Pharmacophore and docking-based sequential virtual screening for the identification of novel Sigma 1 receptor ligands

Structure-based pharmacophore modeling for precision inhibition of mutant ESR2 in breast cancer: A systematic computational approach

BACKGROUND

Breast cancer, a leading cause of female mortality, is closely linked to mutations in estrogen receptor beta (ESR2), particularly in the ligand-binding domain, which contributed to altered signaling pathways and uncontrolled cell growth.

OBJECTIVES/AIMS

This study investigates the molecular and structural aspects of ESR2 mutant proteins to identify shared pharmacophoric regions of ESR2 mutant proteins and potential therapeutic targets aligned within the pharmacophore model.

METHODS

This study was initiated by establishing a common pharmacophore model among three mutant ESR2 proteins (PDB ID: 2FSZ, 7XVZ, and 7XWR). The generated shared feature pharmacophore (SFP) includes four primary binding interactions: Hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), hydrophobic interactions (HPho), and Aromatic interactions (Ar), along with halogen bond donors (XBD) and totalling 11 features (HBD: 2, HBA: 3, HPho: 3, Ar: 2, XBD: 1). By employing an in-house Python script, these 11 features distributed into 336 combinations, which were used as query to isolate a drug library of 41,248 compounds and subjected to virtual screening through the generated SFP.

RESULTS

The virtual screening demonstrated 33 hits showing potential pharmacophoric fit scores and low RMSD value. The top four compounds: ZINC94272748, ZINC79046938, ZINC05925939, and ZINC59928516 showed a fit score of more than 86% and satisfied the Lipinski rule of five. These four compounds and a control underwent molecular (XP Glide mode) docking analysis against wild-type ESR2 protein (PDB ID: 1QKM), resulting in binding affinity of -8.26, -5.73, -10.80, and -8.42 kcal/mol, respectively, along with the control -7.2 kcal/mol. Furthermore, the stability of the selected candidates was determined through molecular dynamics (MD) simulations of 200 ns and MM-GBSA analysis.

CONCLUSION

Based on MD simulations and MM-GBSA analysis, our study identified ZINC05925939 as a promising ESR2 inhibitor among the top four hits. However, it is essential to conduct further wet lab evaluation to assess its efficacy.

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.

Identification of novel inhibitors against hantaviruses through 2D fingerprinting and molecular modeling approaches

With the immensely growing outbreaks of hantavirus with still no effective treatment available, there is an urgent need of exploring new computational approaches which will target potential virulent proteins that will eventually reduce its growth. In this study, an envelope glycoprotein, Gn, was targeted. The glycoproteins, which are the sole targets of neutralizing antibodies, drive virus entry via receptor-mediated endocytosis and endosomal membrane fusion. Inhibitors are herein proposed to negate its action mechanism. On the basis of the scaffolds of favipiravir, a FDA compound already used against hantavirus, a library was designed using a 2D fingerprinting approach. Upon molecular docking analysis, the top four docked compounds-(1) favipiravir (-4.5 kcal/mol), (2) N-hydroxy-3-oxo-3, 4-dihydropyrazine-2-carboxamide (-4.7 kcal/mol), (3) N, 5, 6-trimethyl-2-oxo-1H-pyrazine-3-carboxamide (-4.5 kcal/mol), and (4) 3-propyl-1H-pyrazin-2-one (-3.8)-were prioritized on the basis of the lowest binding energies score. Through molecular docking, the best-categorized compound was subjected to molecular dynamics simulation for a 100-ns time span. Molecular dynamics sheds light on each ligand behavior within the active site. Among the four complexes, only favipiravir and 6320122 compound were found to be stable inside the pocket. This is due to the presence of common rings, pyrazine and carboxamide ring, which make a significant interaction with active key residues Furthermore, the MMPB/GBSA binding free energy analysis calculated for all complexes supported the dynamics results by calculating the most stable values for favipiravir complex (-9.9933 and -8.6951 kcal/mol) and for 6320122 compound complex (-13.8675 and -9.3439 kcal/mol), which demonstrated that the selected compounds have a proper binding affinity with the targeted proteins. The hydrogen bond analysis similarly revealed a strong bonding interaction. The results yielded a strong interaction between the enzyme and the inhibitor throughout the simulation; thus, the inhibitor has the potential to become a lead compound and could be subjected to experimental evaluation to unveil their blockage ability.

Identification and Validation of Novel Biomarkers and Potential Targeted Drugs in Cholangiocarcinoma: Bioinformatics, Virtual Screening, and Biological Evaluation

Cholangiocarcinoma (CCA) is a complex and refractor type of cancer with global prevalence. Several barriers remain in CCA diagnosis, treatment, and prognosis. Therefore, exploring more biomarkers and therapeutic drugs for CCA management is necessary. CCA gene expression data was downloaded from the TCGA and GEO databases. KEGG enrichment, GO analysis, and protein-protein interaction network were used for hub gene identification. miRNA were predicted using Targetscan and validated according to several GEO databases. The relative RNA and miRNA expression levels and prognostic information were obtained from the GEPIA. The candidate drug was screened using pharmacophore-based virtual screening and validated by molecular modeling and through several in vitro studies. 301 differentially expressed genes (DEGs) were screened out. Complement and coagulation cascades-related genes (including AHSG, F2, TTR, and KNG1), and cell cycle-related genes (including CDK1, CCNB1, and KIAA0101) were considered as the hub genes in CCA progression. AHSG, F2, TTR, and KNG1 were found to be significantly decreased and the eight predicted miRNA targeting AHSG, F2, and TTR were increased in CCA patients. CDK1, CCNB1, and KIAA0101 were found to be significantly abundant in CCA patients. In addition, Molport-003-703-800, which is a compound that is derived from pharmacophores-based virtual screening, could directly bind to CDK1 and exhibited anti-tumor activity in cholangiocarcinoma cells. AHSG, F2, TTR, and KNG1 could be novel biomarkers for CCA. Molport-003-703-800 targets CDK1 and work as potential cell cycle inhibitors, thereby having potential for consideration for new chemotherapeutics for CCA.

