AutoDockFR: Advances in Protein-Ligand Docking with Explicitly Specified Binding Site Flexibility

Near-infrared fluorescent probe for sensitive detection and imaging of DNA G4s in living cells

G-quadruplexs (G4s), four-stranded nucleic acid secondary structures, which formed by guanine-rich sequences play a vital role in human biological systems. Studies have shown that the formation of G4s is closely related to tumor development and apoptosis, which is considered as a new target for the development of anti-tumor drugs. Therefore, it is important to develop novel probes for G4s imaging. In this article, we engineered a near-infrared fluorescent probe (TOH) which can be activated by DNA G4s in living cells and tumor. TOH exhibits high selectivity to the structure of DNA G4s with the limit of detection for DNA G4s (Mito-0.5-2) is calculated to be 0.43 nM. Imaging studies of different cell lines revealed that the brighter fluorescence in cancer cell lines than in normal, indicating that DNA G4s maybe highly express in tumor cell lines. Simultaneously, TOH is also introduced into live tumor tissue imaging and found that the fluorescence intensity of tumor is the brightest relative to normal tissue, further validating the high expression of DNA G4s structures in tumor tissue. These features demonstrate TOH not only have the ability to image DNA G4 structures in real time, but also may have tumor diagnostic capabilities.

A computational workflow to determine drug candidates alternative to aminoglycosides targeting the decoding center of E. coli ribosome

The global antibiotic resistance problem necessitates fast and effective approaches to finding novel inhibitors to treat bacterial infections. In this study, we propose a computational workflow to identify plausible high-affinity compounds from FDA-approved, investigational, and experimental libraries for the decoding center on the small subunit 30S of the E. coli ribosome. The workflow basically consists of two molecular docking calculations on the intact 30S, followed by molecular dynamics (MD) simulations coupled with MM-GBSA calculations on a truncated ribosome structure. The parameters used in the molecular docking suits, Glide and AutoDock Vina, as well as in the MD simulations with Desmond were carefully adjusted to obtain expected interactions for the ligand-rRNA complexes. A filtering procedure was followed, considering a fingerprint based on aminoglycoside's binding site on the 30S to obtain seven hit compounds either with different clinical usages or aminoglycoside derivatives under investigation, suggested for in vitro studies. The detailed workflow developed in this study promises an effective and fast approach for the estimation of binding free energies of large protein-RNA and ligand complexes.

Navigating the complexities of docking tools with nicotinic receptors and acetylcholine binding proteins in the realm of neonicotinoids

Molecular docking, pivotal in predicting small-molecule ligand binding modes, struggles with accurately identifying binding conformations and affinities. This is particularly true for neonicotinoids, insecticides whose impacts on ecosystems require precise molecular interaction modeling. This study scrutinizes the effectiveness of prominent docking software (Ledock, ADFR, Autodock Vina, CDOCKER) in simulating interactions of environmental chemicals, especially neonicotinoid-like molecules with nicotinic acetylcholine receptors (nAChRs) and acetylcholine binding proteins (AChBPs). We aimed to assess the accuracy and reliability of these tools in reproducing crystallographic data, focusing on semi-flexible and flexible docking approaches. Our analysis identified Ledock as the most accurate in semi-flexible docking, while Autodock Vina with Vinardo scoring function proved most reliable. However, no software consistently excelled in both accuracy and reliability. Additionally, our evaluation revealed that none of the tools could establish a clear correlation between docking scores and experimental dissociation constants (Kd) for neonicotinoid-like compounds. In contrast, a strong correlation was found with drug-like compounds, bringing to light a bias in considered software towards pharmaceuticals, thus limiting their applicability to environmental chemicals. The comparison between semi-flexible and flexible docking revealed that the increased computational complexity of the latter did not result in enhanced accuracy. In fact, the higher computational cost of flexible docking with its lack of enhanced predictive accuracy, rendered this approach useless for this class of compounds. Conclusively, our findings emphasize the need for continued development of docking methodologies, particularly for environmental chemicals. This study not only illuminates current software capabilities but also underscores the urgency for advancements in computational molecular docking as it is a relevant tool to environmental sciences.

In Silico Investigation against Inhibitors of Alpha-Amylase Using Structure-based Screening, Molecular Docking, and Molecular Simulations Studies

Type-II diabetes mellitus is a chronic disorder that results from fluctuations in the glucose level leading to hyperglycemia with severe adverse effects increasing worldwide. Alpha-Amylase is the key enzyme involved in the mechanism of glucose formation therefore Alpha-Amylase inhibitors have become a therapeutic target in the development of new leads as they have the potential to suppress glucose levels. Existing drugs targeting Alpha-Amylase highlight major drawbacks in terms of poor absorption rate that causes several gastrointestinal issues. So, this research is aimed to develop novel inhibitors interacting with Alpha-Amylase's active site using structural-based screening, binding pattern analysis, and molecular dynamic simulation. Hence, to search for a potential lead, we analyzed a total of 133 valiolamine derivatives and 535 desoxynojirimycin derivatives that exhibited drug-like properties screened through Lipinski filters. Virtual screening followed by binding interaction analysis we identified ten compounds that exhibited better binding energy scores compared to the standard drugs voglibose and miglitol, used in our study. The docking analysis, ADMET and metabolic site prediction estimated the best top two compounds with good drug profiles. Further, top compounds VG9 and VG15 were promoted to simulation study using the Biovia Discovery study to access the stability at a time interval of 100 ns. MD simulation results revealed that our compound VG9 possesses better conformational stability in the complex to the active site residues of Alpha-Amylase target protein than standard drug voglibose. Thus, our investigation revealed that compound VG9 also exhibits the best pharmacokinetic as well as binding affinity results and could act as a potential lead compound targeting Alpha-Amylase for Type II diabetes.

SwissDock 2024: major enhancements for small-molecule docking with Attracting Cavities and AutoDock Vina

Drug discovery aims to identify potential therapeutic compounds capable of modulating the activity of specific biological targets. Molecular docking can efficiently support this process by predicting binding interactions between small molecules and macromolecular targets and potentially accelerating screening campaigns. SwissDock is a computational tool released in 2011 as part of the SwissDrugDesign project, providing a free web-based service for small-molecule docking after automatized preparation of ligands and targets. Here, we present the latest version of SwissDock, in which EADock DSS has been replaced by two state-of-the-art docking programs, i.e. Attracting Cavities and AutoDock Vina. AutoDock Vina provides faster docking predictions, while Attracting Cavities offers more accurate results. Ligands can be imported in various ways, including as files, SMILES notation or molecular sketches. Targets can be imported as PDB files or identified by their PDB ID. In addition, advanced search options are available both for ligands and targets, giving users automatized access to widely-used databases. The web interface has been completely redesigned for interactive submission and analysis of docking results. Moreover, we developed a user-friendly command-line access which, in addition to all options of the web site, also enables covalent ligand docking with Attracting Cavities. The new version of SwissDock is freely available at https://www.swissdock.ch/.

A Robust Induced Fit Docking Approach with the Combination of the Hybrid All-Atom/United-Atom/Coarse-Grained Model and Simulated Annealing

Molecular docking remains an indispensable tool in computational biology and structure-based drug discovery. However, the correct prediction of binding poses remains a major challenge for molecular docking, especially for target proteins where a substrate binding induces significant reorganization of the active site. Here, we introduce an Induced Fit Docking (IFD) approach named AA/UA/CG-SA-IFD, which combines a hybrid All-Atom/United-Atom/Coarse-Grained model with Simulated Annealing. In this approach, the core region is represented by the All-Atom(AA) model, while the protein environment beyond the core region and the solvent are treated with either the United-Atom (UA) or the Coarse-Grained (CG) model. By combining the Elastic Network Model (ENM) for the CG region, the hybrid model ensures a reasonable description of ligand binding and the environmental effects of the protein, facilitating highly efficient and reliable sampling of ligand binding through Simulated Annealing (SA) at a high temperature. Upon validation with two testing sets, the AA/UA/CG-SA-IFD approach demonstrates remarkable accuracy and efficiency in induced fit docking, even for challenging cases where the docked poses significantly deviate from crystal structures.

