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

Potential role of formononetin as a novel natural agent in Alzheimer's disease and osteoporosis comorbidity

BACKGROUND

The growing aging population has led to an increase in the prevalence of Alzheimer's disease (AD) and osteoporosis (OP), both of which significantly impair quality of life. The comorbid nature of these conditions suggests a shared genetic etiology, the understanding of which is crucial for developing targeted therapies.

OBJECTIVE

This study aims to explore the shared genetic etiology underlying AD and OP, using a system biology approach to identify potential therapeutic targets and natural compounds for treatment.

METHODS

We employed Weighted Gene Co-Expression Network Analysis (WGCNA) with molecular docking strategies to uncover the genetic links between AD and OP. MT2A and CACNA1C were identified as key pleiotropic hub genes potentially linking AD and OP. Molecular docking was utilized to screen for compounds with therapeutic potential, leading to the identification of formononetin as a compound with significant binding affinity to these hub genes. Quantitative real-time PCR (qRT-PCR) validation was conducted to confirm the gene expression changes in disease models.

RESULTS

Our study indicate that formononetin exhibits strong binding affinity to the identified hub genes, MT2A and CACNA1C. qRT-PCR validation confirmed the upregulation of these genes in disease models, which was mitigated upon treatment with formononetin, suggesting a reversal of disease markers.

CONCLUSIONS

This study advances our understanding of the genetic intersections between AD and OP and positions formononetin as a promising natural agent for further translational research. Formononetin's multi-target potential makes it a valuable candidate for managing these comorbid conditions, meriting further investigation and development as a therapeutic strategy.

Impact of pH and protein/polysaccharide ratio on phycocyanin-okra polysaccharides complex

Phycocyanin is a natural blue pigment that tends to denature and lose its color in acidic solutions. In response to this problem, the complexes of phycocyanin (PC) with okra polysaccharides (OP) were prepared by ultrasonic processing at different pH conditions, and the molecular interactions of the complexes were characterized. The results showed that there were significant differences in the color, functional groups, and surface morphology of the complexes formed at different pH conditions. By colorimetry and particle size tester, it was demonstrated that the complex solution showed a steady blue color at pH 3.4. The highest fluorescence intensity (1.55 × 107 a.u.) and the significant red-shift of the complexes were observed at 0.4 % m/v polysaccharides addition. Infrared spectroscopy test further demonstrated that OP induced the formation of higher-order trimers of PC, which kept the color stable. Molecular dynamics simulations showed that the binding energy of PC/OP complex was -42.21 ± 2.61 kcal/mol, indicating that the binding affinity was very strong. Overall, this study suggests that this complex stabilizes the structure of PC, which in turn exerts a biological effect and will facilitate the use of PC as an artificial color substitute in food or beverage applications.

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.

Modeling and NMR Data Elucidate the Structure of a G-Quadruplex-Ligand Interaction for a Pu22T-Cyclometalated Iridium(III) System

Cyclometalated iridium(III) complexes are increasingly being developed for application in G-quadruplex (GQ) nucleic acid biosensors. We monitored the interactions of a GQ structure with an iridium(III) complex by nuclear magnetic resonance (NMR) titrations and subsequently compared the binding site inferred from NMR with binding positions modeled by molecular docking and molecular dynamics simulations. When titrated into a solution of G-quadruplex Pu22T, compound 1(PF6), [Ir(ppy)2(pizp)](PF6), where ppy is 2-phenylpyridine and pizp is 2-phenylimidazole[4,5f][1,10]phenanthroline, had the greatest impact on the hydrogen chemical shifts of G5, G8, G9, G13, and G17 residues of Pu22T, indicating end-stacking at the 5' tetrad. In blind cross-docking studies with Autodock 4, end-stacking at the 5' tetrad was found as the lowest energy binding position. AMBER molecular dynamics simulations resulted in a refined binding position at the 5' tetrad with improved pi stacking. For this model system, Pu22T-1, molecular docking and molecular dynamics simulations are tools that are able to predict the experimentally determined binding position.

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.

Predicting Small Molecule Binding Nucleotides in RNA Structures Using RNA Surface Topography

RNA small molecule interactions play a crucial role in drug discovery and inhibitor design. Identifying RNA small molecule binding nucleotides is essential and requires methods that exhibit a high predictive ability to facilitate drug discovery and inhibitor design. Existing methods can predict the binding nucleotides of simple RNA structures, but it is hard to predict binding nucleotides in complex RNA structures with junctions. To address this limitation, we developed a new deep learning model based on spatial correlation, ZHmolReSTasite, which can accurately predict binding nucleotides of small and large RNA with junctions. We utilize RNA surface topography to consider the spatial correlation, characterizing nucleotides from sequence and tertiary structures to learn a high-level representation. Our method outperforms existing methods for benchmark test sets composed of simple RNA structures, achieving precision values of 72.9% on TE18 and 76.7% on RB9 test sets. For a challenging test set composed of RNA structures with junctions, our method outperforms the second best method by 11.6% in precision. Moreover, ZHmolReSTasite demonstrates robustness regarding the predicted RNA structures. In summary, ZHmolReSTasite successfully incorporates spatial correlation, outperforms previous methods on small and large RNA structures using RNA surface topography, and can provide valuable insights into RNA small molecule prediction and accelerate RNA inhibitor design.

