Pharmacophore Model Research Articles

Identification of Selective JAK3/STAT1 and CYP34A from Pyrazolopyrimidine Derivatives: A Search for Potential Drug Targets for Rheumatoid Arthritis using In-silico Drug Discovery Techniques

Objective: This study aimed to discover a novel active compound capable of effectively inhibiting JAK3/STAT1 and CYP3A4 using molecular modelling techniques, with the goal of treating autoimmune diseases such as cancer and specifically rheumatoid arthritis. The study involved modelling compounds derived from pyrazolopyrimidine, followed by screening methods to identify the most promising compounds. Moreover, this study seeks to identify potential compounds that can inhibit JAK3/STAT through molecular modelling techniques and validate the stability and affinity of the predicted molecule. Methods: Various molecular modelling techniques were employed to identify potential compounds and assess the stability and affinity of the predicted molecule. A pharmacophore hypothesis was developed to obtain crucial information about the experimental series of pyrazolopyrimidine studied, which served as the basis for designing new molecules. Additionally, ADMET was utilized to predict and evaluate the pharmacokinetic properties and potential toxicity of the compound prior to synthesis or utilization. To determine the essential residues involved in the interaction between the molecule and the target JAK3 protein, the covalent docking method was applied. We further validated the binding stability of the JAK3 protein with the ligands ZINC62162141 and Tofacitinib, both of which have been approved by the FDA for JAK3/STAT inhibition., using DFT/B3LYP/6-31G molecular dynamics simulations lasting 1000 ns and MM/GBSA. Results: During the study, we identified compounds that displayed notable activity against JAK3/STAT, specifically those containing thiadiazol, oxadiazol, and chlorophenyl groups. Additionally, the pharmacophore model, ADRRR_1, exhibited promising potential for predicting new molecules. The predicted compound, ZINC62162141, demonstrated favourable ADMET properties, including inhibition of CYP3A4. Furthermore, we assessed its binding stability to the target protein and determined its affinity for the protein-ligand complex using MMGBSA. Conclusion: The results of this study suggest that the compounds identified have the potential to be promising candidates for inhibiting JAK3/STAT and CYP3A4, offering potential therapeutic benefits for the treatment of rheumatoid arthritis. These findings provide a foundation for subsequent experimental validation and the development of novel drugs in this field.

Quantitative structure-activity relationship and ADME prediction studies on series of spirooxindoles derivatives for anti-cancer activity against colon cancer cell line HCT-116

Forty-one derivatives of spirooxindoles, active against HCT-116 colon cancer cells, underwent pharmacophore-based 3D-QSAR analysis to understand their correlation with anti-cancer activity. The study identified a seven-point pharmacophore model (ADHHRRR1) and QSAR models, offering insights for lead optimization and novel analogue design, thus advancing anti-cancer drug discovery. This research underscores the value of molecular modeling in elucidating structure-activity relationships and enhancing drug development efforts.

Discovery of Novel, Selective, and Nonbasic Agonists for the Kappa-Opioid Receptor Determined by Salvinorin A-Based Virtual Screening.

Modulating the kappa-opioid receptor (KOR) is a promising strategy for treating various human diseases. KOR agonists show potential for treating pain, pruritus, and epilepsy, while KOR antagonists show potential for treating depression, anxiety, and addiction. The diterpenoid Salvinorin A (SalA), a secondary metabolite of Salvia divinorum, is a potent and selective KOR agonist. Unlike typical opioids, SalA lacks a basic nitrogen, which encouraged us to search for nonbasic KOR ligands. Through structure-based virtual screening using 3D pharmacophore models based on the binding mode of SalA, we identified novel, nonbasic, potent, and selective KOR agonists. In vitro studies confirmed two virtual hits, SalA-VS-07 and SalA-VS-08, as highly selective for the KOR and showing G protein-biased KOR agonist activity. Both KOR ligands share a novel spiro-moiety and a nonbasic scaffold. Our findings provide novel starting points for developing therapeutics aimed at treating pain and other conditions in which KOR is a central player.