Mechanism Analysis of Selenium-Containing Compounds in Alleviating Spinal Cord Injury Based on Network Pharmacology and Molecular Docking Technology

Spinal cord injury (SCI) is a severe traumatic condition in spinal surgery characterized by nerve damage in and below the injured area. Despite advancements in understanding the pathophysiology of SCI, effective clinical treatments remain elusive. Selenium compounds have become a research hotspot due to their diverse medicinal activities. Previously, our group synthesized a selenium-containing Compound 34# with significant anti-inflammatory activity. This study aimed to explore the anti-SCI effects of selenium-containing compounds using network pharmacology, molecular docking (MD), and ADMET methods. To identify SCI-related targets and those associated with 34#, GeneCards, NCBI, and SEA databases were employed. Eight overlapping targets were considered candidate targets, and molecular docking was performed using the PDB database and AutoDock software. The STRING database was used to obtain protein-protein interactions (PPI). Molecular dynamics simulation, MM/GBSA binding free energy score, and ADMET prediction were used to evaluate the potential targets and drug properties of 34#. Finally, experiments on NSC34 cells and mice were to verify the effects of 34# on SCI. Our results revealed eight candidate targets for 34# in the treatment of SCI. PPI and MD identified ADRB2 and HTR1F as the highest connectivity with 34#. ADMET analysis confirmed the low toxicity and safety of 34#. In vitro and in vivo models validated the anti-SCI effects. Our study elucidated candidate targets for alleviating SCI with 34#, explored PPI and target-related signaling pathways, and validated its anti-SCI effects. These findings enhance our understanding of 34#'s mechanism in treating SCI, positioning it as a potential candidate for SCI prevention.

GprC of the nematode-trapping fungus Arthrobotrys flagrans activates mitochondria and reprograms fungal cells for nematode hunting

Initiation of development requires differential gene expression and metabolic adaptations. Here we show in the nematode-trapping fungus, Arthrobotrys flagrans, that both are achieved through a dual-function G-protein-coupled receptor (GPCR). A. flagrans develops adhesive traps and recognizes its prey, Caenorhabditis elegans, through nematode-specific pheromones (ascarosides). Gene-expression analyses revealed that ascarosides activate the fungal GPCR, GprC, at the plasma membrane and together with the G-protein alpha subunit GasA, reprograms the cell. However, GprC and GasA also reside in mitochondria and boost respiration. This dual localization of GprC in A. flagrans resembles the localization of the cannabinoid receptor CB1 in humans. The C. elegans ascaroside-sensing GPCR, SRBC66 and GPCRs of many fungi are also predicted for dual localization, suggesting broad evolutionary conservation. An SRBC64/66-GprC chimaeric protein was functional in A. flagrans, and C. elegans SRBC64/66 and DAF38 share ascaroside-binding sites with the fungal GprC receptor, suggesting 400-million-year convergent evolution.

Molecular screening of phytocompounds targeting the interface between influenza A NS1 and TRIM25 to enhance host immune responses

BACKGROUND

Influenza A virus causes severe respiratory illnesses, especially in developing nations where most child deaths under 5 occur due to lower respiratory tract infections. The RIG-I protein acts as a sensor for viral dsRNA, triggering interferon production through K63-linked poly-ubiquitin chains synthesized by TRIM25. However, the influenza A virus's NS1 protein hinders this process by binding to TRIM25, disrupting its association with RIG-I and preventing downstream interferon signalling, contributing to the virus's evasion of the immune response.

METHODS

In our study we used structural-based drug designing, molecular simulation, and binding free energy approaches to identify the potent phytocompounds from various natural product databases (>100,000 compounds) able to inhibit the binding of NS1 with the TRIM25.

RESULTS

The molecular screening identified EA-8411902 and EA-19951545 from East African Natural Products Database, NA-390261 and NA-71 from North African Natural Products Database, SA-65230 and SA- 4477104 from South African Natural Compounds Database, NEA- 361 and NEA- 4524784 from North-East African Natural Products Database, TCM-4444713 and TCM-6056 from Traditional Chinese Medicines Database as top hits. The molecular docking and binding free energies results revealed that these compounds have high affinity with the specific active site residues (Leu95, Ser99, and Tyr89) involved in the interaction with TRIM25. Additionally, analysis of structural dynamics, binding free energy, and dissociation constants demonstrates a notably stronger binding affinity of these compounds with the NS1 protein. Moreover, all selected compounds exhibit exceptional ADMET properties, including high water solubility, gastrointestinal absorption, and an absence of hepatotoxicity, while adhering to Lipinski's rule.

CONCLUSION

Our molecular simulation findings highlight that the identified compounds demonstrate high affinity for specific active site residues involved in the NS1-TRIM25 interaction, exhibit exceptional ADMET properties, and adhere to drug-likeness criteria, thus presenting promising candidates for further development as antiviral agents against influenza A virus infections.

Quantum Mechanics-Based Fast and Reliable Prediction of Binding Pose Structures

Predicting the binding poses of docking with an accurate estimation of binding energies is highly important but very challenging in computational drug design. A quantum mechanics (QM) calculation-based docking approach considering multiple conformations and orientations of the ligand is introduced here to tackle the problem. This QM docking consists of three steps: generating an ensemble of binding poses with a conventional docking simulation, computing the binding energies with self-consistent charge density functional theory tightly binding with dispersion correction (DFTB-D) to selecting the 10 top binding modes, and optimizing the selected binding mode structures using the ONIOM(DFTB:PM7) technique to determine the binding poses. The ONIOM(DFTB-D:PM6) docking approach is tested on 121 ligand-receptor biocomplexes with the crystal structures obtained from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB). The result shows that the new method is highly satisfactory for the accurate prediction of the binding poses. The new docking method should be beneficial to structure-based drug design.

SAnDReS 2.0: Development of machine-learning models to explore the scoring function space

Classical scoring functions may exhibit low accuracy in determining ligand binding affinity for proteins. The availability of both protein-ligand structures and affinity data make it possible to develop machine-learning models focused on specific protein systems with superior predictive performance. Here, we report a new methodology named SAnDReS that combines AutoDock Vina 1.2 with 54 regression methods available in Scikit-Learn to calculate binding affinity based on protein-ligand structures. This approach allows exploration of the scoring function space. SAnDReS generates machine-learning models based on crystal, docked, and AlphaFold-generated structures. As a proof of concept, we examine the performance of SAnDReS-generated models in three case studies. For all three cases, our models outperformed classical scoring functions. Also, SAnDReS-generated models showed predictive performance close to or better than other machine-learning models such as KDEEP, CSM-lig, and ΔVinaRF20. SAnDReS 2.0 is available to download at https://github.com/azevedolab/sandres.

Dendrobium nobile active ingredient Dendrobin A against hepatocellular carcinoma via inhibiting nuclear factor kappa-B signaling

OBJECTIVE

Dendrobin A, a typical active ingredient of the traditional Chinese medicine Dendrobium nobile, has potential clinical application in cancer treatment; however, its effect and mechanism in anti-hepatocellular carcinoma (HCC) remain unsolved.

METHOD

The effects of Dendrobin A on the viability, migration, invasion, cycle, apoptosis, and epithelial-mesenchymal transition of HepG2 and SK-HEP-1 cells were verified by in vitro experiments. mRNA sequencing was performed to screen the differentially expressed genes (DEGs) of HCC cells before and after Dendrobin A treatment, following GO enrichment and KEGG signaling pathway analyses. Mechanistically, molecular docking was used to evaluate the binding of Dendrobin A with proteins p65 and p50, before further verifying the activation of nuclear factor kappa-B (NF-κB) signaling. Finally, the antiproliferative effect of Dendrobin A on HCC cells was explored through animal experiments.

RESULTS

Dendrobin A arrested cell cycle, induced apoptosis, and inhibited proliferation, migration, invasion, and blocked epithelial-mesenchymal transition in HepG2 and SK-HEP-1 cells. mRNA sequencing identified 830 DEGs, involving various biological processes. KEGG analysis highlighted NF-κB signaling. Molecular docking revealed strong binding of Dendrobin A with p65 and p50 proteins, and western blotting confirmed reduced levels of p-p65 and p-p50 in HCC cells post Dendrobin A treatment. NF-κB agonist PMA reversed Dendrobin A-inhibited cell proliferation migration and invasion. In vivo experiments showed that Dendrobin A inhibited HCC cell growth.

