, DrugPred_RNA and RNACavityMiner. Further analysis indicates that the features selected for model-building generalize well to both apo and holo states with a backbone RMSD tolerance of 3 Å. It can also predict the effect of binding site single point mutations on druggability, which can aid in optimizing synthetic RNA aptamers for small molecule recognition. The DRLiPS model is freely accessible at https://web.iitm.ac.in/bioinfo2/DRLiPS/.

Comprehensive Evaluation of End-Point Free Energy Methods in DNA-Ligand Interaction Predictions

Deoxyribonucleic acid (DNA) serves as a repository of genetic information in cells and is a critical molecular target for various antibiotics and anticancer drugs. A profound understanding of small molecule interaction with DNA is crucial for the rational design of DNA-targeted therapies. While the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics/generalized Born surface area (MM/GBSA) approaches have been well established for predicting protein-ligand binding, their application to DNA-ligand interactions has been less explored. In this study, we systematically investigated the binding of 13 diverse small molecules to DNA, evaluating the accuracy of DNA-ligand interaction predictions across different solvation approaches, interior dielectric constants (εin), and molecular force fields. Our results demonstrate that MM/PBSA, using energy-minimized structures (the bsc1 force field and εin = 20), provides the best correlation (Rp = -0.742) with experimental binding affinities, surpassing the performance of rDock scoring functions (best Rp = -0.481). Notably, the interior dielectric constant was found to significantly impact DNA-ligand binding free energy predictions, especially for MM/PBSA. Moreover, both MM/PBSA and MM/GBSA predictions (εin = 16 or 20) exhibited superior performance in distinguishing native-like binding modes within the top-10 poses from decoys, compared to the molecular docking tools used in this study. However, the popular docking software PLANTS demonstrates notable efficacy in predicting the top-1 binding pose. Given the considerably higher computational cost of MM/PBSA, MM/GBSA rescoring with higher εin = 16 or 20 is more efficient for recognizing the native-like binding poses for DNA-ligand systems. This study presents the first detailed exploration of end-point free energy calculations in the context of DNA-ligand interactions and offers valuable insights for the application of the MM/PB(GB)SA methods in this domain.

Beclin 1 prevents ISG15-mediated cytokine storms to secure fetal hematopoiesis and survival

Proper control of inflammatory responses is essential for embryonic development, but the underlying mechanism is poorly understood. Here, we show that under physiological conditions, inactivation of ISG15, an inflammation amplifier, is associated with the interaction of Beclin 1 (Becn1), via its evolutionarily conserved domain, with STAT3 in the major fetal hematopoietic organ of mice. Conditional loss of Becn1 caused sequential dysfunction and exhaustion of fetal liver hematopoietic stem cells, leading to lethal inflammatory cell-biased hematopoiesis in the fetus. Molecularly, the absence of Becn1 resulted in the release of STAT3 from Becn1 tethering and subsequent phosphorylation and translocation to the nucleus, which in turn directly activated the transcription of ISG15 in fetal liver hematopoietic cells, coupled with increased ISGylation and production of inflammatory cytokines, whereas inactivating STAT3 reduced ISG15 transcription and inflammation but improved hematopoiesis potential, and further silencing ISG15 mitigated the above collapse in the Becn1-null hematopoietic lineage. The Becn1/STAT3/ISG15 axis remains functional in autophagy-disrupted fetal hematopoietic organs. These results suggest that Becn1, in an autophagy-independent manner, secures hematopoiesis and survival of the fetus by directly inhibiting STAT3/ISG15 activation to prevent cytokine storms. Our findings highlight a previously undocumented role of Becn1 in governing ISG15 to safeguard the fetus.

XDock: A General Docking Method for Modeling Protein-Ligand and Nucleic Acid-Ligand Interactions

Molecular docking is an essential computational tool in structure-based drug discovery and the investigation of the molecular mechanisms underlying biological processes. Despite the development of many molecular docking programs for various systems, a universal tool that can accurately dock ligands across multiple system types remains elusive. Meeting the need, we developed XDock, a versatile docking framework built for both protein-ligand and nucleic acid-ligand interactions. XDock efficiently accounts for ligand flexibility by docking multiple conformations of a ligand and flexibly refining the final binding poses. It utilizes a distance geometric method for ligand sampling and leverages our knowledge-based scoring functions for assessing protein-ligand and nucleic acid-ligand interactions. XDock has undergone extensive validations on diverse benchmarks of protein-ligand and nucleic acid-ligand complexes and was compared with six other docking methods, including DOCK 6, AutoDock Vina, PLANTS, LeDock, rDock, and RLDock. In addition, XDock is also computationally efficient and on average can dock a ligand within 1 min.