IN SILICO STUDY: SECONDARY METABOLITES FROM JAMBOLAN (Syzygium Cumini L.) AS POTENTIAL BREAST CANCER TREATMENTS

Breast cancer ranks second in world cancer incidence rates in 2020, contributing to 2,261,419 new cases, or 11.7% of all new cancer cases globally. The search for cancer drugs that work selectively continues to be encouraged to obtain safe and effective therapy, particularly those derived from medicinal plants. Jambolan is a plant that can thrive in both subtropical and tropical climates, including Indonesia. Jambolan (Syzygium cumini L.) has 89% antioxidant activity and 69% cytotoxic activity against T47D cells. Pharmacophore modelling and molecular docking were used to study the binding of 117 active jambolan drugs to alpha and beta estrogen receptors. Rutin was found to be potentially selective for ER-Beta receptors, with a fit score of 53.13. Molecular docking to ER-Beta revealed that rutin has breast cancer activity with a free bond energy value of -10.5 kcal/mol and better conformation and affinity than native ligands (genistein). It also binds to essential amino acids as an anticancer breast at ARG 346 and GLU 305. Lipinski's rule of five prediction results and in silico ADMET prediction from rutin yielded results that met the candidate drug's parameters. Rutin is a potential therapeutic option for treating breast cancer by targeting the ER-Beta receptor.

Combatting Antibiotic-Resistant Staphylococcus aureus: Discovery of TST1N-224, a Potent Inhibitor Targeting Response Regulator VraRC, through Pharmacophore-Based Screening and Molecular Characterizations.

Staphylococcus aureus (S. aureus) is a major global health concern, causing various infections and presenting challenges due to antibiotic resistance. In particular, methicillin-resistant S. aureus, vancomycin-intermediate S. aureus (VISA), and vancomycin-resistant S. aureus pose significant obstacles in treating S. aureus infections. Therefore, the critical need for novel drugs to counter these resistant forms is pressing. Two-component systems (TCSs), integral to bacterial regulation, offer promising targets for disruption. In this study, a comprehensive approach, involving pharmacophore-based inhibitor screening, along with biochemical and biophysical analyses were conducted to identify, characterize, and validate potential inhibitors targeting the response regulator VraRC of S. aureus. The constructed pharmacophore model, Phar-VRPR-N3, demonstrated effectiveness in identifying a potent inhibitor, TST1N-224 (IC50 = 60.2 ± 4.0 μM), against the formation of the VraRC-DNA complex. Notably, TST1N-224 exhibited strong binding to VraRC (KD = 23.4 ± 1.2 μM) using a fast-on-fast-off binding mechanism. Additionally, NMR-based molecular modeling revealed that TST1N-224 predominantly interacts with the α9- and α10-helixes of the DNA-binding domain of VraR, where the interactive and functionally essential residues (N165, K180, S184, and R195) act as hotspots for structure-based inhibitor optimization. Furthermore, TST1N-224 evidently enhanced the susceptibility of VISA to both vancomycin and methicillin. Importantly, TST1N-224 distinguished by 1,2,5,6-tetrathiocane with the 3 and 8 positions modified with ethanesulfonates holds significant potential as a lead compound for the development of new antimicrobial agents.

Chemical Predictive Modelling and Natural Product-based Divergent Synthesis - Design of Type B PPAPs with Nanomolar Activities against MRSA.

In response to the pressing global challenge of antibiotic resistance, time efficient design and synthesis of novel antibiotics are of immense need. Polycyclic polyprenylated acylphloroglucinols (PPAP) were previously reported to effectively combat a range of gram-positive bacteria. Although the exact mode of action is still not clear, we conceptualized a late-stage divergent synthesis approach to expand our natural product-based PPAP library by 30 additional entities to perform SAR studies against methicillin-resistant Staphylococcus aureus (MRSA). Although at this point only data from cellular assays are available and understanding of molecular drug-target interactions are lacking, the experimental data were used to generate 3D-QSAR models via an artificial intelligence training and to identify a common pharmacophore model. The experimentally validated QSAR model enabled the estimation of anti-MRSA activities of a virtual compound library consisting of more than 100,000 in-silico generated B PPAPs, out of which the 20 most promising candidates were synthesized. These novel PPAPs revealed significantly improved cellular activities against MRSA with growth inhibition down to concentrations less than 1 μm.