CONCLUSION

Our findings suggest that Dendrobin A exhibits anti-HCC properties by inhibiting the activation of the NF-κB pathway. These results provide a scientific basis for utilizing Dendrobium nobile in anti-HCC therapies.

Benchmarking compound activity prediction for real-world drug discovery applications

Identifying active compounds for target proteins is fundamental in early drug discovery. Recently, data-driven computational methods have demonstrated promising potential in predicting compound activities. However, there lacks a well-designed benchmark to comprehensively evaluate these methods from a practical perspective. To fill this gap, we propose a Compound Activity benchmark for Real-world Applications (CARA). Through carefully distinguishing assay types, designing train-test splitting schemes and selecting evaluation metrics, CARA can consider the biased distribution of current real-world compound activity data and avoid overestimation of model performances. We observed that although current models can make successful predictions for certain proportions of assays, their performances varied across different assays. In addition, evaluation of several few-shot training strategies demonstrated different performances related to task types. Overall, we provide a high-quality dataset for developing and evaluating compound activity prediction models, and the analyses in this work may inspire better applications of data-driven models in drug discovery.

Targeting SHP2 Cryptic Allosteric Sites for Effective Cancer Therapy

SHP2, a pivotal component downstream of both receptor and non-receptor tyrosine kinases, has been underscored in the progression of various human cancers and neurodevelopmental disorders. Allosteric inhibitors have been proposed to regulate its autoinhibition. However, oncogenic mutations, such as E76K, convert SHP2 into its open state, wherein the catalytic cleft becomes fully exposed to its ligands. This study elucidates the dynamic properties of SHP2 structures across different states, with a focus on the effects of oncogenic mutation on two known binding sites of allosteric inhibitors. Through extensive modeling and simulations, we further identified an alternative allosteric binding pocket in solution structures. Additional analysis provides insights into the dynamics and stability of the potential site. In addition, multi-tier screening was deployed to identify potential binders targeting the potential site. Our efforts to identify a new allosteric site contribute to community-wide initiatives developing therapies using multiple allosteric inhibitors to target distinct pockets on SHP2, in the hope of potentially inhibiting or slowing tumor growth associated with SHP2.

Effect of chlorpyrifos-exposure on the expression levels of CYP genes in Daphnia magna and examination of a possibility that an up-regulated clan 3 CYP, CYP360A8, reacts with pesticides

Daphnia magna is a test organism used for ecological risk assessments of pesticides, but little is known about the expression levels of cytochrome P450s (CYP)s and their changes after pesticide exposure in the less than 24-h-olds used for ecotoxicity tests. In this study, D. magna juveniles were exposed to 0.2 μg/L of chlorpyrifos under the conditions for acute immobilization test as specified by the OECD test guideline for 24 h, and then the gene expression was compared between the control and chlorpyrifos-exposure groups by RNA-sequencing analysis, with a focus on CYP genes. Among 38 CYP genes expressed in the control group, seven were significantly up-regulated while two were significantly down-regulated in the chlorpyrifos-exposure group. Although the sublethal concentration of chlorpyrifos did not change their expression levels so drastically (0.8 < fold change < 2.6), CY360A8 of D. magna (DmCYP360A8), which had been proposed to be responsible for metabolism of xenobiotics, was abundantly expressed in controls yet up-regulated by chlorpyrifos. Therefore, homology modeling of DmCYP360A8 was performed based on the amino acid sequence, and then molecular docking simulations with the insecticides that were indicated to be metabolized by CYPs in D. magna were conducted. The results indicated that DmCYP360A8 could contribute to the metabolism of diazinon and chlorfenapyr but not chlorpyrifos. These findings suggest that chlorpyrifos is probably detoxified by other CYP(s) including up-regulated and/or constitutively expressed one(s).

Study on the interaction between agglutinin and chondroitin sulfate and dermatan sulfate using multiple methods

In this work, the interaction of chondroitin sulfate (CS) and dermatan sulfate (DS) with plant lectins was studied by affinity capillary electrophoresis (ACE), surface plasmon resonance (SPR) technology, molecular docking simulation, and circular dichroism spectroscopy. The ACE method was used for the first time to study the interaction of Ricinus Communis Agglutinin I (RCA I), Wisteria Floribunda Lectin (WFA), and Soybean Agglutinin (SBA) with CS and DS, and the results were in good agreement with those of the SPR method. The results of experiments indicate that RCA I has a strong binding affinity with CS, and the sulfated position does not affect the relationship, but the degree of sulfation can affect the combination of RCA I with CS to some extent. However, the binding affinity with DS is very weak. This study lays the foundation for developing more specialized analysis methods for CS and DS based on RCA I.

Mould allergen Alt a 1 spiked with the micronutrient retinoic acid reduces Th2 response and ameliorates Alternaria allergy in BALB/c mice

BACKGROUND

We investigated the biological function of the mould allergen Alt a 1 as a carrier of micronutrients, such as the vitamin A metabolite retinoic acid (RA) and the influence of RA binding on its allergenicity in vitro and in vivo.

METHODS

Alt a 1-RA complex formation was analyzed in silico and in vitro. PBMCs from Alternaria-allergic donors were stimulated with Alt a 1 complexed with RA (holo-Alt a 1) or empty apo-Alt a 1 and analyzed for cytokine production and CD marker expression. Serum IgE-binding and crosslinking assays to apo- and holo-protein were correlated to B-cell epitope analysis. Female BALB/c mice already sensitized to Alt a 1 were intranasally treated with apo-Alt a 1, holo-Alt a 1 or RA alone before measuring anaphylactic response, serum antibody levels, splenic cytokines and CD marker expression.

RESULTS

In silico docking calculations and in vitro assays showed that the extent of RA binding depended on the higher quaternary state of Alt a 1. Holo-Alt a 1 loaded with RA reduced IL-13 released from PBMCs and CD3+CD4+CRTh2 cells. Complexing Alt a 1 to RA masked its IgE B-cell epitopes and reduced its IgE-binding capacity. In a therapeutic mouse model of Alternaria allergy nasal application of holo-Alt a 1, but not of apo-Alt a 1, significantly impeded the anaphylactic response, impaired splenic antigen-presenting cells and induced IL-10 production.

CONCLUSION

Holo-Alt a 1 binding to RA was able to alleviate Th2 immunity in vitro, modulate an ongoing Th2 response and prevent anaphylactic symptoms in vivo, presenting a novel option for improving allergen-specific immunotherapy in Alternaria allergy.

DiffBindFR: an SE(3) equivariant network for flexible protein-ligand docking

Molecular docking, a key technique in structure-based drug design, plays pivotal roles in protein-ligand interaction modeling, hit identification and optimization, in which accurate prediction of protein-ligand binding mode is essential. Conventional docking approaches perform well in redocking tasks with known protein binding pocket conformation in the complex state. However, in real-world docking scenario without knowing the protein binding conformation for a new ligand, accurately modeling the binding complex structure remains challenging as flexible docking is computationally expensive and inaccurate. Typical deep learning-based docking methods do not explicitly consider protein side chain conformations and fail to ensure the physical plausibility and detailed atomic interactions. In this study, we present DiffBindFR, a full-atom diffusion-based flexible docking model that operates over the product space of ligand overall movements and flexibility and pocket side chain torsion changes. We show that DiffBindFR has higher accuracy in producing native-like binding structures with physically plausible and detailed interactions than available docking methods. Furthermore, in the Apo and AlphaFold2 modeled structures, DiffBindFR demonstrates superior advantages in accurate ligand binding pose and protein binding conformation prediction, making it suitable for Apo and AlphaFold2 structure-based drug design. DiffBindFR provides a powerful flexible docking tool for modeling accurate protein-ligand binding structures.