AI-integrated network for RNA complex structure and dynamic prediction

RNA complexes are essential components in many cellular processes. The functions of these complexes are linked to their tertiary structures, which are shaped by detailed interface information, such as binding sites, interface contact, and dynamic conformational changes. Network-based approaches have been widely used to analyze RNA complex structures. With their roots in the graph theory, these methods have a long history of providing insight into the static and dynamic properties of RNA molecules. These approaches have been effective in identifying functional binding sites and analyzing the dynamic behavior of RNA complexes. Recently, the advent of artificial intelligence (AI) has brought transformative changes to the field. These technologies have been increasingly applied to studying RNA complex structures, providing new avenues for understanding the complex interactions within RNA complexes. By integrating AI with traditional network analysis methods, researchers can build more accurate models of RNA complex structures, predict their dynamic behaviors, and even design RNA-based inhibitors. In this review, we introduce the integration of network-based methodologies with AI techniques to enhance the understanding of RNA complex structures. We examine how these advanced computational tools can be used to model and analyze the detailed interface information and dynamic behaviors of RNA molecules. Additionally, we explore the potential future directions of how AI-integrated networks can aid in the modeling and analyzing RNA complex structures.

Rationally Designed G-Quadruplex Selective "Turn-On" NIR Fluorescent Probe with Large Stokes Shift for Nucleic Acid Research-Based Applications

Guanine-rich DNA/RNA sequences can form Hoogsteen bonds to adopt noncanonical secondary structures called G-quadruplexes, and these have been associated with diverse cellular processes. There has been considerable research interest in the design of G4-interacting ligands for cellular probing of the G4 structure and understanding its associated biological function. Most of the fluorescent G4 ligands either do not have significant selectivity over other nucleic acid structures, have high Stokes shift, or are not in the near-infrared (NIR) region, which limits its cellular visualization. The current work involves the rational design and synthesis of NIR fluorescent probes comprising a (Z)-1-methyl-2-((3-methylbenzo[d]thiazol-2(3H)-ylidene)methyl)quinolin-1-ium scaffold. Among the designed molecules, 4a exhibited far-red fluorescence (λmax = 680 nm) with large Stokes shift (∼182 nm) upon selective binding to human telomeric G-quadruplexes. The dye 4a does not disturb the conformation and stability of G-quadruplexes, thereby making it suitable for nucleic acid research based applications. Interestingly, 4a showed remarkable selectivity over single- and double-stranded structures in contrast to a commercially available quadruplex binding probe, Thiazole orange (TO). The molecular docking studies indicate that 4a binds at the groove region of the telomeric DNA G-quadruplex through π-π stacking interactions with the quinoline and amine-substituted phenyl ring and with the phosphate backbone through anion-π interactions with the benzothiazole ring. The designed molecule 4a has interesting photophysical properties, cell permeability, and biocompatibility with minimal cytotoxicity. Fluorescence imaging studies in live HeLa cells showed that probe 4a binds to the transient population of the DNA G-quadruplex in the nucleus and RNA quadruplexes in the cytoplasm. In brief, G-quadruplex NIR fluorescent probe 4a with a higher signal/noise ratio has significant potential for cellular imaging studies and thus opens avenues to decipher the biological pathways for better understanding of G-quadruplex biology.

Ythdf2 facilitates precursor miR-378/miR-378-5p maturation to support myogenic differentiation

Ythdf2 is known to mediate mRNA degradation in an m6A-dependent manner, and it has been shown to play a role in skeletal muscle differentiation. Recently, Ythdf2 was also found to bind to m6A-modified precursor miRNAs and regulate their maturation. However, it remains unknown whether this mechanism is related to the regulation of myogenesis by Ythdf2. Here, we observed that Ythdf2 knockdown significantly suppressed myotube formation and impacted miRNAs expression during myogenic differentiation. Through integrated analysis of miRNA and mRNA sequencing data, miR-378 and miR-378-5p were identified as important targets of Ythdf2 in myogenesis. Mechanically, Ythdf2 was found to interact with core components of the pre-miRNA processor complex, namely DICER1 and TARBP2, thereby facilitating the maturation of pre-miR-378/miR-378-5p in an m6A-dependent manner and resulting in an increase in the expression levels of mature miR-378 and miR-378-5p. Moreover, the downregulation of either miR-378 or miR-378-5p significantly inhibited myotube formation, while the forced expression of miR-378 or miR-378-5p could partially rescued Ythdf2 knockdown-induced suppression of myogenic differentiation by activating the mTOR pathway. Collectively, our results for the first time suggest that Ythdf2 regulates myogenic differentiation via mediating pre-miR-378/miR-378-5p maturation, which might provide new insights into the molecular mechanisms underlying m6A modification in the regulation of myogenesis.