Exploitation of the nitro- and/or 4-Trifluoromethyl-decorated phenyl fragment to develop small inhibitors of Alpha-Syn fibril aggregation

Here, we report new 2-nitro and/or 4-trifluoromethylphenyl-based small molecules developed as inhibitors of alpha-Syn fibril formation. The set of eighteen compounds was inspired by well-known alpha-Syn aggregation modulators retrieved from literature. The preliminary biochemical data suggested that the two molecules out of eighteen compounds exerted activity comparable to that of reference compound SynuClean-D (SC-D, 5-nitro-6-(3-nitrophenyl)-2-oxo-4-(trifluoromethyl)-1H-pyridine-3-carbonitrile), according to Thioflavin T kinetics. Pharmacophore modelling deciphered the main structural requirements for alpha-Syn aggregation modulators. Moreover, docking and molecular dynamics simulations depicted the binding mode with the targeted alpha-Syn fibrils. The structural data of these new potential α-Syn binders might furnish additional information for understanding the mechanism of action of the ligands that specifically target the NAC domain as theranostic agents for α-synucleopathies.

Unified Aedes aegypti Protein Resource Database (UAAPRD): An Integrated High-Throughput In Silico Platform for Comprehensive Protein Structure Modeling and Functional Target Analysis to Enhance Vector Control Strategies.

A comprehensive examination of Aedes aegypti's proteome to detect key proteins that can be targeted with small molecules can disrupt blood feeding and disease transmission. However, research currently only focuses on finding repellent-like compounds, limiting studies on identifying unexplored proteins in its proteome. High-throughput analysis generates vast amounts of data, raising concerns about accessibility and usability. Establishing a dedicated database is a solution, centralizing information on identified proteins, functions, and modeled structures for easy access and research. This study focuses on scrutinizing key proteins in A. aegypti, modeling their structures using RaptorX standalone tool, identification of druggable binding sites using BiteNet, validating the models via Ramachandran plot studies and refining them via 50-ns molecular dynamic simulations using Schrodinger Maestro. By analyzing ~ 18k proteins in the proteome of A. aegypti in our previous studies, all proteins involved in the light and dark circadian rhythm of the mosquito, inclusive of proteins in blood feeding, metabolism, etc. were chosen for the current study. The outcome is UAAPRD, a unique repository housing information on hundreds of previously unmodeled and un-simulated mosquito proteins. This robust MYSQL database ( https://uaaprd.onrender.com/user ) houses data on 309 modeled & simulated proteins of A. aegypti. It allows users to obtain protein data, view evolutionary analysis data of the protein categories, visualize proteins of interest, and send request to screen against the pharmacophore models present in UAAPRD against ligand of interest. This study offers crucial insights for developing targeted studies, which will ultimately contribute to more effective vector control strategies.

Molecular Regulator Driving Endometriosis Towards Endometrial Cancer: A Multi-Scale Computational Investigation to Repurpose Anti-Cancer drugs.

Endometriosis is a gynecological disorder among reproductive-aged women. Recent epidemiological investigations suggest endometriosis increases the risk of endometrial cancer. However, the molecular entity leading to endometriosis-to-endometrial cancer is largely unknown. This study aimed to combine a variety of computational approaches to identify the key therapeutic target promoting endometriosis-to-endometrial cancer and screen potential inhibitors against target to prevent cancer development. Our systematic investigations, includes transcriptomic profiling, protein network, pharmacophore modeling, docking, binding free energy calculation, dynamics simulation, and quantum mechanics. The gene expression analysis on endometriosis and endometrial cancer was performed and showed 108 shared upregulated genes in both conditions. Further construction of interaction network with 108 genes showed intercellular adhesion molecule 1 (ICAM1) to be a crucial molecule with a high degree of connectivity that influences vital mechanisms related to cancer pathways. We then generated ligand-based pharmacophore models using established ICAM1 inhibitors. Among the models, the ADRRR_8 pharmacophore exhibited a robust area under curve (AUC = 0.83), was employed to screen 1739 anti-cancer drugs. On screening, 421 anti-cancer drugs displayed ICAM1-inhibiting pharmacophore features. Further, the docking of 421 drugs with ICAM1 showed lanreotide (-7.80 kcal/mol) with better affinity than the reference ICAM1 inhibitor (-3.59 kcal/mol). Further validation though binding free energy and dynamics simulation of the lanreotide-ICAM1 complex showed a high binding affinity of -55.90 kcal/mol and contributed stable confirmation. According to quantum chemical calculations, lanreotide's electronic properties favour ICAM1 binding with highest occupied molecular orbital was -6.91 eV and lowest unoccupied molecular orbital was -3.93 eV. Our study supports using lanreotide to treat endometriosis, which could delay or prevent endometrial cancer. These predictions need to be confirmed and examined to determine the use of lanreotide in endometriosis treatment.