Mechanism Analysis of OsZF8-Mediated Regulation of Rice Resistance to Sheath Blight

Transcription factors are key molecules involved in transcriptional and post-transcriptional regulation in plants and play an important regulatory role in resisting biological stress. In this study, we identified a regulatory factor, OsZF8, mediating rice response to Rhizoctonia solani (R. solani) AG1-IA infection. The expression of OsZF8 affects R. solani rice infection. OsZF8 knockout and overexpressed rice plants were constructed, and the phenotypes of mutant and wild-type (WT) plants showed that OsZF8 negatively regulated rice resistance to rice sheath blight. However, it was speculated that OsZF8 plays a regulatory role at the protein level. The interacting protein PRB1 of OsZF8 was screened using the yeast two-hybrid and bimolecular fluorescence complementation test. The results showed that OsZF8 effectively inhibited PRB1-induced cell death in tobacco cells, and molecular docking results showed that PRB1 had a strong binding effect with OsZF8. Further, the binding ability of OsZF8-PRB1 to ergosterol was significantly reduced when compared with the PRB1 protein. These findings provide new insights into elucidating the mechanism of rice resistance to rice sheath blight.

Therapeutic potentials of nonpeptidic V2R agonists for partial cNDI-causing V2R mutants

Loss-of-function mutations in the type 2 vasopressin receptor (V2R) are a major cause of congenital nephrogenic diabetes insipidus (cNDI). In the context of partial cNDI, the response to desmopressin (dDAVP) is partially, but not entirely, diminished. For those with the partial cNDI, restoration of V2R function would offer a prospective therapeutic approach. In this study, we revealed that OPC-51803 (OPC5) and its structurally related V2R agonists could functionally restore V2R mutants causing partial cNDI by inducing prolonged signal activation. The OPC5-related agonists exhibited functional selectivity by inducing signaling through the Gs-cAMP pathway while not recruiting β-arrestin1/2. We found that six cNDI-related V2R partial mutants (V882.53M, Y1283.41S, L1614.47P, T2736.37M, S3298.47R and S3338.51del) displayed varying degrees of plasma membrane expression levels and exhibited moderately impaired signaling function. Several OPC5-related agonists induced higher cAMP responses than AVP at V2R mutants after prolonged agonist stimulation, suggesting their potential effectiveness in compensating impaired V2R-mediated function. Furthermore, docking analysis revealed that the differential interaction of agonists with L3127.40 caused altered coordination of TM7, potentially contributing to the functional selectivity of signaling. These findings suggest that nonpeptide V2R agonists could hold promise as potential drug candidates for addressing partial cNDI.

Development of a Fluorescent Assay and Imidazole-Containing Inhibitors by Targeting SARS-CoV-2 Nsp13 Helicase

Nsp13, a non-structural protein belonging to the coronavirus family 1B (SF1B) helicase, exhibits 5'-3' polarity-dependent DNA or RNA unwinding using NTPs. Crucially, it serves as a key component of the viral replication-transcription complex (RTC), playing an indispensable role in the coronavirus life cycle and thereby making it a promising target for broad-spectrum antiviral therapies. The imidazole scaffold, known for its antiviral potential, has been proposed as a potential scaffold. In this study, a fluorescence-based assay was designed by labeling dsDNA substrates with a commercial fluorophore and monitoring signal changes upon Nsp13 helicase activity. Optimization and high-throughput screening validated the feasibility of this approach. In accordance with the structural characteristics of ADP, we employed a structural-based design strategy to synthesize three classes of imidazole-based compounds through substitution reaction. Through in vitro activity research, pharmacokinetic parameter analysis, and molecular docking simulation, we identified compounds A16 (IC50 = 1.25 μM) and B3 (IC50 = 0.98 μM) as potential lead antiviral compounds for further targeted drug research.

Anti-Aging Potential of the Two Major Flavonoids Occurring in Asian Water Lily Using In Vitro and In Silico Molecular Modeling Assessments

Our previous study investigated the major flavonoids and antioxidant potential of Asian water lily (Nymphaea lotus L., family Nymphaeaceae) stamens and perianth extracts. Quercetin-3-O-rhamnoside (Que-3-Rha) and kaempferol-3-O-galactoside (Kae-3-Gal) were reported as the two most prominent flavonoids found in these extracts. Many flavonoids have been reported on the skin anti-aging effect that are useful for cosmeceutical/phytopharmaceutical application. However, Que-3-Rha and Kae-3-Gal occurring in this medicinal plant have not yet been evaluated for their ability to inhibit skin-aging enzymes. Therefore, this study aimed (1) to assess the enzyme inhibitory activity of Que-3-Rha and Kae-3-Gal, and (2) to conduct molecular modeling of these compounds against critical enzymes involved in skin aging such as collagenase, elastase, and tyrosinase. In vitro enzymatic assays demonstrated that both of the two most prominent flavonoids exhibited moderate to good inhibitory activity toward these enzymes. These experimental findings were supported by molecular docking analysis, which indicated that Que-3-Rha and Kae-3-Gal showed superior binding affinity to the target enzymes compared to the positive controls. Additionally, computational predictions suggested favorable skin permeability and no severe toxicity for both compounds. The results from molecular dynamic (MD) simulation revealed that all the complexes remained stable during the 200 ns MD simulation. Structural analyses and binding free energy calculations also supported the inhibitory potential of these two flavonoids against skin-aging enzymes. In conclusion, this study provides valuable insights into the anti-aging potential of the two major flavonoids occurring in this medicinal plant, paving the way for further development of cosmeceutical/phytopharmaceutical products targeting skin aging.

Abrogation of ORF8-IRF3 binding interface with Carbon nanotube derivatives to rescue the host immune system against SARS-CoV-2 by using molecular screening and simulation approaches

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to over six million deaths worldwide. In human immune system, the type 1 interferon (IFN) pathway plays a crucial role in fighting viral infections. However, the ORF8 protein of the virus evade the immune system by interacting with IRF3, hindering its nuclear translocation and consequently downregulate the type I IFN signaling pathway. To block the binding of ORF8-IRF3 and inhibit viral pathogenesis a quick discovery of an inhibitor molecule is needed. Therefore, in the present study, the interface between the ORF8 and IRF3 was targeted on a high-affinity carbon nanotube by using computational tools. After analysis of 62 carbon nanotubes by multiple docking with the induced fit model, the top five compounds with high docking scores of - 7.94 kcal/mol, - 7.92 kcal/mol, - 7.28 kcal/mol, - 7.19 kcal/mol and - 7.09 kcal/mol (top hit1-5) were found to have inhibitory activity against the ORF8-IRF3 complex. Molecular dynamics analysis of the complexes revealed the high compactness of residues, stable binding, and strong hydrogen binding network among the ORF8-nanotubes complexes. Moreover, the total binding free energy for top hit1-5 was calculated to be - 43.21 ± 0.90 kcal/mol, - 41.17 ± 0.99 kcal/mol, - 48.85 ± 0.62 kcal/mol, - 43.49 ± 0.77 kcal/mol, and - 31.18 ± 0.78 kcal/mol respectively. These results strongly suggest that the identified top five nanotubes (hit1-5) possess significant potential for advancing and exploring innovative drug therapies. This underscores their suitability for subsequent in vivo and in vitro experiments, marking them as promising candidates worthy of further investigation.

Virtual Screening of Molecules via Neural Fingerprint-based Deep Learning Technique

A machine learning-based drug screening technique has been developed and optimized using convolutional neural network-derived fingerprints. The optimization of weights in the neural network-based fingerprinting technique was compared with fixed Morgan fingerprints in regard to binary classification on drug-target binding affinity. The assessment was carried out using six different target proteins using randomly chosen small molecules from the ZINC15 database for training. This new architecture proved to be more efficient in screening molecules that less favorably bind to specific targets and retaining molecules that favorably bind to it.

Scientific contribution

We have developed a new neural fingerprint-based screening model that has a significant ability to capture hits. Despite using a smaller dataset, this model is capable of mapping chemical space similar to other contemporary algorithms designed for molecular screening. The novelty of the present algorithm lies in the speed with which the models are trained and tuned before testing its predictive capabilities and hence is a significant step forward in the field of machine learning-embedded computational drug discovery.