Virtual Screening and Validation of Affinity DNA Functional Ligands for IgG Fc Segment

The effective attachment of antibodies to the immune sensing interface is a crucial factor that determines the detection performance of immunosensors. Therefore, this study aims to investigate a novel antibody immobilization material with low molecular weight, high stability, and excellent directional immobilization effect. In this study, we employed molecular docking technology based on the ZDOCK algorithm to virtually screen DNA functional ligands (DNAFL) for the Fc segment of antibodies. Through a comprehensive analysis of the key binding sites and contact propensities at the interface between DNAFL and IgG antibody, we have gained valuable insights into the affinity relationship, as well as the principles governing amino acid and nucleotide interactions at this interface. Furthermore, molecular affinity experiments and competitive binding experiments were conducted to validate both the binding ability of DNAFL to IgG antibody and its actual binding site. Through affinity experiments using multi-base sequences, we identified bases that significantly influence antibody-DNAFL binding and successfully obtained DNAFL with an enhanced affinity towards the IgG Fc segment. These findings provide a theoretical foundation for the targeted design of higher-affinity DNAFLs while also presenting a new technical approach for immunosensor preparation with potential applications in biodetection.

Aptamer Screening: Current Methods and Future Trend towards Non-SELEX Approach

Aptamers are nucleic acid sequences that specifically bind with target molecules and are vital to applications such as biosensing, drug development, disease diagnostics, etc. The traditional selection procedure of aptamers is based on the Systematic Evolution of Ligands by an Exponential Enrichment (SELEX) process, which relies on repeating cycles of screening and amplification. With the rapid development of aptamer applications, RNA and XNA aptamers draw more attention than before. But their selection is troublesome due to the necessary reverse transcription and transcription process (RNA) or low efficiency and accuracy of enzymes for amplification (XNA). In light of this, we review the recent advances in aptamer selection methods and give an outlook on future development in a non-SELEX approach, which simplifies the procedure and reduces the experimental costs. We first provide an overview of the traditional SELEX methods mostly designed for screening DNA aptamers to introduce the common tools and methods. Then a section on the current screening methods for RNA and XNA is prepared to demonstrate the efforts put into screening these aptamers and the current difficulties. We further predict that the future trend of aptamer selection lies in non-SELEX methods that do not require nucleic acid amplification. We divide non-SELEX methods into an immobilized format and non-immobilized format and discuss how high-resolution partitioning methods could facilitate the further improvement of selection efficiency and accuracy.

Advances in machine-learning approaches to RNA-targeted drug design

RNA molecules play multifaceted functional and regulatory roles within cells and have garnered significant attention in recent years as promising therapeutic targets. With remarkable successes achieved by artificial intelligence (AI) in different fields such as computer vision and natural language processing, there is a growing imperative to harness AI's potential in computer-aided drug design (CADD) to discover novel drug compounds that target RNA. Although machine-learning (ML) approaches have been widely adopted in the discovery of small molecules targeting proteins, the application of ML approaches to model interactions between RNA and small molecule is still in its infancy. Compared to protein-targeted drug discovery, the major challenges in ML-based RNA-targeted drug discovery stem from the scarcity of available data resources. With the growing interest and the development of curated databases focusing on interactions between RNA and small molecule, the field anticipates a rapid growth and the opening of a new avenue for disease treatment. In this review, we aim to provide an overview of recent advancements in computationally modeling RNA-small molecule interactions within the context of RNA-targeted drug discovery, with a particular emphasis on methodologies employing ML techniques.

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.