Aberrant serum-derived FN1 variants bind to integrin β1 on glomerular endothelial cells contributing to thin basement membrane nephropathy

The glomerular basement membrane (GBM) is a critical component of the glomerular filtration barrier (GFB), with its thickness directly influencing renal function. While a uniformly thinned GBM can cause hematuria while preserving normal renal function, this condition is typically diagnosed as thin basement membrane nephropathy (TBMN). However, the pathogenesis and potential progression to renal insufficiency of TBMN are not fully understood. In this study, we analyzed clinical cohorts presenting with microscopic hematuria who underwent genetic testing and identified five novel pathogenic FN1 mutations. Through bioinformatics analysis of these variants, expression localization analysis of GBM-related molecules in renal biopsies, and functional studies of the mutants, we found that these variants exhibited gain-of-function characteristics. This led to the excessive deposition of aberrant serum-derived FN1 variants on glomerular endothelial cells rather than cell-type-specific variants. The deposition competitively binds FN1 variants to Integrin β1, disrupting the interaction with Laminin α5β2γ1 and subsequently reducing the expression of key GBM components, resulting in TBMN. This study elucidated, for the first time, the genetic pathogenesis of TBMN caused by FN1 variants. It provides a crucial foundation for understanding the progression of renal dysfunction associated with simple hematuria, highlights the potential for targeted therapeutic strategies, and differentiates TBMN from early-stage Alport syndrome.

Neuromedin S regulates goat ovarian granulosa cell proliferation and steroidogenesis via endoplasmic reticulum Ca2+-YAP1-ATF4-c-Jun pathway

Neuromedin S (NMS) plays key roles in reproductive regulation, while its function and mechanism in follicular development remain unclear. The current study aims to investigate the specific role and mechanisms of NMS and its receptors in regulating the proliferation and steroidogenesis of ovarian granulosa cells (GCs). Phenotypically, a certain concentration of NMS addition promoted the proliferation and estrogen production of goat GCs, accompanied by an increase in the G1/S cell population and upregulation of the expression levels of cyclin D1, cyclin dependent kinase 6, steroidogenic acute regulatory protein, cytochrome P450, family 11, subfamily A, polypeptide 1, 3beta-hydroxysteroid dehydrogenase, and cytochrome P450, family 11, subfamily A, polypeptide 1, while the effects of NMS treatment were effectively hindered by knockdown of neuromedin U receptor type 2 (NMUR2). Mechanistically, activation of NMUR2 with NMS maintained endoplasmic reticulum (ER) calcium (Ca2+) homeostasis by triggering the PLCG1-IP3R pathway, which helped preserve ER morphology, sustained an appropriate level of endoplasmic reticulum unfolded protein response (UPRer), and suppressed the nuclear translocation of activating transcription factor 4. Moreover, NMS maintained intracellular Ca2+ homeostasis to activate the calmodulin 1-large tumor suppressor kinase 1 pathway, ultimately orchestrating the regulation of goat GC proliferation and estrogen production through the Yes1 associated transcriptional regulator-ATF4-c-Jun pathway. Crucially, the effects of NMS were mitigated by concurrent knockdown of the NMUR2 gene. Collectively, these data suggest that activation of NMUR2 by NMS enhances cell proliferation and estrogen production in goat GCs through modulating the ER and intracellular Ca2+ homeostasis, leading to activation of the YAP1-ATF4-c-Jun pathway. These findings offer valuable insights into the regulatory mechanisms involved in follicular growth and development, providing a novel perspective for future research.