Synthesis, Antimicrobial Activities, and Molecular Modeling Studies of Agents for the Sortase A Enzyme

Sortase A (SrtA) is an attractive target for developing new anti-infective drugs that aim to interfere with essential virulence mechanisms, such as adhesion to host cells and biofilm formation. Herein, twenty hydroxy, nitro, bromo, fluoro, and methoxy substituted chalcone compounds were synthesized, antimicrobial activities and molecular modeling strategies against the SrtA enzyme were investigated. The most active compounds were found to be T2, T4, and T19 against Streptococcus mutans (S. mutans) with MIC values of 1.93, 3.8, 3.94 μg/mL, and docking scores of -6.46, -6.63, -6.73 kcal/mol, respectively. Also, these three active compounds showed better activity than the chlorohexidine (CHX) (MIC value: 4.88 μg/mL, docking score: -6.29 kcal/mol) in both in vitro and in silico. Structural stability and binding free energy analysis of S.mutans SrtA with active compounds were measured by molecular dynamic (MD) simulations throughout 100 nanoseconds (ns) time. It was observed that the stability of the critical interactions between these compounds and the target enzyme was preserved. To prove further, in vivo biological evaluation studies could be conducted for the most promising precursor compounds T2, T4, and T19, and it might open new avenues to the discovery of more potent SrtA inhibitors.

MISATO: machine learning dataset of protein-ligand complexes for structure-based drug discovery

Large language models have greatly enhanced our ability to understand biology and chemistry, yet robust methods for structure-based drug discovery, quantum chemistry and structural biology are still sparse. Precise biomolecule-ligand interaction datasets are urgently needed for large language models. To address this, we present MISATO, a dataset that combines quantum mechanical properties of small molecules and associated molecular dynamics simulations of ~20,000 experimental protein-ligand complexes with extensive validation of experimental data. Starting from the existing experimental structures, semi-empirical quantum mechanics was used to systematically refine these structures. A large collection of molecular dynamics traces of protein-ligand complexes in explicit water is included, accumulating over 170 μs. We give examples of machine learning (ML) baseline models proving an improvement of accuracy by employing our data. An easy entry point for ML experts is provided to enable the next generation of drug discovery artificial intelligence models.

Sorting drug conformers in enzyme active sites: the XTB way

In the present study, we have used the MEI196 set of interaction energies to investigate low-cost computational chemistry approaches for the calculation of binding between a molecule and its environment. Density functional theory (DFT) methods, when used with the vDZP basis set, yield good agreement with the reference energies. On the other hand, semi-empirical methods are less accurate as expected. By examining different groups of systems within MEI196 that contain species of a similar nature, we find that chemical similarity leads to cancellation of errors in the calculation of relative binding energies. Importantly, the semi-empirical method GFN1-xTB (XTB1) yields reasonable results for this purpose. We have thus further assessed the performance of XTB1 for calculating relative energies of docking poses of substrates in enzyme active sites represented by cluster models or within the ONIOM protocol. The results support the observations on error cancellation. This paves the way for the use of XTB1 in parts of large-scale virtual screening workflows to accelerate the drug discovery process.

One substrate many enzymes virtual screening uncovers missing genes of carnitine biosynthesis in human and mouse

The increasing availability of experimental and computational protein structures entices their use for function prediction. Here we develop an automated procedure to identify enzymes involved in metabolic reactions by assessing substrate conformations docked to a library of protein structures. By screening AlphaFold-modeled vitamin B6-dependent enzymes, we find that a metric based on catalytically favorable conformations at the enzyme active site performs best (AUROC Score=0.84) in identifying genes associated with known reactions. Applying this procedure, we identify the mammalian gene encoding hydroxytrimethyllysine aldolase (HTMLA), the second enzyme of carnitine biosynthesis. Upon experimental validation, we find that the top-ranked candidates, serine hydroxymethyl transferase (SHMT) 1 and 2, catalyze the HTMLA reaction. However, a mouse protein absent in humans (threonine aldolase; Tha1) catalyzes the reaction more efficiently. Tha1 did not rank highest based on the AlphaFold model, but its rank improved to second place using the experimental crystal structure we determined at 2.26 Å resolution. Our findings suggest that humans have lost a gene involved in carnitine biosynthesis, with HTMLA activity of SHMT partially compensating for its function.

Discovery of Novel 5,6-Dihydro-4H-pyrido[2,3,4-de]quinazoline Irreversible Inhibitors Targeting Both Wild-Type and A775_G776insYVMA Mutated HER2 Kinases

HER2 mutations were seen in 4% of non-small-cell lung cancer (NSCLC) patients. Most of these mutations (90%) occur as an insertion mutation within the exon 20 frame, leading to the downstream activation of the PI3K-AKT and RAS/MAPK pathways. However, no targeted therapies have yet been approved worldwide. Here a novel series of highly potent HER2 inhibitors with a pyrido[2,3,4-de]quinazoline core were designed and developed. The derivatives with the pyrido[2,3,4-de]quinazoline core displayed superior efficacy of antiproliferation in BaF3 cells harboring HER2insYVMA mutation compared with afatinib and neratinib. Rat studies showed that 8a and 9a with the newly developed core have good pharmacokinetic properties with an oral bioavailability of 41.7 and 42.0%, respectively. Oral administration of 4a and 10e (30 mg/kg, QD) displayed significant antitumor efficacy in an in vivo xenograft model. We proposed promising strategies for the development of HER2insYVMA mutant inhibitors in this study.

NAFLDkb: A Knowledge Base and Platform for Drug Development against Nonalcoholic Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease with a broad spectrum of histologic manifestations. The rapidly growing prevalence and the complex pathologic mechanisms of NAFLD pose great challenges for treatment development. Despite tremendous efforts devoted to drug development, there are no FDA-approved medicines yet. Here, we present NAFLDkb, a specialized knowledge base and platform for computer-aided drug design against NAFLD. With multiperspective information curated from diverse source materials and public databases, NAFLDkb presents the associations of drug-related entities as individual knowledge graphs. Practical drug discovery tools that facilitate the utilization and expansion of NAFLDkb have also been implemented in the web interface, including chemical structure search, drug-likeness screening, knowledge-based repositioning, and research article annotation. Moreover, case studies of a knowledge graph repositioning model and a generative neural network model are presented herein, where three repositioning drug candidates and 137 novel lead-like compounds were newly established as NAFLD pharmacotherapy options reusing data records and machine learning tools in NAFLDkb, suggesting its clinical reliability and great potential in identifying novel drug-disease associations of NAFLD and generating new insights to accelerate NAFLD drug development. NAFLDkb is freely accessible at https://www.biosino.org/nafldkb and will be updated periodically with the latest findings.

Phenylspirodrimane with Moderate Reversal Effect of Multidrug Resistance Isolated from the Deep-Sea Fungus Stachybotrys sp. 3A00409

Two new phenylspirodrimanes, stachybotrins K and L (1 and 2), together with eight known analogues (3-10), were isolated from deep-sea-derived Stachybotrys sp. MCCC 3A00409. Their structures were determined by extensive NMR data and mass spectroscopic analysis. Absolute configurations of new compounds were determined through a comparison of their circular dichroism (CD) spectra with other reported compounds. The possible reversal effects of all compounds were assayed in the resistant cancer cell lines. Stachybotrysin B (8) can reverse multidrug resistance (MDR) in ABCB1-overexpression cells (KBv200, Hela/VCR) at the non-cytotoxic concentration. Doxorubicin accumulation assay and molecular-docking analysis reveal that the mechanism of its reversal MDR effect may be related to the increase in the intracellular concentration of substrate anticancer drugs.

