Autodock Vina Adopts More Accurate Binding Poses but Autodock4 Forms Better Binding Affinity

Mechanisms underlying the therapeutic effects of Gang Huo Qing wen granules in the treatment of influenza based on network pharmacology, molecular docking and molecular dynamics

Influenza (Flu) is a severe health, medical, and economic problem, but no medication that has excellent outcomes and lowers the occurrence of these problems is now available. GanghuoQingwenGranules (GHQWG) is a common Chinese herbal formula for the treatment of influenza (flu). However, its methods of action remain unknown. We used network pharmacology, molecular docking, and molecular dynamics simulation techniques to investigate the pharmacological mechanism of GHQWG in flu. TCMSP and various types of literature were used to obtain active molecules and targets of GHQWG. Flu-related targets were found in the Online Mendelian Inheritance in Man (OMIM) database, the DisFeNET database, the Therapeutic Target Database (TTD), and the DrugBank database. To screen the key targets, a protein-protein interaction (PPI) network was constructed. DAVID was used to analyze GO and KEGG pathway enrichment. Target tissue and organ distribution was assessed. Molecular docking was used to evaluate interactions between possible targets and active molecules. For the ideal core protein-compound complexes obtained using molecular docking, a molecular dynamics simulation was performed. In total, 90 active molecules and 312 GHQWG targets were discovered. The PPI network's topology highlighted six key targets. GHQWG's effects are mediated via genes involved in inflammation, apoptosis, and oxidative stress, as well as the TNF and IL-17 signaling pathways, according to GO and KEGG pathway enrichment analysis. Molecular docking and molecular dynamics simulations demonstrated that the active compounds and tested targets had strong binding capabilities. This analysis accurately predicts the effective components, possible targets, and pathways involved in GHQWG flu treatment. We proposed a novel study strategy for future studies on the molecular processes of GHQWG in flu treatment. Furthermore, the possible active components provide a dependable source for flu drug screening.

Transforming waste into valuables: Preparation and evaluation of dual-ligand hydrophobic charge-induction chromatography using two poor performing ligands

In conventional chromatographic ligand screening, underperforming ligands are often dismissed. However, this practice may inadvertently overlook potential opportunities. This study aims to investigate whether these underperforming ligands can be repurposed as valuable assets. Hydrophobic charge-induction chromatography (HCIC) is chosen as the validation target for its potential as an innovative chromatographic mode. A novel dual-ligand approach is employed, combining two suboptimal ligands (5-Aminobenzimidazole and Tryptamine) to explore enhanced performance and optimization prospects. Various dual-ligand HCIC resins with different ligand densities were synthesized by adjusting the ligand ratio and concentration. The resins were characterized to assess appearance, functional groups, and pore features using SEM, FTIR, and ISEC techniques. Performance assessments were conducted using single-ligand mode resins as controls, evaluating the selectivity against human immunoglobulin G and human serum albumin. Static adsorption experiments were performed to understand pH and salt influence on adsorption. Breakthrough experiments were conducted to assess dynamic adsorption capacity of the novel resin. Finally, chromatographic separation using human serum was performed to evaluate the purity and yield of the resin. Results indicated that the dual-ligand HCIC resin designed for human antibodies demonstrates exceptional selectivity, surpassing not only single ligand states but also outperforming certain high-performing ligand types, particularly under specific salt and pH conditions. Ultimately, a high yield of 83.9 % and purity of 96.7 % were achieved in the separation of hIgG from human serum with the dual-ligand HCIC, significantly superior to the single-ligand resins. In conclusion, through rational design and proper operational conditions, the dual-ligand mode can revitalize underutilized ligands, potentially introducing novel and promising chromatographic modes.

MedChemExpress compounds prevent neuraminidase N1 via physics- and knowledge-based methods

Influenza A viruses spread out worldwide, causing several global concerns. Hence, discovering neuraminidase inhibitors to prevent the influenza A virus is of great interest. In this work, a machine learning model was employed to evaluate the ligand-binding affinity of ca. 10 000 compounds from the MedChemExpress (MCE) database for inhibiting neuraminidase. Atomistic simulations, including molecular docking and molecular dynamics simulations, then confirmed the ligand-binding affinity. Furthermore, we clarified the physical insights into the binding process of ligands to neuraminidase. It was found that five compounds, including micronomicin, didesmethyl cariprazine, argatroban, Kgp-IN-1, and AY 9944, are able to inhibit neuraminidase N1 of the influenza A virus. Ten residues, including Glu119, Asp151, Arg152, Trp179, Gln228, Glu277, Glu278, Arg293, Asn295, and Tyr402, may be very important in controlling the ligand-binding process to N1.

Identification of Potential Inhibitors of the SARS-CoV-2 NSP13 Helicase via Structure-Based Ligand Design, Molecular Docking and Nonequilibrium Alchemical Simulations

We have assembled a computational pipeline based on virtual screening, docking techniques, and nonequilibrium molecular dynamics simulations, with the goal of identifying possible inhibitors of the SARS-CoV-2 NSP13 helicase, catalyzing by ATP hydrolysis the unwinding of double or single-stranded RNA in the viral replication process inside the host cell. The druggable sites for broad-spectrum inhibitors are represented by the RNA binding sites at the 5' entrance and 3' exit of the central channel, a structural motif that is highly conserved across coronaviruses. Potential binders were first generated using structure-based ligand techniques. Their potency was estimated by using four popular docking scoring functions. Common docking hits for NSP13 were finally tested using advanced nonequilibrium alchemical techniques for binding free energy calculations on a high-performing parallel cluster. Four potential NSP13 inhibitors with potency from submicrimolar to nanomolar were finally identified.

BioProtIS: Streamlining protein-ligand interaction pipeline for analysis in genomic and transcriptomic exploration

The identification of protein-ligand interactions plays a pivotal role in elucidating biological processes and discovering potential bioproducts. Harnessing the capabilities of computational methods in drug discovery, we introduce an innovative Inverted Virtual Screening (IVS) pipeline. This pipeline Integrated molecular dynamics and docking analyses to ensure that protein structures are not only energetically favorable but also representative of stable conformations. The primary objective of this pipeline is to automate and streamline the analysis of protein-ligand interactions at both genomic and transcriptomic scales. In the contemporary post-genomic era, high-throughput computational screening for bioproducts, biological systems, and therapeutic drugs has become a cornerstone practice. This approach offers the promise of cost-effectiveness, time efficiency, and optimization of laboratory work. Nevertheless, a notable deficiency persists in the availability of efficient pipelines capable of automating the virtual screening process, seamlessly integrating input and output, and leveraging the full potential of open-source tools. To bridge this critical gap, we have developed a versatile pipeline known as BioProtIS. This tool seamlessly integrates a suite of state-of-the-art tools, including Modeller, AlphaFold, Gromacs, FPOCKET, and AutoDock Vina, thus facilitating the streamlined docking of ligands with an expansive repertoire of proteins sourced from genomes and transcriptomes, and substrates. To assess the pipeline's performance, we employed the transcriptomes of Cereus jamacaru (a cactus species) and Aspisoma lineatum (firefly), along with the genome of Homo sapiens. This integration not only improves the accuracy of ligand-protein interactions by minimizing replicability deviations but also optimizes the discovery process by enabling the simultaneous evaluation of multiple substrates. Furthermore, our pipeline accommodates distinct testing scenarios, such as blind docking or site-specific targeting, which are invaluable in applications ranging from drug repositioning to the exploration of new allosteric binding sites and toxicity assessments. BioProtIS has been designed with modularity at its core. This inherent flexibility empowers users to make custom modifications directly within the source code, tailoring the pipeline to their specific research needs. Moreover, it lays the foundation for seamless integration of diverse docking algorithms in future iterations, promising ongoing advancements in the field of computational biology. This pipeline is available for free distribution and can be download at: https://github.com/BBMDO/BioProtIS.

Morusacerane: A new gammacerane triterpenoid from the trunk of Morus Alba linn. with α-glucosidase inhibitory activity

This phytochemistry investigation on the trunk of Morus alba L. resulted in the isolation of three triterpenoids, including a new gammacerane triterpenoid - morusacerane (1); along with two known compounds of betulinic acid (2) and ursolic acid (3). The structure elucidation was thoroughly conducted based on 1D, 2D-NMR and HRESIMS spectra, followed by a comparison with existing literatures. The evaluation on α-glucosidase inhibitory exhibited the great potential of the application of these isolated compounds in diabetes treatments. The results show that morusacerane (1), betulinic acid (2), and ursolic acid (3) demonstrate the strong inhibitory with the IC50 values of 106.1, 11.12, and 7.20 μM, respectively. All of these compounds interacted well with the allosteric site enzyme α-glucosidase MAL32 through H-bonds and hydrophobic interaction.