Self-assembly antimicrobial peptide for treatment of multidrug-resistant bacterial infection

The wide-spreading of multidrug resistance poses a significant threat to human and animal health. Although antimicrobial peptides (AMPs) show great potential application, their instability has severely limited their clinical application. Here, self-assembled AMPs composed of multiple modules based on the principle of associating natural marine peptide N6 with ß-sheet-forming peptide were designed. It is noteworthy that one of the designed peptides, FFN could self-assemble into nanoparticles at 35.46 µM and achieve a dynamic transformation from nanoparticles to nanofibers in the presence of bacteria, resulting in a significant increase in stability in trypsin and tissues by 1.72-57.5 times compared to that of N6. Additionally, FFN exhibits a broad spectrum of antibacterial activity against multidrug-resistant (MDR) gram-positive (G+) and gram-negative (G-) bacteria with Minimum inhibitory concentrations (MICs) as low as 2 µM by membrane destruction and complemented by nanofiber capture. In vivo mouse mastitis infection model further confirmed the therapeutic potential and promising biosafety of the self-assembled peptide FFN, which can effectively alleviate mastitis caused by MDR Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), and eliminate pathogenic bacteria. In conclusion, the design of peptide-based nanomaterials presents a novel approach for the delivery and clinical translation of AMPs, promoting their application in medicine and animal husbandry.

Identifying small-molecules binding sites in RNA conformational ensembles with SHAMAN

The rational targeting of RNA with small molecules is hampered by our still limited understanding of RNA structural and dynamic properties. Most in silico tools for binding site identification rely on static structures and therefore cannot face the challenges posed by the dynamic nature of RNA molecules. Here, we present SHAMAN, a computational technique to identify potential small-molecule binding sites in RNA structural ensembles. SHAMAN enables exploring the conformational landscape of RNA with atomistic molecular dynamics simulations and at the same time identifying RNA pockets in an efficient way with the aid of probes and enhanced-sampling techniques. In our benchmark composed of large, structured riboswitches as well as small, flexible viral RNAs, SHAMAN successfully identifies all the experimentally resolved pockets and ranks them among the most favorite probe hotspots. Overall, SHAMAN sets a solid foundation for future drug design efforts targeting RNA with small molecules, effectively addressing the long-standing challenges in the field.

Contrastive pre-training and 3D convolution neural network for RNA and small molecule binding affinity prediction

MOTIVATION

The diverse structures and functions inherent in RNAs present a wealth of potential drug targets. Some small molecules are anticipated to serve as leading compounds, providing guidance for the development of novel RNA-targeted therapeutics. Consequently, the determination of RNA-small molecule binding affinity is a critical undertaking in the landscape of RNA-targeted drug discovery and development. Nevertheless, to date, only one computational method for RNA-small molecule binding affinity prediction has been proposed. The prediction of RNA-small molecule binding affinity remains a significant challenge. The development of a computational model is deemed essential to effectively extract relevant features and predict RNA-small molecule binding affinity accurately.

RESULTS

In this study, we introduced RLaffinity, a novel deep learning model designed for the prediction of RNA-small molecule binding affinity based on 3D structures. RLaffinity integrated information from RNA pockets and small molecules, utilizing a 3D convolutional neural network (3D-CNN) coupled with a contrastive learning-based self-supervised pre-training model. To the best of our knowledge, RLaffinity was the first deep learning based method for the prediction of RNA-small molecule binding affinity. Our experimental results exhibited RLaffinity's superior performance compared to baseline methods, revealed by all metrics. The efficacy of RLaffinity underscores the capability of 3D-CNN to accurately extract both global pocket information and local neighbor nucleotide information within RNAs. Notably, the integration of a self-supervised pre-training model significantly enhanced predictive performance. Ultimately, RLaffinity was also proved as a potential tool for RNA-targeted drugs virtual screening.

AVAILABILITY AND IMPLEMENTATION

https://github.com/SaisaiSun/RLaffinity.

Assessing the performance of MM/PBSA and MM/GBSA methods. 10. Prediction reliability of binding affinities and binding poses for RNA-ligand complexes