A Systematic Hierarchical Virtual Screening Model for RhlR Inhibitors Based on PCA, Pharmacophore, Docking, and Molecular Dynamics.

RhlR plays a key role in the quorum sensing of Pseudomonas aeruginosa. The current structure-activity relationship (SAR) studies of RhlR inhibitors mainly focus on elucidating the functional groups. Based on a systematic review of previous research on RhlR inhibitors, this study aims to establish a systematic, hierarchical screening model for RhlR inhibitors. We initially established a database and utilized principal component analysis (PCA) to categorize the inhibitors into two classes. Based on the training set, pharmacophore models were established to elucidate the structural characteristics of ligands. Subsequently, molecular docking, molecular dynamics simulations, and the calculation of binding free energy and strain energy were performed to validate the crucial interactions between ligands and receptors. Then, the screening criteria for RhlR inhibitors were established hierarchically based on ligand structure characteristics, ligand-receptor interaction, and receptor affinity. Test sets were finally employed to validate the hierarchical virtual screening model by comparing it with the current SAR studies of RhlR inhibitors. The hierarchical screening model was confirmed to possess higher accuracy and a true positive rate, which holds promise for subsequent screening and the discovery of active RhlR inhibitors.

Three-Dimensional Quantitative Structure-Activity Relationship Study of Transient Receptor Potential Vanilloid 1 Channel Antagonists Reveals Potential for Drug Design Purposes.

Transient receptor potential vanilloid 1 (TRPV1) was reported to be a putative target for recovery from chronic pain, producing analgesic effects after its inhibition. A series of drug candidates were previously developed, without the ability to ameliorate the therapeutic outcome. Starting from previously designed compounds, derived from the hybridization of antagonist SB-705498 and partial agonist MDR-652, we performed a virtual screening on a pharmacophore model built by exploiting the Cryo-EM 3D structure of a nanomolar antagonist in complex with the human TRPV1 channel. The pharmacophore model was described by three pharmacophoric features, taking advantage of both the bioactive pose of the antagonist and the receptor exclusion spheres. The results of the screening were implemented inside a 3D-QSAR model, correlating with the negative decadic logarithm of the inhibition rate of the ligands. After the validation of the obtained 3D-QSAR model, we designed a new series of compounds by introducing key modifications on the original scaffold. Again, we determined the compounds' binding poses after alignment to the pharmacophoric model, and we predicted their inhibition rates with the validated 3D-QSAR model. The obtained values resulted in being even more promising than parent compounds, demonstrating that ongoing research still leaves much room for improvement.

Resveratrol suppresses liver cancer progression by downregulating AKR1C3: targeting HCC with HSA nanomaterial as a carrier to enhance therapeutic efficacy.