Can Duvelisib and Eganelisib work for both cancer and COVID-19? Molecular-level insights from MD simulations and enhanced samplings

SARS-CoV-2 has caused severe illness and anxiety worldwide, evolving into more dreadful variants capable of evading the host's immunity. Cytokine storms, led by PI3Kγ, are common in cancer and SARS-CoV-2. Naturally, there is a yearning to see whether any drugs could alleviate cytokine storms for both. Upon investigation, we identified two anticancer drugs, Duvelisib and Eganelisib, that could also work against SARS-CoV-2. This report is the first to decipher their synergic therapeutic effectiveness against COVID-19 and cancer with molecular insights from atomistic simulations. In addition to PI3Kγ, these drugs exhibit specificity for the main protease among all SARS-CoV-2 targets, with significant negative binding free energies and small time-dependent conformational changes of the complexes. Complexation makes active sites and secondary structures highly mechanically stiff, with barely any deformation. Replica simulations estimated large pulling forces in enhanced sampling to dissociate the drugs from Mpro's active site. Furthermore, the radial distribution function (RDF) demonstrated that the therapeutic molecules were closest to the His41 and Cys145 catalytic dyad residues. Finally, analyses implied Duvelisib and Eganelisib as promising dual-purposed anti-COVID and anticancer drugs, potentially targeting Mpro and PI3Kγ to stop virus replication and cytokine storms concomitantly. We also distinguished hotspot residues imparting significant interactions.

Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds

To date, the COVID-19 pandemic has still been infectious around the world, continuously causing social and economic damage on a global scale. One of the most important therapeutic targets for the treatment of COVID-19 is the main protease (Mpro) of SARS-CoV-2. In this study, we combined machine-learning (ML) model with atomistic simulations to computationally search for highly promising SARS-CoV-2 Mpro inhibitors from the representative natural compounds of the National Cancer Institute (NCI) Database. First, the trained ML model was used to scan the library quickly and reliably for possible Mpro inhibitors. The ML output was then confirmed using atomistic simulations integrating molecular docking and molecular dynamic simulations with the linear interaction energy scheme. The results turned out to show that there was evidently good agreement between ML and atomistic simulations. Ten substances were proposed to be able to inhibit SARS-CoV-2 Mpro. Seven of them have high-nanomolar affinity and are very potential inhibitors. The strategy has been proven to be reliable and appropriate for fast prediction of SARS-CoV-2 Mpro inhibitors, benefiting for new emerging SARS-CoV-2 variants in the future accordingly.

Tepotinib and tivantinib as potential inhibitors for the serine/threonine kinase of the mpox virus: insights from structural bioinformatics analysis

The serine/threonine kinase (STK) plays a central role as the primary kinase in poxviruses, directing phosphoryl transfer reactions. Such reactions are pivotal for the activation of certain proteins during viral replication, assembly, and maturation. Therefore, targeting this key protein is anticipated to impede virus replication. In this work, a structural bioinformatics approach was employed to evaluate the potential of drug-like kinase inhibitors in binding to the ATP-binding pocket on the STK of the Mpox virus. Virtual screening of known kinase inhibitors revealed that the top 10 inhibitors exhibited binding affinities ranging from -8.59 to -12.05 kcal/mol. The rescoring of compounds using the deep-learning default model in GNINA was performed to predict accurate binding poses. Subsequently, the top three inhibitors underwent unbiased molecular dynamics (MD) simulations for 100 ns. Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) analysis and Principal Component Analysis (PCA) suggested tepotinib as a competitive inhibitor for Mpox virus STK as evidenced by its binding free energy and the induction of similar conformational behavior of the enzyme. Nevertheless, it is sensible to experimentally test all top 10 compounds, as scoring functions and energy calculations may not consistently align with experimental findings. These insights are poised to provide an attempt to identify an effective inhibitor for the Mpox virus.Communicated by Ramaswamy H. Sarma.

Study on the mechanism of Shugan Lidan Xiaoshi granule in preventing acute pancreatitis based on network pharmacology and molecular docking

Background

Shugan Lidan Xiaoshi granules (SLXG) is a herbal granule formulation developed by extensively modifying multiple traditional Chinese medicine compound prescriptions known for their ability to dissolve stones. It is primarily used for the prevention and treatment of cholelithiasis and possesses significant therapeutic potential in both preventing and treating acute pancreatitis. However, the preventive effects of SLXG on cholelithiasis-related complications, such as acute pancreatitis (AP), have been inadequately researched.

Methods

TCMSP database was searched to identify the active components and targets of SLXG's action. The disease gene databases (GeneCards, OMMI, PharmGKB, DrugBank) were used to retrieve the targets associated with AP. A TCM ingredient target network was then constructed by using the intersection of these two datasets. The overlapping targets underwent network analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG)and Protein-Protein Interaction (PPI) analyses. Molecular docking was performed to examine the interaction patterns between the active ingredients and central targets.

Results

A "Traditional Chinese Medicine-Component-Target" complex network consisting of 10 traditional Chinese medicines, 114 compounds, and 164 targets was constructed. GO and KEGG analysis showed that SLXG has the potential to regulate the response of oxygen-containing compounds, apoptosis, and inflammatory factors. Nine central genes were identified by the PPI network and subnetwork. IL6 was chosen as the most significant gene for molecular docking. The three active compounds of SLXG: quercetin, luteolin, and paeoniflorin, along with the active site of IL6 have a good binding ability and thus play a preventive role in AP.

Conclusion

This study provides evidence of the effective preventive role of SLXG against AP, as indicated by bioinformatics analysis. The preventive effect of SLXG is attributed to its multi-component, multi-target, and multi-pathway mechanisms. This finding provides a solid foundation for future research on the clinical application and mechanism of action of drugs.

(Re-)Viewing Role of Intracellular Glucose Beyond Extracellular Regulation of Glucose-Stimulated Insulin Secretion by Pancreatic Cells

For glucose-stimulated insulin secretion (GSIS) by pancreatic β-cells in animals, it is believed that ATP generated from glucose metabolism is primarily responsible. However, this ignores two well-established aspects in literature: (a) intracellular ATP generation from other sources resulting in an overall pool of ATP, regardless of the original source, and (b) that intracellular glucose transport is 10- to 100-fold higher than intracellular glucose phosphorylation in β-cells. The latter especially provides an earlier unaddressed, but highly appealing, observation pertaining to (at least transient) the presence of intracellular glucose molecules. Could these intracellular glucose molecules be responsible for the specificity of GSIS to glucose (instead of the widely believed ATP production from its metabolism)? In this work, we provide a comprehensive compilation of literature on glucose and GSIS using various cellular systems - all studies focus only on the extracellular role of glucose in GSIS. Further, we carried out a comprehensive analysis of differential gene expression in Mouse Insulinoma 6 (MIN6) cells, exposed to low and high extracellular glucose concentrations (EGC), from the existing whole transcriptome data. The expression of other genes involved in glycolysis, Krebs cycle, and electron transport chain was found to be unaffected by EGC, except Gapdh, Atp6v0a4, and Cox20. Remarkably, 3 upregulated genes (Atp6v0a4, Cacnb4, Kif11) in high EGC were identified to have an association with cellular secretion. Using glucose as a possible ligand for the 3 proteins, computational investigations were carried out (that will require future 'wet validation', both in vitro and in vivo, e.g., using primary islets and animal models). The glucose-affinity/binding scores (in kcal/mol) obtained were also compared with glucose binding scores for positive controls (GCK and GLUT2), along with negative controls (RPA1, KU70-80, POLA1, ACAA1A, POLR1A). The binding affinity scores of glucose molecules for the 3 proteins were found to be closer to positive controls. Therefore, we report the glucose binding ability of 3 secretion-related proteins and a possible direct role of intracellular glucose molecules in GSIS.

Comparative analysis of structural dynamics and allosteric mechanisms of RecA/Rad51 family proteins: Integrated atomistic MD simulation and network-based analysis

Homologous recombination plays a key role in double-strand break repair, stalled replication fork repair, and meiosis. The RecA/Rad51 family recombinases catalyze the DNA strand invasion reaction that occurs during homologous recombination. However, the high sequence differences between homologous groups have hindered the thoroughly studies of this ancient protein family. The dynamic mechanisms of the family, particularly at the residual level, remain poorly understood. In this work, five representative RecA/Rad51 recombinase family members from all major kingdoms of living organisms: prokaryotes, eukaryotes, archaea, and viruses, were selected to explore the molecular mechanisms behind their conserved biological significance. A variety of techniques, including all-atom molecular dynamics simulation, perturbation response scanning, and protein structure network analysis, were used to examine the flexibility and correlation of protein domains, distribution of sensors and effectors and conserved hub residues. Furthermore, the potential communication routes between the ATP-binding region and the DNA-binding region of each recombinase were identified. Our results demonstrate the conserved molecular dynamics of these recombinases in the early stage of homologous recombination, including cooperative motions between regions, conserved sensing and effecting functional residue distribution, and conserved hub residues. Meanwhile, the unique ATP-DNA communication routes of each recombinase was also revealed. These results provide new insights into the mechanism of RecA/Rad51 family proteins, and provide new theoretical guidance for the development of allosteric inhibitors and the application of RecA/Rad51 family proteins.