Ribonucleic acid (RNA)-ligand interactions play a pivotal role in a wide spectrum of biological processes, ranging from protein biosynthesis to cellular reproduction. This recognition has prompted the broader acceptance of RNA as a viable candidate for drug targets. Delving into the atomic-scale understanding of RNA-ligand interactions holds paramount importance in unraveling intricate molecular mechanisms and further contributing to RNA-based drug discovery. Computational approaches, particularly molecular docking, offer an efficient way of predicting the interactions between RNA and small molecules. However, the accuracy and reliability of these predictions heavily depend on the performance of scoring functions (SFs). In contrast to the majority of SFs used in RNA-ligand docking, the end-point binding free energy calculation methods, such as molecular mechanics/generalized Born surface area (MM/GBSA) and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA), stand as theoretically more rigorous approaches. Yet, the evaluation of their effectiveness in predicting both binding affinities and binding poses within RNA-ligand systems remains unexplored. This study first reported the performance of MM/PBSA and MM/GBSA with diverse solvation models, interior dielectric constants (εin) and force fields in the context of binding affinity prediction for 29 RNA-ligand complexes. MM/GBSA is based on short (5 ns) molecular dynamics (MD) simulations in an explicit solvent with the YIL force field; the GBGBn2 model with higher interior dielectric constant (εin = 12, 16 or 20) yields the best correlation (Rp = -0.513), which outperforms the best correlation (Rp = -0.317, rDock) offered by various docking programs. Then, the efficacy of MM/GBSA in identifying the near-native binding poses from the decoys was assessed based on 56 RNA-ligand complexes. However, it is evident that MM/GBSA has limitations in accurately predicting binding poses for RNA-ligand systems, particularly compared with notably proficient docking programs like rDock and PLANTS. The best top-1 success rate achieved by MM/GBSA rescoring is 39.3%, which falls below the best results given by docking programs (50%, PLNATS). This study represents the first evaluation of MM/PBSA and MM/GBSA for RNA-ligand systems and is expected to provide valuable insights into their successful application to RNA targets.

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.

Unusual Ni⋯Ni interaction in Ni(ii) complexes as potential inhibitors for the development of new anti-SARS-CoV-2 Omicron drugs

Two nickel(ii) coordination complexes [Ni(L)]2(1) and [Ni(L)]n(2) of a tetradentate Schiff base ligand (H2L) derived from 2-hydroxy-1-naphthaldehyde with ethylenediamine were synthesized, designed, and characterized via spectroscopic and single crystal XRD analyses. Both nickel(ii) complexes exhibited unusual Ni⋯Ni interactions and were fully characterized via single-crystal X-ray crystallography. Nickel(ii) complexes [Ni(L)]2(1) and [Ni(L)]n(2) crystallize in monoclinic and triclinic crystal systems with P21/c and P1̄ space groups, respectively, and revealed square planar geometry around each Ni(ii) ion. The structure of both the complexes have established the existence of a new kind of metal system containing nickel(ii)-nickel(ii) interactions with a square planar-like geometry about the nickel(ii) atoms. Both square planar Ni(ii) complexes were often stacked with relatively short Ni⋯Ni distances. The non-bonded Ni-Ni distance (Ni⋯Ni separation) seems to be 3.356 Å and 3.214 Å from the nickel atoms of [Ni(L)]2(1) and [Ni(L)]n(2), respectively. These distances are shorter than the sum of their van der Waals radii (4.80 Å) but longer than the sum of their covalent radii (2.50 Å), indicating that there is a Ni⋯Ni interaction but not a Ni-Ni bond. The discrete molecules are π-stacked and connected via weak intermolecular interactions (C-H⋯O and C-H⋯N). Cyclic voltammetry measurements were obtained for both the complexes, and their pharmacokinetic and chemoinformatics properties were also explored. Detailed structural analysis and non-covalent supramolecular interactions were investigated using single-crystal structure analysis and computational approaches. Both the unique structures show good inhibition performance for the Omicron spike proteins of the SARS CoV-2 virus. To gain insights into potential SARS-CoV-2 Omicron drugs and find inhibitors against the Omicron variants of SARS-CoV-2, we examined the molecular docking of the nickel(ii) complexes [Ni(L)]2(1) and [Ni(L)]n(2) with the SARS-CoV-2 Omicron spike protein (PDB ID: 7WK2 and 7WVO). A strong binding was predicted between Ni(ii) coordination complexes [Ni(L)]2(1) and [Ni(L)]n(2) with the SARS-CoV-2 Omicron variant receptor protein through the negative value of binding affinity. Molecular docking of Nil(ii) complexes [Ni(L)]2(1) and [Ni(L)]n(2) with a DNA duplex (PDB ID: 7D3T) and RNA (PDB ID: 7TDC) binding protein was also studied. Overall, this study suggests that Ni(ii) complexes can be considered as drug candidates against the Omicron variants of SARS-CoV-2.

Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation

Non-coding RNAs (ncRNAs), generated from nonprotein coding DNA sequences, constitute 98-99% of the human genome. Non-coding RNAs encompass diverse functional classes, including microRNAs, small interfering RNAs, PIWI-interacting RNAs, small nuclear RNAs, small nucleolar RNAs, and long non-coding RNAs. With critical involvement in gene expression and regulation across various biological and physiopathological contexts, such as neuronal disorders, immune responses, cardiovascular diseases, and cancer, non-coding RNAs are emerging as disease biomarkers and therapeutic targets. In this review, after providing an overview of non-coding RNAs' role in cell homeostasis, we illustrate the potential and the challenges of state-of-the-art computational methods exploited to study non-coding RNAs biogenesis, function, and modulation. This can be done by directly targeting them with small molecules or by altering their expression by targeting the cellular engines underlying their biosynthesis. Drawing from applications, also taken from our work, we showcase the significance and role of computer simulations in uncovering fundamental facets of ncRNA mechanisms and modulation. This information may set the basis to advance gene modulation tools and therapeutic strategies to address unmet medical needs.

Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning

Ribonucleic acids (RNAs) play important roles in cellular regulation. Consequently, dysregulation of both coding and non-coding RNAs has been implicated in several disease conditions in the human body. In this regard, a growing interest has been observed to probe into the potential of RNAs to act as drug targets in disease conditions. To accelerate this search for disease-associated novel RNA targets and their small molecular inhibitors, machine learning models for binding affinity prediction were developed specific to six RNA subtypes namely, aptamers, miRNAs, repeats, ribosomal RNAs, riboswitches and viral RNAs. We found that differences in RNA sequence composition, flexibility and polar nature of RNA-binding ligands are important for predicting the binding affinity. Our method showed an average Pearson correlation (r) of 0.83 and a mean absolute error of 0.66 upon evaluation using the jack-knife test, indicating their reliability despite the low amount of data available for several RNA subtypes. Further, the models were validated with external blind test datasets, which outperform other existing quantitative structure-activity relationship (QSAR) models. We have developed a web server to host the models, RNA-Small molecule binding Affinity Predictor, which is freely available at: https://web.iitm.ac.in/bioinfo2/RSAPred/.

Comparative Assessment of Pose Prediction Accuracy in RNA-Ligand Docking

Structure-based virtual high-throughput screening is used in early-stage drug discovery. Over the years, docking protocols and scoring functions for protein-ligand complexes have evolved to improve the accuracy in the computation of binding strengths and poses. In the past decade, RNA has also emerged as a target class for new small-molecule drugs. However, most ligand docking programs have been validated and tested for proteins and not RNA. Here, we test the docking power (pose prediction accuracy) of three state-of-the-art docking protocols on 173 RNA-small molecule crystal structures. The programs are AutoDock4 (AD4) and AutoDock Vina (Vina), which were designed for protein targets, and rDock, which was designed for both protein and nucleic acid targets. AD4 performed relatively poorly. For RNA targets for which a crystal structure of a bound ligand used to limit the docking search space is available and for which the goal is to identify new molecules for the same pocket, rDock performs slightly better than Vina, with success rates of 48% and 63%, respectively. However, in the more common type of early-stage drug discovery setting, in which no structure of a ligand-target complex is known and for which a larger search space is defined, rDock performed similarly to Vina, with a low success rate of ∼27%. Vina was found to have bias for ligands with certain physicochemical properties, whereas rDock performs similarly for all ligand properties. Thus, for projects where no ligand-protein structure already exists, Vina and rDock are both applicable. However, the relatively poor performance of all methods relative to protein-target docking illustrates a need for further methods refinement.

RNA-Selective Small-Molecule Ligands: Recent Advances in Live-Cell Imaging and Drug Discovery

RNA structures, including those formed from coding and noncoding RNAs, alternative to protein-based drug targets, could be a promising target of small molecules for drug discovery against various human diseases, particularly in anticancer, antibacterial and antivirus development. The normal cellular activity of cells is critically dependent on the function of various RNA molecules generated from DNA transcription. Moreover, many studies support that mRNA-targeting small molecules may regulate the synthesis of disease-related proteins via the non-covalent mRNA-ligand interactions that do not involve gene modification. RNA-ligand interaction is thus an attractive approach to address the challenge of "undruggable" proteins in drug discovery because the intracellular activity of these proteins is hard to be suppressed with small molecule ligands. We selectively surveyed a specific area of RNA structure-selective small molecule ligands in fluorescence live cell imaging and drug discovery because the area was currently underexplored. This state-of-the-art review thus mainly focuses on the research published within the past three years and aims to provide the most recent information on this research area; hopefully, it could be complementary to the previously reported reviews and give new insights into the future development on RNA-specific small molecule ligands for live cell imaging and drug discovery.