Geometric deep learning for the prediction of magnesium-binding sites in RNA structures

Magnesium ions (Mg2+) are essential for the folding, functional expression, and structural stability of RNA molecules. However, predicting Mg2+-binding sites in RNA molecules based solely on RNA structures is still challenging. The molecular surface, characterized by a continuous shape with geometric and chemical properties, is important for RNA modelling and carries essential information for understanding the interactions between RNAs and Mg2+ ions. Here, we propose an approach named RNA-magnesium ion surface interaction fingerprinting (RMSIF), a geometric deep learning-based conceptual framework to predict magnesium ion binding sites in RNA structures. To evaluate the performance of RMSIF, we systematically enumerated decoy Mg2+ ions across a full-space grid within the range of 2 to 10 Å from the RNA molecule and made predictions accordingly. Visualization techniques were used to validate the prediction results and calculate success rates. Comparative assessments against state-of-the-art methods like MetalionRNA, MgNet, and Metal3DRNA revealed that RMSIF achieved superior success rates and accuracy in predicting Mg2+-binding sites. Additionally, in terms of the spatial distribution of Mg2+ ions within the RNA structures, a majority were situated in the deep grooves, while a minority occupied the shallow grooves. Collectively, the conceptual framework developed in this study holds promise for advancing insights into drug design, RNA co-transcriptional folding, and structure prediction.

Ionic liquids and deep eutectic solvents for the stabilization of biopharmaceuticals: A review

Biopharmaceuticals have allowed the control of previously untreatable diseases. However, their low solubility and stability still hinder their application, transport, and storage. Hence, researchers have applied different compounds to preserve and enhance the delivery of biopharmaceuticals, such as ionic liquids (ILs) and deep eutectic solvents (DESs). Although the biopharmaceutical industry can employ various substances for enhancing formulations, their effect will change depending on the properties of the target biomolecule and environmental conditions. Hence, this review organized the current state-of-the-art on the application of ILs and DESs to stabilize biopharmaceuticals, considering the properties of the biomolecules, ILs, and DESs classes, concentration range, types of stability, and effect. We also provided a critical discussion regarding the potential utilization of ILs and DESs in pharmaceutical formulations, considering the restrictions in this field, as well as the advantages and drawbacks of these substances for medical applications. Overall, the most applied IL and DES classes for stabilizing biopharmaceuticals were cholinium-, imidazolium-, and ammonium-based, with cholinium ILs also employed to improve their delivery. Interestingly, dilute and concentrated ILs and DESs solutions presented similar results regarding the stabilization of biopharmaceuticals. With additional investigation, ILs and DESs have the potential to overcome current challenges in biopharmaceutical formulation.

Record Resolution of Nanometal Surface Energy Transfer Optical Nanoruler Projects 3D Spatial Configuration of Aptamers on a Living Cell Membrane

Decrypting the in situ three-dimensional spatial configuration of an aptamer is of considerable significance; however, suitable nanoscale resolution tools are lacking. Herein, we show that a new nanometal surface energy transfer (NSET) optical nanoruler has a record resolution, down to single-nucleobase levels. We labeled fluorophores on different T bases of XQ-2d, including 5', 3', 6T, 22T, 38T, and 52T positions. The NSET nanoruler in situ decrypted the base sequence-dependent distance projection on the nanogold surface, demonstrating that 5', 3', stem, and loop structures are symmetrical in three-dimensional spatial configuration. The orientation of the 5' and 3' stem was toward the antiCD71-binding site, whereas the loop was in the opposite direction at a considerable distance. Molecular docking simulation was performed to list all of the possible conformations; however, all base distance parameters projecting on the nanogold surface determined a single conformation of XQ-2d. The specific binding sites of XQ-2d were Lys477, Ser691, and Arg698 on the CD71 receptor.

Exploring the role of microbial proteins in controlling environmental pollutants based on molecular simulation

Molecular simulation has been widely used to study microbial proteins' structural composition and dynamic properties, such as volatility, flexibility, and stability at the microscopic scale. Herein, this review describes the key elements of molecular docking and molecular dynamics (MD) simulations in molecular simulation; reviews the techniques combined with molecular simulation, such as crystallography, spectroscopy, molecular biology, and machine learning, to validate simulation results and bridge information gaps in the structure, microenvironmental changes, expression mechanisms, and intensity quantification; illustrates the application of molecular simulation, in characterizing the molecular mechanisms of interaction of microbial proteins with four different types of contaminants, namely heavy metals (HMs), pesticides, dyes and emerging contaminants (ECs). Finally, the review outlines the important role of molecular simulations in the study of microbial proteins for controlling environmental contamination and provides ideas for the application of molecular simulation in screening microbial proteins and incorporating targeted mutagenesis to obtain more effective contaminant control proteins.

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.