Dirigent isoflavene-forming PsPTS2: 3D structure, stereochemical, and kinetic characterization comparison with pterocarpan-forming PsPTS1 homolog in pea

Pea phytoalexins (-)-maackiain and (+)-pisatin have opposite C6a/C11a configurations, but biosynthetically how this occurs is unknown. Pea dirigent-protein (DP) PsPTS2 generates 7,2'-dihydroxy-4',5'-methylenedioxyisoflav-3-ene (DMDIF), and stereoselectivity toward four possible 7,2'-dihydroxy-4',5'-methylenedioxyisoflavan-4-ol (DMDI) stereoisomers was investigated. Stereoisomer configurations were determined using NMR spectroscopy, electronic circular dichroism, and molecular orbital analyses. PsPTS2 efficiently converted cis-(3R,4R)-DMDI into DMDIF 20-fold faster than the trans-(3R,4S)-isomer. The 4R-configured substrate's near β-axial OH orientation significantly enhanced its leaving group abilities in generating A-ring mono-quinone methide (QM), whereas 4S-isomer's α-equatorial-OH was a poorer leaving group. Docking simulations indicated that the 4R-configured β-axial OH was closest to Asp51, whereas 4S-isomer's α-equatorial OH was further away. Neither cis-(3S,4S)- nor trans-(3S,4R)-DMDIs were substrates, even with the former having C3/C4 stereochemistry as in (+)-pisatin. PsPTS2 used cis-(3R,4R)-7,2'-dihydroxy-4'-methoxyisoflavan-4-ol [cis-(3R,4R)-DMI] and C3/C4 stereoisomers to give 2',7-dihydroxy-4'-methoxyisoflav-3-ene (DMIF). DP homologs may exist in licorice (Glycyrrhiza pallidiflora) and tree legume Bolusanthus speciosus, as DMIF occurs in both species. PsPTS1 utilized cis-(3R,4R)-DMDI to give (-)-maackiain 2200-fold more efficiently than with cis-(3R,4R)-DMI to give (-)-medicarpin. PsPTS1 also slowly converted trans-(3S,4R)-DMDI into (+)-maackiain, reflecting the better 4R configured OH leaving group. PsPTS2 and PsPTS1 provisionally provide the means to enable differing C6a and C11a configurations in (+)-pisatin and (-)-maackiain, via identical DP-engendered mono-QM bound intermediate generation, which PsPTS2 either re-aromatizes to give DMDIF or PsPTS1 intramolecularly cyclizes to afford (-)-maackiain. Substrate docking simulations using PsPTS2 and PsPTS1 indicate cis-(3R,4R)-DMDI binds in the anti-configuration in PsPTS2 to afford DMDIF, and the syn-configuration in PsPTS1 to give maackiain.

Natural compounds inhibit Monkeypox virus methyltransferase VP39 in silico studies

VP39, an essential 2'-O-RNA methyltransferase enzyme discovered in Monkeypox virus (MPXV), plays a vital role in viral RNA replication and transcription. Inhibition of the enzyme may prevent viral replication. In this context, using a combination of molecular docking and molecular dynamics (MDs) simulations, the inhibitory ability of NCI Diversity Set VII natural compounds to VP39 protein was investigated. It should be noted that the computed binding free energy of ligand via molecular docking and linear interaction energy (LIE) approaches are in good agreement with the corresponding experiments with coefficients of R = 0.72 and 0.75, respectively. NSC 319990, NSC 196515 and NSC 376254 compounds were demonstrated that can inhibit MPVX methyltransferase VP39 protein with the similar affinity compared to available inhibitor sinefungin. Moreover, nine residues involving Gln39, Gly68, Gly72, Asp95, Arg97, Val116, Asp138, Arg140 and Asn156 may be argued that they play an important role in binding process of inhibitors to VP39.Communicated by Ramaswamy H. Sarma.

Identification of diagnostic signature, molecular subtypes, and potential drugs in allergic rhinitis based on an inflammatory response gene set

Background

Rhinitis is a complex condition characterized by various subtypes, including allergic rhinitis (AR), which involves inflammatory reactions. The objective of this research was to identify crucial genes associated with inflammatory response that are relevant for the treatment and diagnosis of AR.

Methods

We acquired the AR-related expression datasets (GSE75011 and GSE50223) from the Gene Expression Omnibus (GEO) database. In GSE75011, we compared the gene expression profiles between the HC and AR groups and identified differentially expressed genes (DEGs). By intersecting these DEGs with inflammatory response-related genes (IRGGs), resulting in the identification of differentially expressed inflammatory response-related genes (DIRRGs). Afterwards, we utilized the protein-protein interaction (PPI) network, machine learning algorithms, namely least absolute shrinkage and selection operator (LASSO) regression and random forest, to identify the signature markers. We employed a nomogram to evaluate the diagnostic effectiveness of the method, which has been confirmed through validation using GSE50223. qRT-PCR was used to confirm the expression of diagnostic genes in clinical samples. In addition, a consensus clustering method was employed to categorize patients with AR. Subsequently, extensive investigation was conducted to explore the discrepancies in gene expression, enriched functions and pathways, as well as potential therapeutic drugs among these distinct subtypes.

Results

A total of 22 DIRRGs were acquired, which participated in pathways including chemokine and TNF signaling pathway. Additionally, machine learning algorithms identified NFKBIA, HIF1A, MYC, and CCRL2 as signature genes associated with AR's inflammatory response, indicating their potential as AR biomarkers. The nomogram based on feature genes could offer clinical benefits to AR patients. We discovered two molecular subtypes, C1 and C2, and observed that the C2 subtype exhibited activation of immune- and inflammation-related pathways.

Conclusions

NFKBIA, HIF1A, MYC, and CCRL2 are the key genes involved in the inflammatory response and have the strongest association with the advancement of disease in AR. The proposed molecular subgroups could provide fresh insights for personalized treatment of AR.

Design of new molecules against cervical cancer using DFT, theoretical spectroscopy, 2D/3D-QSAR, molecular docking, pharmacophore and ADMET investigations

Cervical cancer is a major health problem of women. Hormone therapy, via aromatase inhibition, has been proposed as a promising way of blocking estrogen production as well as treating the progression of estrogen-dependent cancer. To overcome the challenging complexities of costly drug design, in-silico strategy, integrating Structure-Based Drug Design (SBDD) and Ligand-Based Drug Design (LBDD), was applied to large representative databases of 39 quinazoline and thioquinazolinone compound derivatives. Quantum chemical and physicochemical descriptors have been investigated using density functional theory (DFT) and MM2 force fields, respectively, to develop 2D-QSAR models, while CoMSIA and CoMFA descriptors were used to build 3D-QSAR models. The robustness and predictive power of the reliable models were verified, via several validation methods, leading to the design of 6 new drug-candidates. Afterwards, 2 ligands were carefully selected using virtual screening methods, taking into account the applicability domain, synthetic accessibility, and Lipinski's criteria. Molecular docking and pharmacophore modelling studies were performed to examine potential interactions with aromatase (PDB ID: 3EQM). Finally, the ADMET properties were investigated in order to select potential drug-candidates against cervical cancer for experimental in vitro and in vivo testing.

Machine Learning Application for Medicinal Chemistry: Colchicine Case, New Structures, and Anticancer Activity Prediction

In the contemporary era, the exploration of machine learning (ML) has gained widespread attention and is being leveraged to augment traditional methodologies in quantitative structure-activity relationship (QSAR) investigations. The principal objective of this research was to assess the anticancer potential of colchicine-based compounds across five distinct cell lines. This research endeavor ultimately sought to construct ML models proficient in forecasting anticancer activity as quantified by the IC50 value, while concurrently generating innovative colchicine-derived compounds. The resistance index (RI) is computed to evaluate the drug resistance exhibited by LoVo/DX cells relative to LoVo cancer cell lines. Meanwhile, the selectivity index (SI) is computed to determine the potential of a compound to demonstrate superior efficacy against tumor cells compared to its toxicity against normal cells, such as BALB/3T3. We introduce a novel ML system adept at recommending novel chemical structures predicated on known anticancer activity. Our investigation entailed the assessment of inhibitory capabilities across five cell lines, employing predictive models utilizing various algorithms, including random forest, decision tree, support vector machines, k-nearest neighbors, and multiple linear regression. The most proficient model, as determined by quality metrics, was employed to predict the anticancer activity of novel colchicine-based compounds. This methodological approach yielded the establishment of a library encompassing new colchicine-based compounds, each assigned an IC50 value. Additionally, this study resulted in the development of a validated predictive model, capable of reasonably estimating IC50 values based on molecular structure input.

Tinctoric acid A-B, two new hopan-type triterpenoids from the Vietnamese lichen, Parmotrema tinctorum (Despr. ex Nyl.) hale with α-glucosidase inhibitory activity

Two new hopan-type triterpenoids, namely tinctoric acid A-B (1-2), were isolated from the lichen Parmotrema tinctorum (Despr. ex Nyl.) Hale. Their structures were elucidated by extensive spectroscopic analyses (1D and 2D NMR). The absolute configuration at C-22 of 1 was established through DP4 probability. Compounds 1-2 were evaluated for their inhibitory activity against α-glucosidase and found to be more potent than those of positive control (acarbose, IC50 168 µM) with values IC50 74.7 and 98.2 µM, respectively. Both of these compounds interacted well with enzyme α-glucosidase MAL32 through H-bonds and hydrophobic interaction.

Conformational diversity and protein-protein interfaces in drug repurposing in Ras signaling pathway

We focus on drug repurposing in the Ras signaling pathway, considering structural similarities of protein-protein interfaces. The interfaces formed by physically interacting proteins are found from PDB if available and via PRISM (PRotein Interaction by Structural Matching) otherwise. The structural coverage of these interactions has been increased from 21 to 92% using PRISM. Multiple conformations of each protein are used to include protein dynamics and diversity. Next, we find FDA-approved drugs bound to structurally similar protein-protein interfaces. The results suggest that HIV protease inhibitors tipranavir, indinavir, and saquinavir may bind to EGFR and ERBB3/HER3 interface. Tipranavir and indinavir may also bind to EGFR and ERBB2/HER2 interface. Additionally, a drug used in Alzheimer's disease can bind to RAF1 and BRAF interface. Hence, we propose a methodology to find drugs to be potentially used for cancer using a dataset of structurally similar protein-protein interface clusters rather than pockets in a systematic way.