In Vitro Affinity Maturation of Nanobodies against Mpox Virus A29 Protein Based on Computer-Aided Design

Mpox virus (MPXV), the most pathogenic zoonotic orthopoxvirus, caused worldwide concern during the SARS-CoV-2 epidemic. Growing evidence suggests that the MPXV surface protein A29 could be a specific diagnostic marker for immunological detection. In this study, a fully synthetic phage display library was screened, revealing two nanobodies (A1 and H8) that specifically recognize A29. Subsequently, an in vitro affinity maturation strategy based on computer-aided design was proposed by building and docking the A29 and A1 three-dimensional structures. Ligand-receptor binding and molecular dynamics simulations were performed to predict binding modes and key residues. Three mutant antibodies were predicted using the platform, increasing the affinity by approximately 10-fold compared with the parental form. These results will facilitate the application of computers in antibody optimization and reduce the cost of antibody development; moreover, the predicted antibodies provide a reference for establishing an immunological response against MPXV.

How Good Are Current Docking Programs at Nucleic Acid-Ligand Docking? A Comprehensive Evaluation

Nucleic acid (NA)-ligand interactions are of paramount importance in a variety of biological processes, including cellular reproduction and protein biosynthesis, and therefore, NAs have been broadly recognized as potential drug targets. Understanding NA-ligand interactions at the atomic scale is essential for investigating the molecular mechanism and further assisting in NA-targeted drug discovery. Molecular docking is one of the predominant computational approaches for predicting the interactions between NAs and small molecules. Despite the availability of versatile docking programs, their performance profiles for NA-ligand complexes have not been thoroughly characterized. In this study, we first compiled the largest structure-based NA-ligand binding data set to date, containing 800 noncovalent NA-ligand complexes with clearly identified ligands. Based on this extensive data set, eight frequently used docking programs, including six protein-ligand docking programs (LeDock, Surflex-Dock, UCSF Dock6, AutoDock, AutoDock Vina, and PLANTS) and two specific NA-ligand docking programs (rDock and RLDOCK), were systematically evaluated in terms of binding pose and binding affinity predictions. The results demonstrated that some protein-ligand docking programs, specifically PLANTS and LeDock, produced more promising or comparable results compared with the specialized NA-ligand docking programs. Among the programs evaluated, PLANTS, rDock, and LeDock showed the highest performance in binding pose prediction, and their top-1 and best root-mean-square deviation (rmsd) success rates were as follows: PLANTS (35.93 and 76.05%), rDock (27.25 and 72.16%), and LeDock (27.40 and 64.37%). Compared with the moderate level of binding pose prediction, few programs were successful in binding affinity prediction, and the best correlation (Rp = -0.461) was observed with PLANTS. Finally, further comparison with the latest NA-ligand docking program (NLDock) on four well-established data sets revealed that PLANTS and LeDock outperformed NLDock in terms of binding pose prediction on all data sets, demonstrating their significant potential for NA-ligand docking. To the best of our knowledge, this study is the most comprehensive evaluation of popular molecular docking programs for NA-ligand systems.

RNA 3D Structure Prediction: Progress and Perspective

Ribonucleic acid (RNA) molecules play vital roles in numerous important biological functions such as catalysis and gene regulation. The functions of RNAs are strongly coupled to their structures or proper structure changes, and RNA structure prediction has been paid much attention in the last two decades. Some computational models have been developed to predict RNA three-dimensional (3D) structures in silico, and these models are generally composed of predicting RNA 3D structure ensemble, evaluating near-native RNAs from the structure ensemble, and refining the identified RNAs. In this review, we will make a comprehensive overview of the recent advances in RNA 3D structure modeling, including structure ensemble prediction, evaluation, and refinement. Finally, we will emphasize some insights and perspectives in modeling RNA 3D structures.

Comparative Assessment of Docking Programs for Docking and Virtual Screening of Ribosomal Oxazolidinone Antibacterial Agents