The enzyme AKR1C3 plays a crucial role in hormone and drug metabolism and is associated with abnormal expression in liver cancer, leading to tumor progression and poor prognosis. Nanoparticles modified with HSA can modulate the tumor microenvironment by enhancing photodynamic therapy to induce apoptosis in tumor cells and alleviate hypoxia. Therefore, exploring the potential regulatory mechanisms of resveratrol on AKR1C3 through the construction of HSA-RSV NPs carriers holds significant theoretical and clinical implications for the treatment of liver cancer. The aim of this study is to investigate the targeted regulation of AKR1C3 expression through the loading of resveratrol (RSV) on nanomaterials HSA-RSV NPs (Nanoparticles) in order to alleviate tumor hypoxia and inhibit the progression of hepatocellular carcinoma (HCC), and to explore its molecular mechanism. PubChem database and PharmMapper server were used to screen the target genes of RSV. HCC-related differentially expressed genes (DEGs) were analyzed through the GEO dataset, and relevant genes were retrieved from the GeneCards database, resulting in the intersection of the three to obtain candidate DEGs. GO and KEGG enrichment analyses were performed on the candidate DEGs to analyze the potential cellular functions and molecular signaling pathways affected by the main target genes. The cytohubba plugin was used to screen the top 10 target genes ranked by Degree and further intersected the results of LASSO and Random Forest (RF) to obtain hub genes. The expression analysis of hub genes and the prediction of malignant tumor prognosis were conducted. Furthermore, a pharmacophore model was constructed using PharmMapper. Molecular docking simulations were performed using AutoDockTools 1.5.6 software, and ROC curve analysis was performed to determine the core target. In vitro cell experiments were carried out by selecting appropriate HCC cell lines, treating HCC cells with different concentrations of RSV, or silencing or overexpressing AKR1C3 using lentivirus. CCK-8, clone formation, flow cytometry, scratch experiment, and Transwell were used to measure cancer cell viability, proliferation, migration, invasion, and apoptosis, respectively. Cellular oxygen consumption rate was analyzed using the Seahorse XF24 analyzer. HSA-RSV NPs were prepared, and their characterization and cytotoxicity were evaluated. The biological functional changes of HCC cells after treatment were detected. An HCC subcutaneous xenograft model was established in mice using HepG2 cell lines. HSA-RSV NPs were injected via the tail vein, with a control group set, to observe changes in tumor growth, tumor targeting of NPs, and biological safety. TUNEL, Ki67, and APC-hypoxia probe staining were performed on excised tumor tissue to detect tumor cell proliferation, apoptosis, and hypoxia. Lentivirus was used to silence or overexpress AKR1C3 simultaneously with the injection of HSA-RSV NPs via the tail vein to assess the impact of AKR1C3 on the regulation of HSA-RSV NPs in HCC progression. Bioinformatics analysis revealed that AKR1C3 is an important target gene involved in the regulation of HCC by RSV, which is associated with the prognosis of HCC patients and upregulated in expression. In vitro cell experiments showed that RSV significantly inhibits the respiratory metabolism of HCC cells, suppressing their proliferation, migration, and invasion and promoting apoptosis. Silencing AKR1C3 further enhances the toxicity of RSV towards HCC cells. The characterization and cytotoxicity experiments of nanomaterials demonstrated the successful construction of HSA-RSV NPs, which exhibited stronger inhibitory effects on HCC cells. In vivo, animal experiments further confirmed that targeted downregulation of AKR1C3 by HSA-RSV NPs suppresses the progression of HCC and tumor hypoxia while exhibiting tumor targeting and biological safety. Targeted downregulation of AKR1C3 by HSA-RSV NPs can alleviate HCC tumor hypoxia and inhibit the progression of HCC.

Identification of Potent ADCK3 Inhibitors through Structure-Based Virtual Screening.

ADCK3 is a member of the UbiB family of atypical protein kinases in humans, with homologues in archaea, bacteria, and eukaryotes. In lieu of protein kinase activity, ADCK3 plays a role in the biosynthesis of coenzyme Q10 (CoQ10), and inactivating mutations can cause a CoQ10 deficiency and ataxia. However, the exact functions of ADCK3 are still unclear, and small-molecule inhibitors could be useful as chemical probes to elucidate its molecular mechanisms. In this study, we applied structure-based virtual screening (VS) to discover a novel chemical series of ADCK3 inhibitors. Through extensive structural analysis of the active-site residues, we developed a pharmacophore model and applied it to a large-scale VS. Out of ∼170,000 compounds virtually screened, 800 top-ranking candidate compounds were selected and tested in both ADCK3 and p38 biochemical assays for hit validation. In total, 129 compounds were confirmed as ADCK3 inhibitors, and among them, 114 compounds are selective against p38, which was used as a counter-target. Molecular dynamics (MD) simulations were then conducted to predict the binding modes of the most potent compounds within the ADCK3 active site. Through metadynamics analysis, we successfully detected the key amino acid residues that govern intermolecular interactions. The findings provided in this study can serve as a promising starting point for drug development.