Biochanin A abrogates osteoclastogenesis in type 2 diabetic osteoporosis via regulating ROS/MAPK signaling pathway based on integrating molecular docking and experimental validation

BACKGROUND

There are accumulating type 2 diabetes patients who have osteoporosis simultaneously. More effective therapeutic strategies should be discovered. Biochanin A (BCA) has been indicated that can play a role in improving metabolic disorders of type 2 diabetes and preventing osteoporosis. But whether BCA can treat type 2 diabetic osteoporosis has not been studied.

PURPOSE

To investigate if the BCA can protect against type 2 diabetic osteoporosis and clarify the mechanism.

METHODS

Micro-CT and histology assays were performed to detect the trabecular bone and analyze the bone histomorphology effect of BCA. CCK-8 assay was performed to detect the toxicity of BCA. TRAcP staining, immunofluorescence and hydroxyapatite resorption assay were used to observe osteoclasts differentiation and resorptive activity. Molecular docking provided evidence about BCA regulating the MAPK axis via prediction by the algorithm. QRT-PCR and Western Blotting were utilized to detect the expression of osteoclastogenesis-related markers and MAPK signaling pathway.

RESULTS

Accumulation of bone volume after BCA treatment could be found based on the 3D reconstruction. Besides, there were fewer osteoclasts in db/db mice treated with BCA than db/db mice treated with saline. In vitro, we found that BCA hadn't toxicity in osteoclasts precursor, but also inhibited differentiation of osteoclasts. Further, we found that BCA suppresses osteoclastogenesis via ROS/MAPK signaling pathway.

CONCLUSION

BCA can prevent type 2 diabetic osteoporosis by restricting osteoclast differentiation via ROS/MAPK signaling pathway.

Free-Docking and Template-Based Docking: Physics Versus Knowledge-Based Docking

Docking methods can be used to predict the orientations of two or more molecules with respect of each other using a plethora of various algorithms, which can be based on the physics of interactions or can use information from databases and templates. The usability of these approaches depends on the type and size of the molecules, whose relative orientation will be estimated. The two most important limitations are (i) the computational cost of the prediction and (ii) the availability of the structural information for similar complexes. In general, if there is enough information about similar systems, knowledge-based and template-based methods can significantly reduce the computational cost while providing high accuracy of the prediction. However, if the information about the system topology and interactions between its partners is scarce, physics-based methods are more reliable or even the only choice. In this chapter, knowledge-, template-, and physics-based methods will be compared and briefly discussed providing examples of their usability with a special emphasis on physics-based protein-protein, protein-peptide, and protein-fullerene docking in the UNRES coarse-grained model.

Network Pharmacology Study on Herb Pair Bletilla striata-Galla chinensis in the Treatment of Chronic Skin Ulcers

BACKGROUND

Herb pair Bletilla striata-Galla chinensis (BS-GC) is a classic combination of topical traditional Chinese medicine formulae in the treatment of chronic skin ulcers (CSUs).

OBJECTIVE

The aim of this study is to explore the effective active ingredients of BS-GC, as well as the core targets and signal transduction pathways of its action on CSUs.

METHODS

The ingredients of BS-GC were obtained from TCMSP and HERB databases. The targets of all active ingredients were retrieved from the SwissTargetPrediction database. The targets of CSUs were obtained from OMIM, GeneCards, Drugbank, and DisGeNET databases. A drug-disease target protein-protein interaction (PPI) network was constructed to select the most core targets, and an herb-ingredient-target network was built by utilizing Cytoscape 3.7.2. Furthermore, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes database (KEGG) analysis and verified the results of network pharmacology through molecular docking.

RESULTS

A total of 40 active ingredients from the herb pair BS-GC were initially screened, and a total of 528 targets were retrieved. Meanwhile, the total number of CSU targets was 1032. Then, the number of common targets between BS-GC and CSUs was 107. The 13 core targets of herb pair BS-GC with CSUs were filtered out according to the PPI network, including AKT1, TNF, EGFR, BCL2, HIF1A, MMP-9, etc. The 5 main core active ingredients were 1-(4-Hydroxybenzyl)-2-methoxy-9,10-dihydrophenanthrene-4,7-diol, 1-(4- Hydroxybenzyl)-4-methoxy-9,10-dihydrophenanthrene-2,7-diol, physcion, dihydromyricetin, and myricetin. The main biological processes were inflammation, oxidative stress, and immune response, involving the AGE-RAGE signaling pathway in diabetic complications, HIF-1 signaling pathway, NF-κB signaling pathway, and calcium signaling pathway. Molecular docking results showed good binding activity between the 5 main core active ingredients and 13 core targets.

CONCLUSION

This study predicted the core targets and signal transduction pathways in the treatment of CSUs to provide a reference for further molecular mechanism research.

Epiberberine induced p53/p21-dependent G2/M cell cycle arrest and cell apoptosis in gastric cancer cells by activating γ-aminobutyric acid receptor- β3

BACKGROUND AND PURPOSE

Epiberberine (EPI) is one of the most important bioalkaloid found in the rhizome of Coptis chinensis, which has been observed to exhibit pharmaceutical effects against gastric cancer (GC). Nevertheless, the potential mechanism of EPI against GC cells still remains unclear. This study aimed to identify the core receptor on GC cells through which EPI inhibited the growth of GC cells and to explore the underlying inhibitory mechanisms.

METHODS

To identify hub receptor targets that respond to EPI treatment, RNA sequencing (RNA-Seq) data from a tumor-bearing mouse model were analyzed using bioinformatics method and molecular docking. The binding interaction between EPI and GABRB3 was validated through western blotting based-cellular thermal shift assay (WB-CETSA). To further verify the binding region between EPI and GABRB3 through circular dichroism (CD) chromatography, fragments of the extracellular and transmembrane domains of the GABRB3 protein were expressed and purified in vitro. Stable cell lines with the overexpression or knockdown of GABRB3 were established using the recombinant lentivirus system. MTT ((3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyl tetrazolium bromide)) assay, colony formation assay, invasion and migration experiments, and flow cytometry were conducted to validate the inhibitory effect of EPI on the GC cells via GABRB3. Additionally, western blotting was utilized to explore the potential inhibitory mechanisms.

RESULTS

Through the combination of multiple bioinformatics methods and molecular docking, we found that the γ-aminobutyric acid type A receptor subunit -β3 (GABRB3) might be the critical receptor target in response to EPI treatment. The results of WB-CETSA analysis indicated that EPI significantly promoted the thermostability of the GABRB3 protein. Importantly, EPI could directly bind to GABRB3 and alter the secondary structure of GABRB3 fragments similar to the natural agonist, γ-aminobutyric acid (GABA). The EPI-induced suppression of the malignant phenotype of GC cells was dependent on the presence of GABRB3. GABRB3 expression was positively correlated with TP53 in patients with GC. The binding of EPI to GABRB3 stimulated p53 accumulation in GC cells. This activated the p21/CDK1/cyclinB1 pathway, resulting in G2/M cell cycle arrest, and induced the Bcl-2/BAX/Caspase axis-dependent cell apoptosis.

CONCLUSION

This study revealed the target receptor for EPI in GC cells and provided new insights into its anticancer mechanisms.

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.

Strong and selective interactions of palmatine with G-rich sequences in TRF2 promoter; experimental and computational studies

G-rich sequences have the potential to fold into G-quadruplexes (GQs). G-quadruplexes, particularly those positioned in the regulatory regions of proto-oncogenes, have recently garnered attention in anti-cancer drug design. A thermal FRET assay was employed to conduct preliminary screening of various alkaloids, aiming to identify stronger interactions with a specific set of G-rich double-labeled oligonucleotides in both K + and Na + buffers. These oligonucleotides were derived from regions associated with Kit, Myc, Ceb, Bcl2, human telomeres, and potential G-quadruplex forming sequences found in the Nrf2 and Trf2 promoters. Palmatine generally increased the stability of different G-rich sequences into their folded GQ structures, more or less in a concentration dependent manner. The thermal stability and interaction of palmatine was further studied using transition FRET (t-FRET), CD and UV-visible spectroscopy and molecular dynamics simulation methods. Palmatine showed the strongest interaction with T RF2 in both K+ and Na+ buffers even at equimolar concentration ratio. T-FRET studies revealed that palmatine has the potential to disrupt double-strand formation by the T RF2 sequence in the presence of its complementary strand. Palmatine exhibits a stronger interaction with G-rich strand DNA, promoting its folding into G-quadruplex structures. It is noteworthy that palmatine exhibits the strongest interaction with T RF2, which is the shortest sequence among the G-rich oligonucleotides studied, featuring only one nucleotide for two of its loops. Palmatine represents a suitable structure for drug design to develop more specific ligands targeting G-quadruplexes. Whether palmatine can also affect the expression of the T RF2 gene requires further studies.Communicated by Ramaswamy H. Sarma.