In silico investigation of falcipain-2 inhibition by hybrid benzimidazole-thiosemicarbazone antiplasmodial agents: A molecular docking, molecular dynamics simulation, and kinetics study

The emergence of artemisinin-resistant variants of Plasmodium falciparum necessitates the urgent search for novel antimalarial drugs. In this regard, an in silico study to screen antimalarial drug candidates from a series of benzimidazole-thiosemicarbazone hybrid molecules with interesting antiplasmodial properties and explore their falcipain-2 (FP2) inhibitory potentials has been undertaken herein. FP2 is a key cysteine protease that degrades hemoglobin in Plasmodium falciparum and is an important biomolecular target in the development of antimalarial drugs. Pharmacokinetic properties, ADMET profiles, MM/GBSA-based binding free energies, reaction mechanisms, and associated barrier heights have been investigated. DFT, molecular dynamics simulation, molecular docking, and ONIOM methods were used. From the results obtained, four 4N-substituted derivatives of the hybrid molecule (E)-2-(1-(5-chloro-1H-benzo[d]imidazol-2-yl)ethylidene)hydrazine-1-carbothioamide (1A) denoted 1B, 1C, 1D, and 1E are drug-like and promising inhibitors of FP2, exhibiting remarkably small inhibitory constants (5.94 × 10-14 - 2.59 × 10-04 n M) and favorable binding free energies (-30.32 to -17.17 kcal/mol). Moreover, the ONIOM results have revealed that 1B and possibly 1C and 1D may act as covalent inhibitors of FP2. The rate-determining step of the thermodynamically favorable covalent binding mechanism occurs across a surmountable barrier height of 24.18 kcal/mol in water and 28.42 kcal/mol in diethyl ether. Our findings are useful for further experimental investigations on the antimalarial activities of the hybrid molecules studied.

Ionic liquid modification reshapes the substrate pockets of lipase to boost its stability and activity in vitamin E succinate synthesis

BACKGROUND

The relative low stability, reusability and activity of enzymes made the industrial production of vitamin E succinate (VES) can only be performed with complex processes and high cost using chemical methods. To address these issues, in the present study, an ionic liquids (ILs) modification strategy was developed to improve the activity and stability of lipases in VES synthesis.

RESULTS

The results showed that the [1-butyl-3-methyl imidazole] [N-acetyl-l-proline] ILs modified Candida rugosa lipase (CRL) has the highest modification degree (48.28%), activity (774 U g-1 ), thermostability and solvent tolerance in three selected modifiers. Additionally, after reaction condition optimization, the highest yield of VES can be improved to 95.18% at 45 °C for 15 h, which was significantly improved compared to some previous studies.

CONCLUSION

In the present study, a high-efficiency VES synthesis strategy was successfully developed via modification of lipase. Moreover, the mechanism by which ILs modification can enhance the activity and stability of lipase was investigated via both experimental and computational-aided methods. Molecular dynamics simulation suggested that ILs modification changed the geometry of Phe344 from flat to upright, which significantly reshaped and enhanced the size of substrate binding pocket of CRL. It is also agreement with our circular dichroism and fluorescence spectroscopy results, which suggested that the modification changed the secondary structure of CRL to a certain extent. The larger pocket also endowed the suitable binding pose of succinate, which made the hydrogen bonds between succinate and active site Ser209 become stronger, and thus improving the yield of VES. © 2023 Society of Chemical Industry.

Study on the structure-activity relationship of rice immunopeptides based on molecular docking

Research on food-derived immunoregulatory peptides has attracted increasing attention of scientists worldwide. However, the structure-activity relationship of rice immunopeptides was not clearly. Herein, 114 rice immunopeptides were obtained by simulating the enzymatic hydrolysis of rice proteins and were further analyzed by NetMHCIipan-4.0. Subsequently, the molecular docking was used to simulate the binding of immunoreactive peptides to major histocompatibility complex class II (MHC-II) molecules. Results show that S, R, D, E, and T amino acid could easily form hydrogen bonds with MHC-II molecules, thus enhancing innate and adaptive immunity. Finally, glucose-modified rice immunopeptides were to investigate the binding of the peptides with MHC-II molecules after glycosylation modification; this provided a theoretical basis for the targeted modification of the generated immunopeptides. All in all, the present study provides a theoretical foundation to further utilize rice processing byproducts and other food products to enhance immunity.

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

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

Golgi α-mannosidase: opposing structures of Drosophila melanogaster and novel human model using molecular dynamics simulations and docking at different pHs

The search for Golgi α-mannosidase II (GMII) potent and specific inhibitors has been a focus of many studies for the past three decades since this enzyme is a key target for cancer treatment. α-Mannosidases, such as those from Drosophila melanogaster or Jack bean, have been used as functional models of the human Golgi α-mannosidase II (hGMII) because mammalian mannosidases are difficult to purify and characterize experimentally. Meanwhile, computational studies have been seen as privileged tools able to explore assertive solutions to specific enzymes, providing molecular details of these macromolecules, their protonation states and their interactions. Thus, modelling techniques can successfully predict hGMII 3D structure with high confidence, speeding up the development of new hits. In this study, Drosophila melanogaster Golgi mannosidase II (dGMII) and a novel human model, developed in silico and equilibrated via molecular dynamics simulations, were both opposed for docking. Our findings highlight that the design of novel inhibitors should be carried out considering the human model's characteristics and the enzyme operating pH. A reliable model is evidenced, showing a good correlation between Ki/IC50 experimental data and theoretical ΔGbinding estimations in GMII, opening the possibility of optimizing the rational drug design of new derivatives.Communicated by Ramaswamy H. Sarma.

Discovery of a potent and highly selective inhibitor of SIRT6 against pancreatic cancer metastasis in vivo

Pancreatic cancer, one of the most aggressive malignancies, has no effective treatment due to the lack of targets and drugs related to tumour metastasis. SIRT6 can promote the migration of pancreatic cancer and could be a potential target for antimetastasis of pancreatic cancer. However, highly selective and potency SIRT6 inhibitor that can be used in vivo is yet to be discovered. Here, we developed a novel SIRT6 allosteric inhibitor, compound 11e, with maximal inhibitory potency and an IC50 value of 0.98 ± 0.13 μmol/L. Moreover, compound 11e exhibited significant selectivity against other histone deacetylases (HADC1‒11 and SIRT1‒3) at concentrations up to 100 μmol/L. The allosteric site and the molecular mechanism of inhibition were extensively elucidated by cocrystal complex structure and dynamic structural analyses. Importantly, we confirmed the antimetastatic function of such inhibitors in four pancreatic cancer cell lines as well as in two mouse models of pancreatic cancer liver metastasis. To our knowledge, this is the first study to reveal the in vivo effects of SIRT6 inhibitors on liver metastatic pancreatic cancer. It not only provides a promising lead compound for subsequent inhibitor development targeting SIRT6 but also provides a potential approach to address the challenge of metastasis in pancreatic cancer.

Biocatalytic cascade to polysaccharide amination

BACKGROUND

Chitin, the main form of aminated polysaccharide in nature, is a biocompatible, polycationic, and antimicrobial biopolymer used extensively in industrial processes. Despite the abundance of chitin, applications thereof are hampered by difficulties in feedstock harvesting and limited structural versatility. To address these problems, we proposed a two-step cascade employing carbohydrate oxidoreductases and amine transaminases for plant polysaccharide aminations via one-pot reactions. Using a galactose oxidase from Fusarium graminearum for oxidation, this study compared the performance of CvATA (from Chromobacterium violaceum) and SpATA (from Silicibacter pomeroyi) on a range of oxidized carbohydrates with various structures and sizes. Using a rational enzyme engineering approach, four point mutations were introduced on the SpATA surface, and their effects on enzyme activity were evaluated.

RESULTS

Herein, a quantitative colorimetric assay was developed to enable simple and accurate time-course measurement of the yield of transamination reactions. With higher operational stability, SpATA produced higher product yields in 36 h reactions despite its lower initial activity. Successful amination of oxidized galactomannan by SpATA was confirmed using a deuterium labeling method; higher aminated carbohydrate yields achieved with SpATA compared to CvATA were verified using HPLC and XPS. By balancing the oxidase and transaminase loadings, improved operating conditions were identified where the side product formation was largely suppressed without negatively impacting the product yield. SpATA mutants with multiple alanine substitutions besides E407A showed improved product yield. The E407A mutation reduced SpATA activity substantially, supporting its predicted role in maintaining the dimeric enzyme structure.

CONCLUSIONS

Using oxidase-amine transaminase cascades, the study demonstrated a fully enzymatic route to polysaccharide amination. Although the activity of SpATA may be further improved via enzyme engineering, the low operational stability of characterized amine transaminases, as a result of low retention of PMP cofactors, was identified as a key factor limiting the yield of the designed cascade. To increase the process feasibility, future efforts to engineer improved SpATA variants should focus on improving the cofactor affinity, and thus the operational stability of the enzyme.