Hybrid Virtual Screening Approach to Predict Novel Natural Compounds against HIV-1 CCR5.

Human immunodeficiency virus (HIV) infection continues to pose a major global health challenge. HIV entry into host cells via membrane fusion mediated by the viral envelope glycoprotein gp120/gp41 is a key step in the HIV life cycle. CCR5, expressed on CD4+ T cells and macrophages, acts as a coreceptor facilitating HIV-1 entry. The CCR5 antagonist maraviroc is used to treat HIV infection. However, it can cause adverse effects and has limitations such as only inhibiting CCR5-tropic viruses. There remains a need to develop alternative CCR5 inhibitors with improved safety profiles. Natural products may offer advantages over synthetic inhibitors including higher bioavailability, binding affinity, effectiveness, lower toxicity, and molecular diversity. However, screening the vast chemical space of natural compounds to identify novel CCR5 inhibitors presents challenges. This study aimed to address this gap through a hybrid ligand-based pharmacophore modeling and molecular docking approach to virtually screen large natural product databases. A reliable pharmacophore model was developed based on 311 known CCR5 antagonists and validated against an external data set. Five natural product databases containing over 306,000 compounds were filtered based on drug-likeness rules. The validated pharmacophore model screened the databases to identify 611 hits. Key residues of the CCR5 receptor crystal structure were identified for docking. The top hits were docked, and interactions were analyzed. Molecular dynamics simulations were conducted to examine complex stability. Computational prediction evaluated pharmacokinetic properties. Three compounds exhibited similar interactions and binding energies to maraviroc. MD simulations demonstrated complex stability comparable to maraviroc. One compound showed optimal predicted absorption, minimal metabolism, and a lower likelihood of interactions than maraviroc. This computational screening workflow identified three natural compounds with promising CCR5 inhibition and favorable pharmacokinetic profiles. One compound emerged as a lead based on bioavailability potential and minimal interaction risk. These findings present opportunities for developing alternative CCR5 antagonists and warrant further experimental investigation. Overall, the hybrid virtual screening approach proved effective for mining large natural product spaces to discover novel molecular entities with drug-like properties.

Identification of novel covalent JAK3 inhibitors through consensus scoring virtual screening: integration of common feature pharmacophore and covalent docking.

Accumulated research strongly indicates that Janus kinase 3 (JAK3) is intricately involved in the initiation and advancement of a diverse range of human diseases, underscoring JAK3 as a promising target for therapeutic intervention. However, JAK3 shows significant homology with other JAK family isoforms, posing substantial challenges in the development of JAK3 inhibitors. To address these limitations, one strategy is to design selective covalent JAK3 inhibitors. Therefore, this study introduces a virtual screening approach that combines common feature pharmacophore modeling, covalent docking, and consensus scoring to identify novel inhibitors for JAK3. First, common feature pharmacophore models were constructed based on a selection of representative covalent JAK3 inhibitors. The optimal qualitative pharmacophore model proved highly effective in distinguishing active and inactive compounds. Second, 14 crystal structures of the JAK3-covalent inhibitor complex were chosen for the covalent docking studies. Following validation of the screening performance, 5TTU was identified as the most suitable candidate for screening potential JAK3 inhibitors due to its higher predictive accuracy. Finally, a virtual screening protocol based on consensus scoring was conducted, integrating pharmacophore mapping and covalent docking. This approach resulted in the discovery of multiple compounds with notable potential as effective JAK3 inhibitors. We hope that the developed virtual screening strategy will provide valuable guidance in the discovery of novel covalent JAK3 inhibitors.