Virtual screening combined with experimental verification reveals the potential mechanism of Fuzitang decoction against Gouty Arthritis

Background

and Purpose: Fuzitang decoction (FZT), a classic prescription of traditional Chinese medicine (TCM), has excellent efficacy in treating gouty arthritis (GA). However, the underlying molecular mechanism remains obscure. In the present study, we aimed to explore the underlying mechanisms of FZT in treating GA by virtual screening combined with experimental verification.

Methods

In this study, the active components of FZT and their corresponding targets were screened from the TCMSP database and TargetNet database. Then, the potential targets of FZT against GA were retrieved from multiple databases to generate a network. Protein-protein interaction, herbal-component-target, Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were applied to identify potential targets and related signaling pathways. Furthermore, molecular docking simulation was applied to identify the interactions between the drug and targets. Finally, in vitro experiments were conducted to validate the potential targets and signaling pathways.

Results

In the present study, several crucial components, including kaempferol, luteolin, catechin, deoxyandrographolide, and perlolyrine in FZT, were obtained through network pharmacology, and several potential targets to treat GA were developed, such as PPARG, CYP3A4, PTGS2 (known as COX2), VEGFA, and CYP1A1. Experimental validation suggested that deoxyandrographolide significantly suppressed the expression of IL-1β, COX2, NLRP3 and IL-6 in inflammatory monocyte cells.

Conclusions

Our results identified a novel anti-inflammatory compound, deoxyandrographolide, which helps to explain the potential mechanism of FZT in treating GA and provides evidence to support FZT's clinical use.

Steric Blockade of Oxy-Myoglobin Oxidation by Thiosemicarbazones: Structure-Activity Relationships of the Novel PPP4pT Series

The di-2-pyridylketone thiosemicarbazones demonstrated marked anticancer efficacy, prompting progression of DpC to clinical trials. However, DpC induced deleterious oxy-myoglobin oxidation, stifling development. To address this, novel substituted phenyl thiosemicarbazone (PPP4pT) analogues and their Fe(III), Cu(II), and Zn(II) complexes were prepared. The PPP4pT analogues demonstrated potent antiproliferative activity (IC50: 0.009-0.066 μM), with the 1:1 Cu:L complexes showing the greatest efficacy. Substitutions leading to decreased redox potential of the PPP4pT:Cu(II) complexes were associated with higher antiproliferative activity, while increasing potential correlated with increased redox activity. Surprisingly, there was no correlation between redox activity and antiproliferative efficacy. The PPP4pT:Fe(III) complexes attenuated oxy-myoglobin oxidation significantly more than the clinically trialed thiosemicarbazones, Triapine, COTI-2, and DpC, or earlier thiosemicarbazone series. Incorporation of phenyl- and styryl-substituents led to steric blockade, preventing approach of the PPP4pT:Fe(III) complexes to the heme plane and its oxidation. The 1:1 Cu(II):PPP4pT complexes were inert to transmetalation and did not induce oxy-myoglobin oxidation.

Understanding the impact of structural modifications at the NNAT gene's post-translational acetylation site: in silico approach for predicting its drug-interaction role in anorexia nervosa

PURPOSE

Anorexia nervosa (AN) is a neuropsychological public health concern with a socially disabling routine and affects a person's healthy relationship with food. The role of the NNAT (Neuronatin) gene in AN is well established. The impact of mutation at the protein's post-translational modification (PTM) site has been exclusively associated with the worsening of the protein's biochemical dynamics.

METHODS

To understand the relationship between genotype and phenotype, it is essential to investigate the appropriate molecular stability of protein required for proper biological functioning. In this regard, we investigated the PTM-acetylation site of the NNAT gene in terms of 19 other specific amino acid probabilities in place of wild type (WT) through various in silico algorithms. Based on the highest pathogenic impact computed through the consensus classifier tool, we generated 3 residue-specific (K59D, P, W) structurally modified 3D models of NNAT. These models were further tested through the AutoDock Vina tool to compute the molecular drug binding affinities and inhibition constant (Ki) of structural variants and WT 3D models.

RESULTS

With trained in silico machine learning algorithms and consensus classifier; the three structural modifications (K59D, P, W), which were also the most deleterious substitution at the acetylation site of the NNAT gene, showed the highest structural destabilization and decreased molecular flexibility. The validation and quality assessment of the 3D model of these structural modifications and WT were performed. They were further docked with drugs used to manage AN, it was found that the ΔGbind (kcal/mol) values and the inhibition constants (Ki) were relatively lower in structurally modified models as compared to WT.

CONCLUSION

We concluded that any future structural variation(s) at the PTM-acetylation site of the NNAT gene due to possible mutational consequences, will serve as a basis to explore its relationship with the propensity of developing AN.

LEVEL OF EVIDENCE

No level of evidence-open access bioinformatics research.

Genetic insights into obesity: in silico identification of pathogenic SNPs in MBOAT4 gene and their structural molecular dynamics consequences

Membrane Bound O-Acyltransferase Domain-Containing 4 (MBOAT4) protein catalyzes ghrelin acylation, leading to prominent ghrelin activity, hence characterizing its role as an anti-obesity target. We extracted 625 exonic SNPs from the ENSEMBL database and one phenotype-based missense mutation associated with obesity (A46T) from the HGMD (Human Gene Mutation Database). These were differentiated on deleterious missense SNPs of the MBOAT4 gene through MAF (minor allele frequency: <0.01) cut-off criteria in relation to some bioinformatics-based supervised machine learning tools. We found 8 rare-coding and harmful missense SNPs. The consensus classifier (PredictSNP) tool predicted that the SNP (G57S, C: rs561065025) was the most pathogenic. Several trained in silico algorithms have predicted decreased protein stability [ΔΔG (kcal/mol)] function in the presence of these rare-coding pathogenic mutations in the MBOAT4 gene. Then, a stereochemical quality check (i.e. validation and assessment) of the 3D model was performed, followed by a blind cavity docking approach, used to search for druggable cavities and molecular interactions with citrus flavonoids of the Rutaceae family, ranked with energetic estimations. Significant interactions with Phloretin 3',5'-Di-C-Glucoside were also observed at R304, W306, N307, A311, L314 and H338 with (iGEMDOCK: -95.82 kcal/mol and AutoDock: -7.80 kcal/mol). The RMSD values and other variables of MD simulation analyses on this protein further validated its significant interactions with the above flavonoids. The MBOAT4 gene and its molecular interactions could serve as an interventional future anti-obesity target. The current study's findings will benefit future prospects for large population-based studies and drug development, particularly for generating personalized medicine.Communicated by Ramaswamy H. Sarma.

Friedelin, a novel inhibitor of CYP17A1 in prostate cancer from Cassia tora

In prostate cancer (PC), drugs targeting CYP17A1 have shown great success in regulating PC progression. However, successful drug molecules show adverse side effects and therapeutic resistance in PC. Therefore, we proposed to discover the potent phytochemical-based inhibitor against CYP17A1 using virtual screening. In this study, a phytochemicals library of ∼13800 molecules was selected to screen the best possible inhibitors against CYP17A1. A molecular modelling approach investigated detailed intermolecular interactions, their structural stability, and binding affinity. Further, in vitro and in vivo studies were performed to confirm the anticancer activity of identified potential inhibitor against CYP17A1. Friedelin from Cassia tora (CT) is identified as the best possible inhibitor from the screened library. MD simulation study reveals stable binding of Friedelin to conserved binding pocket of CYP17A1 with higher binding affinity than studied control, that is, Orteronel. Friedelin was tested on hormone-sensitive (22Rv1) and insensitive (DU145) cell lines and the IC50 value was found to be 72.025 and 81.766 µg/ml, respectively. CT extract showed a 25.28% IC50 value against 22Rv1, ∼92.6% increase in late Apoptosis/Necrosis, and three folds decrease in early apoptosis in treated cells compared to untreated cells. Further, animal studies show a marked decrease in prostate weight by 39.6% and prostate index by 36.5%, along with a reduction in serum PSA level by 71.7% and testosterone level by 92.4% compared to the testosterone group, which was further validated with histopathological studies. Thus, we propose Friedelin and CT extract as potential leads, which could be taken further for drug development in PC.[Figure: see text]Communicated by Ramaswamy H. Sarma.

Statistical Improvement of rGILCC 1 and rPOXA 1B Laccases Activity Assay Conditions Supported by Molecular Dynamics

Laccases (E.C. 1.10.3.2) are glycoproteins widely distributed in nature. Their structural conformation includes three copper sites in their catalytic center, which are responsible for facilitating substrate oxidation, leading to the generation of H2O instead of H2O2. The measurement of laccase activity (UL-1) results may vary depending on the type of laccase, buffer, redox mediators, and substrates employed. The aim was to select the best conditions for rGILCC 1 and rPOXA 1B laccases activity assay. After sequential statistical assays, the molecular dynamics proved to support this process, and we aimed to accumulate valuable insights into the potential application of these enzymes for the degradation of novel substrates with negative environmental implications. Citrate buffer treatment T2 (CB T2) (pH 3.0 ± 0.2; λ420nm, 2 mM ABTS) had the most favorable results, with 7.315 ± 0.131 UL-1 for rGILCC 1 and 5291.665 ± 45.83 UL-1 for rPOXA 1B. The use of citrate buffer increased the enzyme affinity for ABTS since lower Km values occurred for both enzymes (1.49 × 10-2 mM for rGILCC 1 and 3.72 × 10-2 mM for rPOXA 1B) compared to those obtained in acetate buffer (5.36 × 10-2 mM for rGILCC 1 and 1.72 mM for rPOXA 1B). The molecular dynamics of GILCC 1-ABTS and POXA 1B-ABTS showed stable behavior, with root mean square deviation (RMSD) values not exceeding 2.0 Å. Enzyme activities (rGILCC 1 and rPOXA 1B) and 3D model-ABTS interactions (GILCC 1-ABTS and POXA 1B-ABTS) were under the strong influence of pH, wavelength, ions, and ABTS concentration, supported by computational studies identifying the stabilizing residues and interactions. Integration of the experimental and computational approaches yielded a comprehensive understanding of enzyme-substrate interactions, offering potential applications in environmental substrate treatments.