Pyrrole-Based Enaminones as Building Blocks for the Synthesis of Indolizines and Pyrrolo[1,2-a]pyrazines Showing Potent Antifungal Activity

As a new approach, pyrrolo[1,2-a]pyrazines were synthesized through the cyclization of 2-formylpyrrole-based enaminones in the presence of ammonium acetate. The enaminones were prepared with a straightforward method, reacting the corresponding alkyl 2-(2-formyl-1H-pyrrol-1-yl)acetates, 2-(2-formyl-1H-pyrrol-1-yl)acetonitrile, and 2-(2-formyl-1H-pyrrol-1-yl)acetophenones with DMFDMA. Analogous enaminones elaborated from alkyl (E)-3-(1H-pyrrol-2-yl)acrylates were treated with a Lewis acid to afford indolizines. The antifungal activity of the series of substituted pyrroles, pyrrole-based enaminones, pyrrolo[1,2-a]pyrazines, and indolizines was evaluated on six Candida spp., including two multidrug-resistant ones. Compared to the reference drugs, most test compounds produced a more robust antifungal effect. Docking analysis suggests that the inhibition of yeast growth was probably mediated by the interaction of the compounds with the catalytic site of HMGR of the Candida species.

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.

The Advances and Limitations of the Determination and Applications of Water Structure in Molecular Engineering

Water is a key actor of various processes of nature and, therefore, molecular engineering has to take the structural and energetic consequences of hydration into account. While the present review focuses on the target-ligand interactions in drug design, with a focus on biomolecules, these methods and applications can be easily adapted to other fields of the molecular engineering of molecular complexes, including solid hydrates. The review starts with the problems and solutions of the determination of water structures. The experimental approaches and theoretical calculations are summarized, including conceptual classifications. The implementations and applications of water models are featured for the calculation of the binding thermodynamics and computational ligand docking. It is concluded that theoretical approaches not only reproduce or complete experimental water structures, but also provide key information on the contribution of individual water molecules and are indispensable tools in molecular engineering.

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.

PacDOCK: A Web Server for Positional Distance-Based and Interaction-Based Analysis of Docking Results

Molecular docking is a key method for structure-based drug design used to predict the conformations assumed by small drug-like ligands when bound to their target. However, the evaluation of molecular docking studies can be hampered by the lack of a free and easy to use platform for the complete analysis of results obtained by the principal docking programs. To this aim, we developed PacDOCK, a freely available and user-friendly web server that comprises a collection of tools for positional distance-based and interaction-based analysis of docking results, which can be provided in several file formats. PacDOCK allows a complete analysis of molecular docking results through root mean square deviation (RMSD) calculation, molecular visualization, and cluster analysis of docked poses. The RMSD calculation compares docked structures with a reference structure, also when atoms are randomly labelled, and their conformational and positional differences can be visualised. In addition, it is possible to visualise a ligand into the target binding pocket and investigate the key receptor–ligand interactions. Moreover, PacDOCK enables the clustering of docking results by identifying a restrained number of clusters from many docked poses. We believe that PacDOCK will contribute to facilitating the analysis of docking results to improve the efficiency of computer-aided drug design.

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.

Understanding the binding behavior of Malathion with calf thymus DNA by spectroscopic, cell viability and molecular dynamics simulation techniques: binary and ternary systems comparison

The interaction between calf thymus DNA (ctDNA) and Malathion in the absence and presence of Histone 1 has been enquired by the means of spectroscopic, viscometry, molecular modeling, and cell viability assay techniques. Malathion is capable of quenching the fluorescence of ct DNA in the absence and presence of H1. The binding constants of Malathion-ctDNA complex in the absence of H1 have been calculated to be 6.62 × 10⁴, 4.31 × 10⁴ and 1.93 × 10⁴ M^-1 at 298, 303, and 308 K, respectively that revealed static quenching in complex formation. The observed negative values of enthalpy and entropy changes indicate that the main binding interaction forces were van der Waals force and hydrogen bonding. The binding constant between Malathion and single-stranded ctDNA (ss ctDNA) seemed to be much weaker than that of Malathion and double-stranded ctDNA (ds ctDNA). Furthermore, Malathion can induce detectable alterations in the CD spectrum of ctDNA, along with changes in its viscosity. In the presence of H1, fluorescence quenching of ctDNA-Malathion complex displays dynamic behavior and binding constants were perceived to be 1.66 × 10⁴, 2.93 × 10⁴ and 5.77 × 10⁴ M^-1 at 298, 303, and 308 K, respectively. The different of interaction behavior between ctDNA and Malathion in the absence and presence of H1 clearly revealed H1 role in the complex formation and forces change between ctDNA and Malathion. The positive values of enthalpy and entropy changes have suggested that binding process is primarily driven by hydrophobic interactions. The tendency to interact with ss ctDNA, reduced viscosity have designated that the Malathion bound to ctDNA in the presence of H1 is groove binding. The results of molecular docking and molecular dynamics simulation also confirmed potent interactions between Malathion and the macromolecules in the binary and ternary systems.Communicated by Ramaswamy H. Sarma.