Virtual Screening of Indonesian Herbal Compounds with Neuraminidase Inhibitor Activity against N2 Influenza Virus Protein: An in silico Study

Background: Neuraminidase inhibitor (NAI) is one of anti-influenza drugs recommended for use by the World Health Organization (WHO). However, after treatment with NAI drugs in human, resistance to influenza antiviral drugs is begun to rise. Therefore, identification of compounds from Indonesian herbal plants as natural inhibitors of the influenza virus neuraminidase protein needs to be conducted for the development of new anti-influenza drugs.Materials and methods: The crystal structure of the neuraminidase protein complex used in this study was obtained from the Protein Data Bank (PDB). Structure-based pharmacophore modeling was performed using LigandScout version 4.4.5 software. Indonesian herbal plant compounds were collected from the HerbalDB database. Protein and ligand processing was carried out using Autodock 4.2 software. The 3D interaction visualization was carried out with Autodock software, while 2D interaction visualization was carried out with LigPlot software. To determine the toxicity and drug-likeliness of the ligand, the test ligands that had the best docking results were predicted using SwissADME and AdmetSAR.Results: From the virtual screening results, 24 hits were found, and five compounds had the best binding energy among the 24 tested compounds, these were pollenitin (ΔG=-7.22 kcal/mol), OPC-4:0 (ΔG=-7.11 kcal/mol), 6-hydroxykaempferol (ΔG=-7.08 kcal/mol), 5,8-dihydroxy-7,4'-dimethoxyflavone (ΔG=-7.07 kcal/mol), and 3,5,6,7-tetrahydroxy-4'-methoxyflavone (ΔG=-6.95 kcal/mol). The best five compounds were then chosen for further analysis.Conclusion: OPC-4:0 is found to be the best compound for the NAI based on its binding energy, pharmacokinetics, toxicity, and drug-likeliness. Thus, OPC-4:0 might be a potential candidate as a NAI of HxN2 virus. Keywords: influenza, molecular docking, neuraminidase, resistance, virtual screening

Epigallocatechin gallate and curcumin inhibit Bcl-2: a pharmacophore and docking based approach against cancer

The protein Bcl-2, well-known for its anti-apoptotic properties, has been implicated in cancer pathogenesis. Identifying the primary gene responsible for promoting improved cell survival and development has provided compelling evidence for preventing cellular death in the progression of malignancies. Numerous research studies have provided evidence that the abundance of Bcl-2 is higher in malignant cells, suggesting that suppressing Bcl-2 expression could be a viable therapeutic approach for cancer treatment. In this study, we acquired a compound collection using a database that includes constituents from Traditional Chinese Medicine (TCM). Initially, we established a pharmacophore model and utilized it to search the TCM database for potential compounds. Compounds with a fitness score exceeding 0.75 were selected for further analysis. The Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analysis identified six compounds with favorable therapeutic characteristics. The compounds that successfully passed the initial screening process based on the pharmacodynamic model were subjected to further evaluation. Extra-precision (XP) docking was employed to identify the compounds with the most favorable XP docking scores. Further analysis using the Molecular Mechanics Generalized Born Surface Area (MM-GBSA) method to calculate the overall free binding energy. The binding energy between the prospective ligand molecule and the target protein Bcl-2 was assessed by a 100 ns molecular dynamics simulation for curcumin and Epigallocatechin gallate (EGCG). The findings of this investigation demonstrate the identification of a molecular structure that effectively inhibits the functionality of the Bcl-2 when bound to the ligand EGCG. Consequently, this finding presents a novel avenue for the development of pharmaceuticals capable of effectively addressing both inflammatory and tumorous conditions.

Uncovering the unique characteristics of different groups of 5-HT5AR ligands with reference to their interaction with the target protein