Novel trans-caffeate hydrazide derivatives: synthesis, inhibition of nitric oxide (NO) production and molecular docking studies

Eight new caffeyl hydrazide derivatives (4a-4h) were synthesised via a convenient esterification of caffeic acid with some substituted aryl acid hydrazides. The synthesised caffeyl derivatives were evaluated for their inhibitory effects on lipopolysaccharide (LPS)-induced nitric oxide (NO) production in RAW264.7 macrophages. The fluorobenzoylhydrazide derivatives 4f, 4 g and 4h were found to be the most powerful anti-inflammatory compounds with IC50 values ranging from 11.90 to 24.17 μM, which were more potent than the reference compound L-NMMA (IC50 32.8 μM). Additionally, synthesised compounds have been rationalised by using molecular docking studies which were performed in order to understand insights on the action mechanism of newly synthesised inhibitors against inflammatory mediator (iNOS). Obtained data indicate that compounds 4f, 4h, 4a and 4 g were observed to effectively bind to iNOS receptor with dock score values of -11.62, -10.81, -10.78 and -10.51 kcal/mol, respectively.

Anti-Viral Activity of Bioactive Molecules of Silymarin against COVID-19 via In Silico Studies

The severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection drove the global coronavirus disease 2019 (COVID-19) pandemic, causing a huge loss of human life and a negative impact on economic development. It is an urgent necessity to explore potential drugs against viruses, such as SARS-CoV-2. Silymarin, a mixture of herb-derived polyphenolic flavonoids extracted from the milk thistle, possesses potent antioxidative, anti-apoptotic, and anti-inflammatory properties. Accumulating research studies have demonstrated the killing activity of silymarin against viruses, such as dengue virus, chikungunya virus, and hepatitis C virus. However, the anti-COVID-19 mechanisms of silymarin remain unclear. In this study, multiple disciplinary approaches and methodologies were applied to evaluate the potential mechanisms of silymarin as an anti-viral agent against SARS-CoV-2 infection. In silico approaches such as molecular docking, network pharmacology, and bioinformatic methods were incorporated to assess the ligand-protein binding properties and analyze the protein-protein interaction network. The DAVID database was used to analyze gene functions, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment. TCMSP and GeneCards were used to identify drug target genes and COVID-19-related genes. Our results revealed that silymarin compounds, such as silybin A/B and silymonin, displayed triplicate functions against SARS-CoV-2 infection, including directly binding with human angiotensin-converting enzyme 2 (ACE2) to inhibit SARS-CoV-2 entry into the host cells, directly binding with viral proteins RdRp and helicase to inhibit viral replication and proliferation, and regulating host immune response to indirectly inhibit viral infection. Specifically, the targets of silymarin molecules in immune regulation were screened out, such as proinflammatory cytokines TNF and IL-6 and cell growth factors VEGFA and EGF. In addition, the molecular mechanism of drug-target protein interaction was investigated, including the binding pockets of drug molecules in human ACE2 and viral proteins, the formation of hydrogen bonds, hydrophobic interactions, and other drug-protein ligand interactions. Finally, the drug-likeness results of candidate molecules passed the criteria for drug screening. Overall, this study demonstrates the molecular mechanism of silymarin molecules against SARS-CoV-2 infection.

ReverseDock: a web server for blind docking of a single ligand to multiple protein targets using AutoDock Vina

Several platforms exist to perform molecular docking to computationally predict binders to a specific protein target from a library of ligands. The reverse, that is, docking a single ligand to various protein targets, can currently be done by very few web servers, which limits the search to a small set of pre-selected human proteins. However, the possibility to in silico predict which targets a compound identified in a high-throughput drug screen bind would help optimize and reduce the costs of the experimental workflow needed to reveal the molecular mechanism of action of a ligand. Here, we present ReverseDock, a blind docking web server based on AutoDock Vina specifically designed to allow users with no computational expertise to dock a ligand to 100 protein structures of their choice. ReverseDock increases the number and type of proteins a ligand can be docked to, making the task of in silico docking of a ligand to entire families of proteins straightforward. We envision ReverseDock will support researchers by providing the possibility to apply inverse docking computations using web browser. ReverseDock is available at: https://reversedock.biologie.uni-freiburg.de/.

Keras/TensorFlow in Drug Design for Immunity Disorders

Homeostasis of the host immune system is regulated by white blood cells with a variety of cell surface receptors for cytokines. Chemotactic cytokines (chemokines) activate their receptors to evoke the chemotaxis of immune cells in homeostatic migrations or inflammatory conditions towards inflamed tissue or pathogens. Dysregulation of the immune system leading to disorders such as allergies, autoimmune diseases, or cancer requires efficient, fast-acting drugs to minimize the long-term effects of chronic inflammation. Here, we performed structure-based virtual screening (SBVS) assisted by the Keras/TensorFlow neural network (NN) to find novel compound scaffolds acting on three chemokine receptors: CCR2, CCR3, and one CXC receptor, CXCR3. Keras/TensorFlow NN was used here not as a typically used binary classifier but as an efficient multi-class classifier that can discard not only inactive compounds but also low- or medium-activity compounds. Several compounds proposed by SBVS and NN were tested in 100 ns all-atom molecular dynamics simulations to confirm their binding affinity. To improve the basic binding affinity of the compounds, new chemical modifications were proposed. The modified compounds were compared with known antagonists of these three chemokine receptors. Known CXCR3 compounds were among the top predicted compounds; thus, the benefits of using Keras/TensorFlow in drug discovery have been shown in addition to structure-based approaches. Furthermore, we showed that Keras/TensorFlow NN can accurately predict the receptor subtype selectivity of compounds, for which SBVS often fails. We cross-tested chemokine receptor datasets retrieved from ChEMBL and curated datasets for cannabinoid receptors. The NN model trained on the cannabinoid receptor datasets retrieved from ChEMBL was the most accurate in the receptor subtype selectivity prediction. Among NN models trained on the chemokine receptor datasets, the CXCR3 model showed the highest accuracy in differentiating the receptor subtype for a given compound dataset.

Identifying Aging-Related Biomarkers and Immune Infiltration Features in Diabetic Nephropathy Using Integrative Bioinformatics Approaches and Machine-Learning Strategies

BACKGROUND

Aging plays an essential role in the development of diabetic nephropathy (DN). This study aimed to identify and verify potential aging-related genes associated with DN using bioinformatics analysis.

METHODS

To begin with, we combined the datasets from GEO microarrays (GSE104954 and GSE30528) to find the genes that were differentially expressed (DEGs) across samples from DN and healthy patient populations. By overlapping DEGs, weighted co-expression network analysis (WGCNA), and 1357 aging-related genes (ARGs), differentially expressed ARGs (DEARGs) were discovered. We next performed functional analysis to determine DEARGs' possible roles. Moreover, protein-protein interactions were examined using STRING. The hub DEARGs were identified using the CytoHubba, MCODE, and LASSO algorithms. We next used two validation datasets and Receiver Operating Characteristic (ROC) curves to determine the diagnostic significance of the hub DEARGs. RT-qPCR, meanwhile, was used to confirm the hub DEARGs' expression levels in vitro. In addition, we investigated the relationships between immune cells and hub DEARGs. Next, Gene Set Enrichment Analysis (GSEA) was used to identify each biomarker's biological role. The hub DEARGs' subcellular location and cell subpopulations were both identified and predicted using the HPA and COMPARTMENTS databases, respectively. Finally, drug-protein interactions were predicted and validated using STITCH and AutoDock Vina.

RESULTS

A total of 57 DEARGs were identified, and functional analysis reveals that they play a major role in inflammatory processes and immunomodulation in DN. In particular, aging and the AGE-RAGE signaling pathway in diabetic complications are significantly enriched. Four hub DEARGs (CCR2, VCAM1, CSF1R, and ITGAM) were further screened using the interaction network, CytoHubba, MCODE, and LASSO algorithms. The results above were further supported by validation sets, ROC curves, and RT-qPCR. According to an evaluation of immune infiltration, DN had significantly more resting mast cells and delta gamma T cells but fewer regulatory T cells and active mast cells. Four DEARGs have statistical correlations with them as well. Further investigation revealed that four DEARGs were implicated in immune cell abnormalities and regulated a wide range of immunological and inflammatory responses. Furthermore, the drug-protein interactions included four possible therapeutic medicines that target four DEARGs, and molecular docking could make this association practical.

CONCLUSIONS

This study identified four DEARGs (CCR2, VCAM1, CSF1R, and ITGAM) associated with DN, which might play a key role in the development of DN and could be potential biomarkers in DN.