Peptide-Conjugated MRI Probe Targeted to Netrin-1, a Novel Metastatic Breast Cancer Biomarker

Despite significant progress in cancer imaging and treatment over the years, early diagnosis and metastasis detection remain a challenge. Molecular magnetic resonance imaging (MRI), with its high resolution, can be well adapted to fulfill this need, requiring the design of contrast agents which target specific tumor biomarkers. Netrin-1 is an extracellular protein overexpressed in metastatic breast cancer and implicated in tumor progression and the appearance of metastasis. This study focuses on the design and preclinical evaluation of a novel Netrin-1-specific peptide-based MRI probe, GdDOTA-KKTHDAVR (Gd-K), to visualize metastatic breast cancer. The targeting peptide sequence was identified based on the X-ray structure of the complex between Netrin-1 and its transmembrane receptor DCC. Molecular docking simulations support the probe design. In vitro studies evidenced submicromolar affinity of Gd-K for Netrin-1 (KD = 0.29 μM) and good MRI efficacy (proton relaxivity, r1 = 4.75 mM-1 s-1 at 9.4 T, 37 °C). In vivo MRI studies in a murine model of triple-negative metastatic breast cancer revealed successful tumor visualization at earlier stages of tumor development (smaller tumor volume). Excellent signal enhancement, 120% at 2 min and 70% up to 35 min post injection, was achieved (0.2 mmol/kg injected dose), representing a reasonable imaging time window and a superior contrast enhancement in the tumor as compared to Dotarem injection.

Exploring the substrate stereoselectivity and catalytic mechanism of nonribosomal peptide macrocyclization in surugamides biosynthesis

SurE, the first reported penicillin-binding protein-like thioesterase (PBP-like TE), is known as a new off-loading cyclase, which catalyzes heterochiral coupling in nonribosomal peptides (NRPs). However, the structural rationale for substrate stereoselectivity and enzymatic mechanism remains mysterious. Here, computational models, integrating MD simulations and QM/MM methods, unveiled SurE's substrate recognition and catalytic process. An oxyanion hole stabilized the C-terminal D-residue during recognition. Residue R446 anchored the substrate for macrocyclization. A vital hydrogen-bonding network (Y154, K66, N156), verified by mutation results, was responsible for the recognition of N-terminal L-residue and involvement in catalytic process with a calculated 19.4 kcal/mol energy barrier. Four novel-designed peptide precursors were effectively cyclized into cyclopeptides by SurE based on computational analysis. Our results provide a comprehensive understanding of SurE's catalytic mechanism and guiding design of versatile PBP-like TEs for novel macrocyclic NRPs.

Pterostilbene upregulates MICA/B via the PI3K/AKT signaling pathway to enhance the capability of natural killer cells to kill cervical cancer cells

Natural killer (NK) cells are triggered by the innate immune response in the tumor microenvironment. The extensive set of stimulating and inhibiting receptors mediates the target recognition of NK cells, and controls the strength of the effector reaction countering specific targeted cells. Yet, lacking major MHC (histocompatibility complex) MICA/B class I chain-related proteins on the membrane of tumor cells results in the failure of NK cell recognition and ability to resist NK cell destruction. Searching databases and molecular docking suggested that in cervical cancer, pterostilbene (3,5-dimethoxy-40-hydroxystilbene; PTS) in Vaccinium corymbosum extract could constrain PI3K/AKT signaling and improving the MICA/B expression. In flow cytometry, MTT assay, viability/cytotoxicity assay, and colony development assays, PTS reduced the development of cervical cancer cells and increased apoptosis. The quantitative real-time PCR (qRT-PCR) and a Western blot indicate that PTS controlled the cytolytic action of NK cells in tumor cells via increasing the MICA/B expression, thus modifying the anti-tumor immune response in cervical cancer.

Studies on the selectivity of the SARS-CoV-2 papain-like protease reveal the importance of the P2' proline of the viral polyprotein

The SARS-CoV-2 papain-like protease (PLpro) is an antiviral drug target that catalyzes the hydrolysis of the viral polyproteins pp1a/1ab, so releasing the non-structural proteins (nsps) 1-3 that are essential for the coronavirus lifecycle. The LXGG↓X motif in pp1a/1ab is crucial for recognition and cleavage by PLpro. We describe molecular dynamics, docking, and quantum mechanics/molecular mechanics (QM/MM) calculations to investigate how oligopeptide substrates derived from the viral polyprotein bind to PLpro. The results reveal how the substrate sequence affects the efficiency of PLpro-catalyzed hydrolysis. In particular, a proline at the P2' position promotes catalysis, as validated by residue substitutions and mass spectrometry-based analyses. Analysis of PLpro catalyzed hydrolysis of LXGG motif-containing oligopeptides derived from human proteins suggests that factors beyond the LXGG motif and the presence of a proline residue at P2' contribute to catalytic efficiency, possibly reflecting the promiscuity of PLpro. The results will help in identifying PLpro substrates and guiding inhibitor design.

Docking and other computing tools in drug design against SARS-CoV-2

The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.

Computational Approaches to Evaluate the Acetylcholinesterase Binding Interaction with Taxifolin for the Management of Alzheimer's Disease

Acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) are enzymes that break down and reduce the level of the neurotransmitter acetylcholine (ACh). This can cause a variety of cognitive and neurological problems, including Alzheimer's disease. Taxifolin is a natural phytochemical generally found in yew tree bark and has significant pharmacological properties, such as being anti-cancer, anti-inflammatory, and antioxidant. The binding affinity and inhibitory potency of taxifolin to these enzymes were evaluated through molecular docking and molecular dynamics simulations followed by the MMPBSA approach, and the results were significant. Taxifolin's affinity for binding to the AChE-taxifolin complex was -8.85 kcal/mol, with an inhibition constant of 326.70 nM. It was observed to interact through hydrogen bonds. In contrast, the BChE-taxifolin complex binding energy was observed to be -7.42 kcal/mol, and it was significantly nearly equal to the standard inhibitor donepezil. The molecular dynamics and simulation signified the observed interactions of taxifolin with the studied enzymes. The MMPBSA total free energy of binding for AChE-taxifolin was -24.34 kcal/mol, while BChE-taxifolin was -16.14 kcal/mol. The present research suggests that taxifolin has a strong ability to bind and inhibit AChE and BChE and could be used to manage neuron-associated problems; however, further research is required to explore taxifolin's neurological therapeutic potential using animal models of Alzheimer's disease.

Homology Modeling, Molecular Dynamics Simulation, and Prediction of Bovine TLR2 Heterodimerization

Toll-like receptor 2 (TLR2) is a major membrane-bound receptor with ligand and species specificity that activates the host immune response. Heterodimerization of TLR2 with TLR1 (TLR2/1) or TLR6 (TLR2/6), triggered by ligand binding, is essential to initiating the signaling pathway. Bovine TLR2 (bTLR2) heterodimerization has not been defined yet compared with human and mouse TLR2s (hTLR2 and mTLR2). The aim of the present study was to model bovine TLRs (TLRs 1, 2 and 6) and create the heterodimeric forms of the bovine TLR2 using molecular dynamics (MD) simulations. We compared the intermolecular interactions in bTLR2/1-PAM3 and bTLR2/6-PAM2 with the hTLR2 and mTLR2 complexes through docking simulations and subsequent MD analyses. The present computational findings showed that bTLR2 dimerization could have a biological function and activate the immune response, similar to hTLR2 and mTLR2. Agonists and antagonists that are designed for hTLR2 and mTLR2 can target bTLR2. However, the experimental approaches to comparing the functional immune response of TLR2 across species were missing in the present study. This computational study provides a structural analysis of the bTLR2 interaction with bTLR1 and bTLR6 in the presence of an agonist/antagonist and reveals the three-dimensional structure of bTLR2 dimerization. The present findings could guide future experimental studies targeting bTLR2 with different ligands and lipopeptides.