Oxazolidinones are a broad-spectrum class of synthetic antibiotics that bind to the 50S ribosomal subunit of Gram-positive and Gram-negative bacteria. Many crystal structures of the ribosomes with oxazolidinone ligands have been reported in the literature, facilitating structure-based design using methods such as molecular docking. It would be of great interest to know in advance how well docking methods can reproduce the correct ligand binding modes and rank these correctly. We examined the performance of five molecular docking programs (AutoDock 4, AutoDock Vina, DOCK 6, rDock, and RLDock) for their ability to model ribosomal-ligand interactions with oxazolidinones. Eleven ribosomal crystal structures with oxazolidinones as the ligands were docked. The accuracy was evaluated by calculating the docked complexes' root-mean-square deviation (RMSD) and the program's internal scoring function. The rankings for each program based on the median RMSD between the native and predicted were DOCK 6 > AD4 > Vina > RDOCK >> RLDOCK. Results demonstrate that the top-performing program, DOCK 6, could accurately replicate the ligand binding in only four of the eleven ribosomes due to the poor electron density of said ribosomal structures. In this study, we have further benchmarked the performance of the DOCK 6 docking algorithm and scoring in improving virtual screening (VS) enrichment using the dataset of 285 oxazolidinone derivatives against oxazolidinone binding sites in the S. aureus ribosome. However, there was no clear trend between the structure and activity of the oxazolidinones in VS. Overall, the docking performance indicates that the RNA pocket's high flexibility does not allow for accurate docking prediction, highlighting the need to validate VS. protocols for ligand-RNA before future use. Later, we developed a re-scoring method incorporating absolute docking scores and molecular descriptors, and the results indicate that the descriptors greatly improve the correlation of docking scores and pMIC values. Morgan fingerprint analysis was also used, suggesting that DOCK 6 underpredicted molecules with tail modifications with acetamide, n-methylacetamide, or n-ethylacetamide and over-predicted molecule derivatives with methylamino bits. Alternatively, a ligand-based approach similar to a field template was taken, indicating that each derivative's tail groups have strong positive and negative electrostatic potential contributing to microbial activity. These results indicate that one should perform VS. campaigns of ribosomal antibiotics with care and that more comprehensive strategies, including molecular dynamics simulations and relative free energy calculations, might be necessary in conjunction with VS. and docking.

Protein-Based Virtual Screening Tools Applied for RNA-Ligand Docking Identify New Binders of the preQ1-Riboswitch

Targeting RNA with small molecules is an emerging field. While several ligands for different RNA targets are reported, structure-based virtual screenings (VSs) against RNAs are still rare. Here, we elucidated the general capabilities of protein-based docking programs to reproduce native binding modes of small-molecule RNA ligands and to discriminate known binders from decoys by the scoring function. The programs were found to perform similar compared to the RNA-based docking tool rDOCK, and the challenges faced during docking, namely, protomer and tautomer selection, target dynamics, and explicit solvent, do not largely differ from challenges in conventional protein-ligand docking. A prospective VS with the Bacillus subtilis preQ₁-riboswitch aptamer domain performed with FRED, HYBRID, and FlexX followed by microscale thermophoresis assays identified six active compounds out of 23 tested VS hits with potencies between 29.5 nM and 11.0 μM. The hits were selected not solely based on their docking score but for resembling key interactions of the native ligand. Therefore, this study demonstrates the general feasibility to perform structure-based VSs against RNA targets, while at the same time it highlights pitfalls and their potential solutions when executing RNA-ligand docking.

Targeting Pathogenic DNA and RNA Repeats: A Conceptual Therapeutic Way for Repeat Expansion Diseases

Expansions of short tandem repeats (STRs) in the human genome cause nearly 50 neurodegenerative diseases, which are mostly inheritable, nonpreventable and incurable, posing as a huge threat to human health. Non-B DNAs formed by STRs are thought to be structural intermediates that can cause repeat expansions. The subsequent transcripts harboring expanded RNA repeats can further induce cellular toxicity through forming specific structures. Direct targeting of these pathogenic DNA and RNA repeats has emerged as a new potential therapeutic strategy to cure repeat expansion diseases. In this conceptual review, we first introduce the roles of DNA and RNA structures in the genetic instabilities and pathomechanisms of repeat expansion diseases, then describe structural features of DNA and RNA repeats with a focus on the tertiary structures determined by X-ray crystallography and solution nuclear magnetic resonance spectroscopy, and finally discuss recent progress and perspectives of developing chemical tools that target pathogenic DNA and RNA repeats for curing repeat expansion diseases.

Docking and scoring for nucleic acid-ligand interactions: Principles and current status

Nucleic acid (NA)-ligand interactions have crucial roles in many cellular processes and, thus, are increasingly attracting therapeutic interest in drug discovery. Molecular docking is a valuable tool for studying molecular interactions. However, because NAs differ significantly from proteins in both their physical and chemical properties, traditional docking algorithms and scoring functions for protein-ligand interactions might not be applicable to NA-ligand docking. Therefore, various sampling strategies and scoring functions for NA-ligand interactions have been developed. Here, we review the basic principles and current status of docking algorithms and scoring functions for DNA/RNA-ligand interactions. We also discuss challenges and limitations of current docking and scoring approaches.