Calculation of binding affinity of JAK1 inhibitors via accurately computational estimation

Janus kinase 1 (JAK1) is a tyrosine kinase that is involved in the initiation of responses to a number of different cytokine receptor families. The JAK1-dependent pathway is a therapeutic target, and several JAK inhibitors have been developed thanks to intensive research. However, since the ATP binding sites of JAK family members are quite alike, JAK1 inhibitors can thus be less selective, resulting in unanticipated adverse effects. Despite this, minor variations in the ATP-binding site have been extensively used to find a variety of small compounds with different inhibitory properties. Stronger binding affinity of JAK1 inhibitors is believed to be able to reduce the negative effects, leading to better treatment results. Therefore, a thorough computational search that can effectively identify ligands with extremely high binding affinity for JAK1 to serve as promising inhibitors is required. Here, a method combining steered-molecular dynamic (SMD) simulations with a modified linear interaction energy (LIE) model has been developed to evaluate the binding affinities of known JAK1 inhibitors. The correlation coefficient between the estimated and experimental values was 0.72 and a root-mean-square error was 0.97 kcal•mol-1, revealing that the SMD/LIE method can precisely and quickly predict the binding free energies of JAK1 inhibitors. Furthermore, three marine fungus-derived compounds, namely hansforesters E, hansforesters G and tetroazolemycins B, were identified to be particularly promising JAK1 inhibitors, accordingly. These findings show that the SMD/LIE method has a lot of promise for in silico screening of possible JAK1 inhibitors from a vast number of compounds that are now accessible.Communicated by Ramaswamy H. Sarma.

αKG-driven RNA polymerase II transcription of cyclin D1 licenses malic enzyme 2 to promote cell-cycle progression

Increased metabolic activity usually provides energy and nutrients for biomass synthesis and is indispensable for the progression of the cell cycle. Here, we find a role for α-ketoglutarate (αKG) generation in regulating cell-cycle gene transcription. A reduction in cellular αKG levels triggered by malic enzyme 2 (ME2) or isocitrate dehydrogenase 1 (IDH1) depletion leads to a pronounced arrest in G1 phase, while αKG supplementation promotes cell-cycle progression. Mechanistically, αKG directly binds to RNA polymerase II (RNAPII) and increases the level of RNAPII binding to the cyclin D1 gene promoter via promoting pre-initiation complex (PIC) assembly, consequently enhancing cyclin D1 transcription. Notably, αKG addition is sufficient to restore cyclin D1 expression in ME2- or IDH1-depleted cells, facilitating cell-cycle progression and proliferation in these cells. Therefore, our findings indicate a function of αKG in gene transcriptional regulation and cell-cycle control.

Thiazolidinedione-triazole conjugates: design, synthesis and probing of the α-amylase inhibitory potential

Aim: The primary objective of this investigation was the synthesis, spectral interpretation and evaluation of the α-amylase inhibition of rationally designed thiazolidinedione-triazole conjugates (7a-7aa). Materials & methods: The designed compounds were synthesized by stirring a mixture of thiazolidine-2,4-dione, propargyl bromide, cinnamaldehyde and azide derivatives in polyethylene glycol-400. The α-amylase inhibitory activity of the synthesized conjugates was examined by integrating in vitro and in silico studies. Results: The investigated derivatives exhibited promising α-amylase inhibitory activity, with IC50 values ranging between 0.028 and 0.088 μmol ml-1. Various computational approaches were employed to get detailed information about the inhibition mechanism. Conclusion: The thiazolidinedione-triazole conjugate 7p, with IC50 = 0.028 μmol ml-1, was identified as the best hit for inhibiting α-amylase.

Exploring hub pyroptosis-related genes, molecular subtypes, and potential drugs in ankylosing spondylitis by comprehensive bioinformatics analysis and molecular docking

BACKGROUND

Ankylosing spondylitis (AS) is a chronic inflammatory autoimmune disease, and the diagnosis and treatment of AS have been limited because its pathogenesis is still unclear. Pyroptosis is a proinflammatory type of cell death that plays an important role in the immune system. However, the relationship between pyroptosis genes and AS has never been elucidated.

METHODS

GSE73754, GSE25101, and GSE221786 datasets were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed pyroptosis-related genes (DE-PRGs) were identified by R software. Machine learning and PPI networks were used to screen key genes to construct a diagnostic model of AS. AS patients were clustered into different pyroptosis subtypes according to DE-PRGs using consensus cluster analysis and validated using principal component analysis (PCA). WGCNA was used for screening hub gene modules between two subtypes. Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used for enrichment analysis to elucidate underlying mechanisms. The ESTIMATE and CIBERSORT algorithms were used to reveal immune signatures. The connectivity map (CMAP) database was used to predict potential drugs for the treatment of AS. Molecular docking was used to calculate the binding affinity between potential drugs and the hub gene.

RESULTS

Sixteen DE-PRGs were detected in AS compared to healthy controls, and some of these genes showed a significant correlation with immune cells such as neutrophils, CD8 + T cells, and resting NK cells. Enrichment analysis showed that DE-PRGs were mainly related to pyroptosis, IL-1β, and TNF signaling pathways. The key genes (TNF, NLRC4, and GZMB) screened by machine learning and the protein-protein interaction (PPI) network were used to establish the diagnostic model of AS. ROC analysis showed that the diagnostic model had good diagnostic properties in GSE73754 (AUC: 0.881), GSE25101 (AUC: 0.797), and GSE221786 (AUC: 0.713). Using 16 DE-PRGs, AS patients were divided into C1 and C2 subtypes, and these two subtypes showed significant differences in immune infiltration. A key gene module was identified from the two subtypes using WGCNA, and enrichment analysis suggested that the module was mainly related to immune function. Three potential drugs, including ascorbic acid, RO 90-7501, and celastrol, were selected based on CMAP analysis. Cytoscape showed GZMB as the highest-scoring hub gene. Finally, molecular docking results showed that GZMB and ascorbic acid formed three hydrogen bonds, including ARG-41, LYS-40, and HIS-57 (affinity: -5.3 kcal/mol). GZMB and RO-90-7501 formed one hydrogen bond, including CYS-136 (affinity: -8.8 kcal/mol). GZMB and celastrol formed three hydrogen bonds, including TYR-94, HIS-57, and LYS-40 (affinity: -9.4 kcal/mol).

CONCLUSIONS

Our research systematically analyzed the relationship between pyroptosis and AS. Pyroptosis may play an essential role in the immune microenvironment of AS. Our findings will contribute to a further understanding of the pathogenesis of AS.

iORandLigandDB: A Website for Three-Dimensional Structure Prediction of Insect Odorant Receptors and Docking with Odorants

The use of insect-specific odorants to control the behavior of insects has always been a hot spot in research on "green" control strategies of insects. However, it is generally time-consuming and laborious to explore insect-specific odorants with traditional reverse chemical ecology methods. Here, an insect odorant receptor (OR) and ligand database website (iORandLigandDB) was developed for the specific exploration of insect-specific odorants by using deep learning algorithms. The website provides a range of specific odorants before molecular biology experiments as well as the properties of ORs in closely related insects. At present, the existing three-dimensional structures of ORs in insects and the docking data with related odorants can be retrieved from the database and further analyzed.

Comprehensive exploration of JQ1 and GSK2801 targets in breast cancer using network pharmacology and molecular modeling approaches

JQ1 and GSK2801 are bromo domain inhibitors (BDI) known to exhibit enhanced anti-cancer activity when combined with other agents. However, the underlying molecular mechanisms behind such enhanced activity remain unclear. We used network-pharmacology approaches to understand the shared molecular mechanisms behind the enhanced activity of JQ1 and GSK2801 when used together to treat breast cancer (BC). The gene targets of JQ1 and GSK2801 were intersected with known BC-targets and their putative targets against BC were derived. The key genes were explored through gene-ontology-enrichment, Protein-Protein-Interaction (PPI) networking, survival analysis, and molecular modeling simulations. The genes, CTSB, MAPK14, MET, PSEN2 and STAT3, were found to be common targets for both drugs. In total, 49 biological processes, five molecular functions and 61 metabolic pathways were similarly enriched for JQ1 and GSK2801 BC targets among which several terms are related to cancer: IL-17, TNF and JAK-STAT signaling pathways. Survival analyses revealed that all five putative synergistic targets are significantly associated with survival in BC (log-rank p < 0.05). Molecular modeling studies showed stable binding of JQ1 and GSK2801 against their targets. In conclusion, this study explored and illuminated the possible molecular mechanisms behind the enhanced activity of JQ1 and GSK2801 against BC and suggests synergistic action through their similar BC-targets and gene-ontologies.

Virtual Screening-Based Study of Novel Anti-Cancer Drugs Targeting G-Quadruplex

In order to develop new anti-cancer drugs more efficiently and reduce side effects based on active drug targets, the virtual drug screening was carried out through the target of G-quadruplexes and 23 hit compounds were, thus, screened out as potential anticancer drugs. Six classical G-quadruplex complexes were introduced as query molecules, and the three-dimensional similarity of molecules was calculated by shape feature similarity (SHAFTS) method so as to reduce the range of potential compounds. Afterwards, the molecular docking technology was utilized to perform the final screening followed by the exploration of the binding between each compound and four different structures of G-quadruplex. In order to verify the anticancer activity of the selected compounds, compounds 1, 6 and 7 were chosen to treat A549 cells in vitro, the lung cancer epithelial cells, for further exploring their anticancer activity. These three compounds were found to be of good characteristics in the treatment of cancer, which revealed the great application prospect of the virtual screening method in developing new drugs.