BackgroundThe serotonin 5-HT5A receptor has attracted much more research attention, due to the therapeutic potential of its ligands being increasingly recognized, and the possibilities that lie ahead of these findings. There is a growing body of evidence indicating that these ligands have procognitive, pro-social, and anti-depressant properties, which offers new avenues for the development of treatments that could address socially important conditions related to the malfunctioning of the central nervous system. The aim of our study was to unravel the molecular determinants for 5-HT5AR ligands that govern their activity towards the receptor.MethodsIn response to the need for identification of molecular determinants for 5-HT5AR activity, we prepared a comprehensive collection of 5-HT5AR ligands, carefully gathering literature and patent data. Leveraging molecular modeling techniques, such as pharmacophore hypothesis development, docking, and molecular dynamics simulations enables to gain valuable insights into the specific interactions of 5-HT5AR ligand groups with the receptor.ResultsThe obtained comprehensive set of 2160 compounds was divided into dozens of subsets, and a pharmacophore model was developed for each group. The results from the docking and molecular dynamics simulations have enabled the identification of crucial ligand–protein interactions that are essential for the compound's activity towards 5-HT5AR.ConclusionsThe findings from the molecular modeling study provide valuable insights that can guide medicinal chemists in the development of new 5-HT5AR ligands. Considering the pharmacological significance of these compounds, they have the potential to become impactful treatments for individuals and communities in the future. Understanding how different crystal/cryo-EM structures of 5-HT5AR affect molecular modeling experiments could have major implications for future computational studies on this receptor.Graphical abstract

Novel 1,3,4-oxadiazole derivatives of naproxen targeting EGFR: Synthesis, molecular docking studies, and cytotoxic evaluation.

The close association between inflammation and cancer inspired the synthesis of a series of 1,3,4-oxadiazole derivatives (compounds H4-A-F) of 6-methoxynaphtalene. The chemical structures of the new compounds were validated utilizing Fourier-transform infrared, proton nuclear magnetic resonance, and carbon-13 nuclear magnetic resonance spectroscopic techniques and CHN analysis. Computer-aided drug design methods were used to predict the compounds biological target, ADMET properties, toxicity, and to evaluate the molecular similarities between the design compounds and erlotinib, a standard epidermal growth factor receptor (EGFR) inhibitor. The antiproliferative effects of the new compounds were evaluated by the 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide assay, cell cycle analysis, apoptosis detection by microscopy, quantitative reverse transcription-polymerase chain reaction, and immunoblotting, and EGFR enzyme inhibition assay. In silico analysis of the new oxadiazole derivatives indicated that these compounds target EGFR, and that compounds H4-A, H4-B, H4-C, and H4-E show similar molecular properties to erlotinib. Additionally, the results indicated that none of the synthesized compounds are carcinogenic, and that compounds H4-A, H4-C, and H4-F are nontoxic. Compound H4-A showed the best-fit score against EGFR pharmacophore model, however, the in vitro studies indicated that compound H4-C was the most cytotoxic. Compound H4-C caused cytotoxicity in HCT-116 colorectal cancer cells by inducing both apoptosis and necrosis. Furthermore, compounds H4-D, H4-C, and H4-B had potent inhibitory effect on EGFR tyrosine kinase that was comparable to erlotinib. The findings of this inquiry offer a basis for further investigation into the differences between the synthesized compounds and erlotinib. However, additional testing will be needed to assess all of these differences and to identify the most promising compound for further research.

Integrated pharmacophore‐based screening and docking strategy for identifying type II inhibitors of VEGFR‐2 kinase

Pharmacophore modeling and quantitative structure-activity relationship (QSAR) analysis represent indispensable methodologies in modern drug discovery, offering insights into ligand-receptor interactions and aiding in the rational design of potent therapeutic agents. This study presents a thorough investigation into the identification of novel inhibitors targeting VEGFR-2 kinase, a key regulator in angiogenesis and a promising target in oncology.Leveraging a dataset comprising 72 type-II inhibitors, a series of pharmacophore hypotheses were developed using a set of six active ligands. Subsequent evaluation led to the selection of top-performing hypotheses, ADDHRR_6 and ADDHRR_10, which demonstrated good predictive capabilities in atom-based 3D QSAR modeling.Virtual screening of the Maybridge database using these pharmacophore hypotheses resulted in the identification of potential hits, which were evaluated through molecular docking analyses. Ten compounds exhibited favorable docking scores and important interactions with key residues, such as Glu885, Glu917, Cys919, and Asp1046.Additionally, comprehensive absorption, distribution, metabolism, and excretion (ADME) analysis was performed and indicated that these compound had acceptable physicochemical properties. These findings highlight the utility of integrated pharmacophore-based screening and molecular docking approaches in the identification of novel type II VEGFR-2 kinase inhibitors.