Elucidating the binding mechanism of SARS-CoV-2 NSP6-TBK1 and structure-based designing of phytocompounds inhibitors for instigating the host immune response

SARS-CoV-2, also referred to as severe acute respiratory syndrome coronavirus 2, is the virus responsible for causing COVID-19, an infectious disease that emerged in Wuhan, China, in December 2019. Among its crucial functions, NSP6 plays a vital role in evading the human immune system by directly interacting with a receptor called TANK-binding kinase (TBK1), leading to the suppression of IFNβ production. Consequently, in the present study we used the structural and biophysical approaches to analyze the effect of newly emerged mutations on the binding of NSP6 and TBK1. Among the identified mutations, four (F35G, L37F, L125F, and I162T) were found to significantly destabilize the structure of NSP6. Furthermore, the molecular docking analysis highlighted that the mutant NSP6 displayed its highest binding affinity with TBK1, exhibiting docking scores of -1436.2 for the wildtype and -1723.2, -1788.6, -1510.2, and -1551.7 for the F35G, L37F, L125F, and I162T mutants, respectively. This suggests the potential for an enhanced immune system evasion capability of NSP6. Particularly, the F35G mutation exhibited the strongest binding affinity, supported by a calculated binding free energy of -172.19 kcal/mol. To disrupt the binding between NSP6 and TBK1, we conducted virtual drug screening to develop a novel inhibitor derived from natural products. From this screening, we identified the top 5 hit compounds as the most promising candidates with a docking score of -6.59 kcal/mol, -6.52 kcal/mol, -6.32 kcal/mol, -6.22 kcal/mol, and -6.21 kcal/mol. The molecular dynamic simulation of top 3 hits further verified the dynamic stability of drugs-NSP6 complexes. In conclusion, this study provides valuable insight into the higher infectivity of the SARS-CoV-2 new variants and a strong rationale for the development of novel drugs against NSP6.

Mutational analysis of SARS-CoV-2 ORF6-KPNA2 binding interface and identification of potent small molecule inhibitors to recuse the host immune system

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) surfaced on 31 December, 2019, and was identified as the causative agent of the global COVID-19 pandemic, leading to a pneumonia-like disease. One of its accessory proteins, ORF6, has been found to play a critical role in immune evasion by interacting with KPNA2 to antagonize IFN signaling and production pathways, resulting in the inhibition of IRF3 and STAT1 nuclear translocation. Since various mutations have been observed in ORF6, therefore, a comparative binding, biophysical, and structural analysis was used to reveal how these mutations affect the virus's ability to evade the human immune system. Among the identified mutations, the V9F, V24A, W27L, and I33T, were found to have a highly destabilizing effect on the protein structure of ORF6. Additionally, the molecular docking analysis of wildtype and mutant ORF6 and KPNA2 revealed the docking score of - 53.72 kcal/mol for wildtype while, -267.90 kcal/mol, -258.41kcal/mol, -254.51 kcal/mol and -268.79 kcal/mol for V9F, V24A, W27L, and I33T respectively. As compared to the wildtype the V9F showed a stronger binding affinity with KPNA2 which is further verified by the binding free energy (-42.28 kcal/mol) calculation. Furthermore, to halt the binding interface of the ORF6-KPNA2 complex, we used a computational molecular search of potential natural products. A multi-step virtual screening of the African natural database identified the top 5 compounds with best docking scores of -6.40 kcal/mol, -6.10 kcal/mol, -6.09 kcal/mol, -6.06 kcal/mol, and -6.03 kcal/mol for tophit1-5 respectively. Subsequent all-atoms simulations of these top hits revealed consistent dynamics, indicating their stability and their potential to interact effectively with the interface residues. In conclusion, our study represents the first attempt to establish a foundation for understanding the heightened infectivity of new SARS-CoV-2 variants and provides a strong impetus for the development of novel drugs against them.

Exploring the natural products chemical space to abrogate the F3L-dsRNA interface of monkeypox virus to enhance the immune responses using molecular screening and free energy calculations

Amid the ongoing monkeypox outbreak, there is an urgent need for the rapid development of effective therapeutic interventions capable of countering the immune evasion mechanisms employed by the monkeypox virus (MPXV). The evasion strategy involves the binding of the F3L protein to dsRNA, resulting in diminished interferon (IFN) production. Consequently, our current research focuses on utilizing virtual drug screening techniques to target the RNA binding domain of the F3L protein. Out of the 954 compounds within the South African natural compound database, only four demonstrated notable docking scores: -6.55, -6.47, -6.37, and -6.35 kcal/mol. The dissociation constant (KD) analysis revealed a stronger binding affinity of the top hits 1-4 (-5.34, -5.32, -5.29, and -5.36 kcal/mol) with the F3L in the MPXV. All-atom simulations of the top-ranked hits 1 to 4 consistently exhibited stable dynamics, suggesting their potential to interact effectively with interface residues. This was further substantiated through analyses of parameters such as radius of gyration (Rg), Root Mean Square Fluctuation, and hydrogen bonding. Cumulative assessments of binding free energy confirmed the top-performing candidates among all the compounds, with values of -35.90, -52.74, -28.17, and -32.11 kcal/mol for top hits 1-4, respectively. These results indicate that compounds top hit 1-4 could hold significant promise for advancing innovative drug therapies, suggesting their suitability for both in vivo and in vitro experiments.

Computational Molecular Docking and Simulation-Based Assessment of Anti-Inflammatory Properties of Nyctanthes arbor-tristis Linn Phytochemicals

The leaves, flowers, seeds, and bark of the Nyctanthes arbor-tristis Linn plant have been pharmacologically evaluated to signify the medicinal importance traditionally described for various ailments. We evaluated the anti-inflammatory potentials of 26 natural compounds using AutoDock 4.2 and Molecular Dynamics (MDS) performed with the GROMACS tool. SwissADME evaluated ADME (adsorption, distribution, metabolism, and excretion) parameters. Arb_E and Beta-sito, natural compounds of the plant, showed significant levels of binding affinity against COX-1, COX-2, PDE4, PDE7, IL-17A, IL-17D, TNF-α, IL-1β, prostaglandin E2, and prostaglandin F synthase. The control drug celecoxib exhibited a binding energy of -9.29 kcal/mol, and among the tested compounds, Arb_E was the most significant (docking energy: -10.26 kcal/mol). Beta_sito was also observed with high and considerable docking energy of -8.86 kcal/mol with the COX-2 receptor. COX-2 simulation in the presence of Arb_E and control drug celecoxib, RMSD ranged from 0.15 to 0.25 nm, showing stability until the end of the simulation. Also, MM-PBSA analysis showed that Arb_E bound to COX-2 exhibited the lowest binding energy of -277.602 kJ/mol. Arb_E and Beta_sito showed interesting ADME physico-chemical and drug-like characteristics with significant drug-like effects. Therefore, the studied natural compounds could be potential anti-inflammatory molecules and need further in vitro/in vivo experimentation to develop novel anti-inflammatory drugs.

Small-molecule activators of a bacterial signaling pathway inhibit virulence

The Burkholderia genus encompasses multiple human pathogens, including potential bioterrorism agents, that are often extensively antibiotic resistant. The FixLJ pathway in Burkholderia is a two-component system that regulates virulence. Previous work showed that fixLJ mutations arising during chronic infection confer increased virulence while decreasing the activity of the FixLJ pathway. We hypothesized that small-molecule activators of the FixLJ pathway could serve as anti-virulence therapies. Here, we developed a high-throughput assay that screened over 28,000 compounds and identified 11 that could specifically active the FixLJ pathway. Eight of these compounds, denoted Burkholderia Fix Activator (BFA) 1-8, inhibited the intracellular survival of Burkholderia in THP-1-dervived macrophages in a fixLJ-dependent manner without significant toxicity. One of the compounds, BFA1, inhibited the intracellular survival in macrophages of multiple Burkholderia species. Predictive modeling of the interaction of BFA1 with Burkholderia FixL suggests that BFA1 binds to the putative ATP/ADP binding pocket in the kinase domain, indicating a potential mechanism for pathway activation. These results indicate that small-molecule FixLJ pathway activators are promising anti-virulence agents for Burkholderia and define a new paradigm for antibacterial therapeutic discovery.