Identification of Catechins' Binding Sites in Monomeric Aβ42 through Ensemble Docking and MD Simulations

The assembly of the amyloid-β peptide (Aβ) into toxic oligomers and fibrils is associated with Alzheimer's disease and dementia. Therefore, disrupting amyloid assembly by direct targeting of the Aβ monomeric form with small molecules or antibodies is a promising therapeutic strategy. However, given the dynamic nature of Aβ, standard computational tools cannot be easily applied for high-throughput structure-based virtual screening in drug discovery projects. In the current study, we propose a computational pipeline-in the framework of the ensemble docking strategy-to identify catechins' binding sites in monomeric Aβ₄₂. It is shown that both hydrophobic aromatic interactions and hydrogen bonding are crucial for the binding of catechins to Aβ₄₂. Additionally, it has been found that all the studied ligands, especially EGCG, can act as potent inhibitors against amyloid aggregation by blocking the central hydrophobic region of Aβ. Our findings are evaluated and confirmed with multi-microsecond MD simulations. Finally, it is suggested that our proposed pipeline, with low computational cost in comparison with MD simulations, is a suitable approach for the virtual screening of ligand libraries against Aβ.

Bioinformatics analysis combined with molecular dynamics simulation validation to elucidate the potential molecular mechanisms of Jianshen Decoction for treatment of osteoporotic fracture

Osteoporotic fracture (OPF) is a prevalent skeletal disease in the middle-aged and elderly. In clinical practice, Jianshen Decoction (JSD) has been used to treat OPFs. However, the specific effective components and mechanisms of JSD on OPF have not been explored. Therefore, this study used bioinformatics analysis combined with molecular dynamics simulation validation to explore the molecular mechanism of JSD treatment of OPF. Public databases (TCMSP, Batman TCM) were used to find the effective active components and corresponding target proteins of JSD (screening conditions: OB ≥ 30%, drug-likeness ≥ 0.18, half-life ≥ 4). Differentially expressed genes (DEGs) related to OPF lesions were obtained based on the gene expression omnibus database (screening conditions: adjust P value < .01, | log2 FC | ≥ 1.0). The BisoGenet plug-in and the CytoNCA plug-in of Cytoscape were used to derive the potential core target proteins of JSD in the treatment of OPF. The JSD active ingredient target interaction network and the JSD-OPF target protein core network were constructed using the Cytoscape software. In addition, the R language Bioconductor package and clusterProfiler package were used to perform gene ontology (GO)/Kyoto Encylopedia Of Genes And Genome (KEGG) enrichment analysis on core genes to explain the biological functions and signal pathways of core proteins. Finally, molecular docking and molecular dynamics simulations were carried out through PyMOL, AutoDockTools 1.5.6, Vina, LeDock, Discovery Studio (DS) 2019, and other software to verify the binding ability of drug active ingredients and core target proteins. A total of 245 targets and 70 active components were identified. Through protein-protein interaction (PPI) network construction, 39 core targets were selected for further research. GO/KEGG enrichment analysis showed that the DNA-binding transcription factor binding, RNA polymerase II-specific DNA-binding transcription factor binding, MAPK signaling pathway, and ErbB signaling pathway were mainly involved. The results of molecular docking and molecular dynamics simulations supported the good interaction between MYC protein and Quercetin/Stigmasterol. In this study, bioinformatics, molecular docking, and molecular dynamics simulations were used for the first time to clarify the active components, molecular targets, and key biological pathways of JSD in the treatment of OPF, providing a theoretical basis for further research.

Computational Insights into the Dynamic Structural Features and Binding Characteristics of Recombinase UvsX Compared with RecA

RecA family recombinases are the core enzymes in the process of homologous recombination, and their normal operation ensures the stability of the genome and the healthy development of organisms. The UvsX protein from bacteriophage T4 is a member of the RecA family recombinases and plays a central role in T4 phage DNA repair and replication, which provides an important model for the biochemistry and genetics of DNA metabolism. UvsX shares a high degree of structural similarity and function with RecA, which is the most deeply studied member of the RecA family. However, the detailed molecular mechanism of UvsX has not been resolved. In this study, a comprehensive all-atom molecular dynamics simulation of the UvsX protein dimer complex was carried out in order to investigate the conformational and binding properties of UvsX in combination with ATP and DNA, and the simulation of RecA was synchronized with the property comparison learning for UvsX. This study confirmed the highly conserved molecular structure characteristics and catalytic centers of RecA and UvsX, and also discovered differences in regional conformation, volatility and the ability to bind DNA between the two proteins at different temperatures, which would be helpful for the subsequent understanding and application of related recombinases.

ME2 Promotes Hepatocellular Carcinoma Cell Migration through Pyruvate

Cancer metastasis is still a major challenge in clinical cancer treatment. The migration and invasion of cancer cells into surrounding tissues and blood vessels is the primary step in cancer metastasis. However, the underlying mechanism of regulating cell migration and invasion are not fully understood. Here, we show the role of malic enzyme 2 (ME2) in promoting human liver cancer cell lines SK-Hep1 and Huh7 cells migration and invasion. Depletion of ME2 reduces cell migration and invasion, whereas overexpression of ME2 increases cell migration and invasion. Mechanistically, ME2 promotes the production of pyruvate, which directly binds to β-catenin and increases β-catenin protein levels. Notably, pyruvate treatment restores cell migration and invasion of ME2-depleted cells. Our findings provide a mechanistic understanding of the link between ME2 and cell migration and invasion.

Discovery of 3CLpro inhibitor of SARS-CoV-2 main protease

Coronavirus main protease (3CLpro), a special cysteine protease in coronavirus family, is highly desirable in the life cycle of coronavirus. Here, molecular docking, ADMET pharmacokinetic profiles and molecular dynamics (MD) simulation were performed to develop specific 3CLpro inhibitor. The results showed that the 137 compounds originated from Chinese herbal have good binding affinity to 3CLpro. Among these, Cleomiscosin C, (+)-Norchelidonine, Protopine, Turkiyenine, Isochelidonine and Mallotucin A possessed prominent drug-likeness properties. Cleomiscosin C and Turkiyenine exhibited excellent pharmacokinetic profiles. Furthermore, the complex of Cleomiscosin C with SARS-CoV-2 main protease presented high stability. The findings in this work indicated that Cleomiscosin C is highly promising as a potential 3CLpro inhibitor, thus facilitating the development of effective drugs for COVID-19.

Lymphocyte antigen 6K signaling to aurora kinase promotes advancement of the cell cycle and the growth of cancer cells, which is inhibited by LY6K-NSC243928 interaction

Lymphocyte antigen 6K (LY6K) is a small GPI-linked protein that is normally expressed in testes. Increased expression of LY6K is significantly associated with poor survival outcomes in many solid cancers, including cancers of the breast, ovary, gastrointestinal tract, head and neck, brain, bladder, and lung. LY6K is required for ERK-AKT and TGF-β pathways in cancer cells and is required for in vivo tumor growth. In this report, we describe a novel role for LY6K in mitosis and cytokinesis through aurora B kinase and its substrate histone H3 signaling axis. Further, we describe the structural basis of the molecular interaction of small molecule NSC243928 with LY6K protein and the disruption of LY6K-aurora B signaling in cell cycle progression due to LY6K-NSC243928 interaction. Overall, disruption of LY6K function via NSC243928 led to failed cytokinesis, multinucleated cells, DNA damage, senescence, and apoptosis of cancer cells. LY6K is not required for vital organ function, thus inhibition of LY6K signaling is an ideal therapeutic approach for hard-to-treat cancers that lack targeted therapy such as triple-negative breast cancer.

Molecular docking and dynamics simulation of Orthosiphon stamineus against SGLT1 and SGLT2

Orthosiphon stamineus Benth a traditional medicine used in the treatment of diabetes and kidney diseases. Sodium-glucose co-transporter (SGLT1 and SGLT2) inhibitors are the novel group of drugs used to treat patients with type 2 diabetes mellitus. In this study 20 phytochemical compounds from Orthosiphon stamineus Benth were obtained from 3 databases viz Dr.Duke's phytochemical, Ethno botanical database and IMPPAT. They were subjected to physiochemical, drug likeliness, and ADMET and toxicity predictions. Homology modeling and molecular docking against SGLT1 and SGLT2 were performed and the stability of the selected drug molecule was validated by molecular dynamic (MD) simulation for 200 ns. Among the 20 compounds, 14-Dexo-14-O-acetylorthosiphol Y alone showed higher binding affinity with SGLT1 and SGLT2 protein with the binding energy of -9.6 and -11.4 Kcal/mol respectively and had highest affinity towards SGLT2 inhibitor. This compound also satisfied Lipinski rule of 5 and had a good ADMET profile. The compound is non-toxic to marine organisms and to normal cell lines and non-mutagenic. The RMSD value attained equilibrium at 150 ns with the stability around 4.8 Å and no significant deviation was reported from 160 to 200 ns for SGLT2. Our study suggests that 14-Dexo-14-O-acetylorthosiphol Y showed promising results against the SGLT2 and could be considered as a potent anti-diabetic drug.Communicated by Ramaswamy H. Sarma.