Virtual Combinatorial Chemistry and Pharmacological Screening: A Short Guide to Drug Design

Traditionally, drug development involved the individual synthesis and biological evaluation of hundreds to thousands of compounds with the intention of highlighting their biological activity, selectivity, and bioavailability, as well as their low toxicity. On average, this process of new drug development involved, in addition to high economic costs, a period of several years before hopefully finding a drug with suitable characteristics to drive its commercialization. Therefore, the chemical synthesis of new compounds became the limiting step in the process of searching for or optimizing leads for new drug development. This need for large chemical libraries led to the birth of high-throughput synthesis methods and combinatorial chemistry. Virtual combinatorial chemistry is based on the same principle as real chemistry—many different compounds can be generated from a few building blocks at once. The difference lies in its speed, as millions of compounds can be produced in a few seconds. On the other hand, many virtual screening methods, such as QSAR (Quantitative Sturcture-Activity Relationship), pharmacophore models, and molecular docking, have been developed to study these libraries. These models allow for the selection of molecules to be synthesized and tested with a high probability of success. The virtual combinatorial chemistry–virtual screening tandem has become a fundamental tool in the process of searching for and developing a drug, as it allows the process to be accelerated with extraordinary economic savings.

Molecular insights into the selective binding mechanism targeting parallel human telomeric G-quadruplex

Stabilizing human telomere DNA G-quadruplex (G4) proves a promising anti-cancer strategy. Though plenty of G4 stabilizing molecules have been reported, little is known about their selective binding mechanism among various G4s. Recently, a designed monohydrazone derivative (compound 15) was reported to display specific preference in binding and stabilizing parallel human telomeric G4. To reveal the selective binding mechanism, a comparative theoretical investigation was performed on two monohydrazone derivatives (compounds 1 and 15) and three telomeric G4s showing parallel, hybrid-I, and hybrid-II conformations. Two probable binding modes, i.e. the end-stacking binding and the groove binding, were predicted by molecular dockings for each monohydrazone in its binding with the telomeric G4s. Further long-timescale molecular dynamics simulations reveal the conversion from the groove binding to the end-stacking binding for both compounds, indicating the preference of the end-stacking binding mode. Structural analysis together with binding free energy calculations show that the van der Waals interaction plays a leading role in ranking the binding affinity. By forming extensive van der Waals interactions, the parallel G4-15 binding complex shows the highest binding affinity, and the corresponding compound 15 exhibits the strongest stabilizing effect to the telomeric G4. These findings agree well with the experimental observations. Through characterizing the selective binding between monohydrazones and telomeric G4s at the atomic level, the current study provides support to the design of novel selective stabilizers targeting telomeric G4s.

Hybrid In Silico Approach Reveals Novel Inhibitors of Multiple SARS-CoV-2 Variants

The National Center for Advancing Translational Sciences (NCATS) has been actively generating SARS-CoV-2 high-throughput screening data and disseminates it through the OpenData Portal (https://opendata.ncats.nih.gov/covid19/). Here, we provide a hybrid approach that utilizes NCATS screening data from the SARS-CoV-2 cytopathic effect reduction assay to build predictive models, using both machine learning and pharmacophore-based modeling. Optimized models were used to perform two iterative rounds of virtual screening to predict small molecules active against SARS-CoV-2. Experimental testing with live virus provided 100 (∼16% of predicted hits) active compounds (efficacy > 30%, IC50 ≤ 15 μM). Systematic clustering analysis of active compounds revealed three promising chemotypes which have not been previously identified as inhibitors of SARS-CoV-2 infection. Further investigation resulted in the identification of allosteric binders to host receptor angiotensin-converting enzyme 2; these compounds were then shown to inhibit the entry of pseudoparticles bearing spike protein of wild-type SARS-CoV-2, as well as South African B.1.351 and UK B.1.1.7 variants.

A Network Pharmacology and Molecular Docking Strategy to Explore Potential Targets and Mechanisms Underlying the Effect of Curcumin on Osteonecrosis of the Femoral Head in Systemic Lupus Erythematosus

Background

Systemic lupus erythematosus (SLE) is a refractory immune disease, which is often complicated with osteonecrosis of the femoral head (ONFH). Curcumin, the most active ingredient of Curcuma longa with a variety of biological activities, has wide effects on the body system. The study is aimed at exploring the potential therapeutic targets underlying the effect of curcumin on SLE-ONFH by utilizing a network pharmacology approach and molecular docking strategy.

Methods

Curcumin and its drug targets were identified using network analysis. First, the Swiss target prediction, GeneCards, and OMIM databases were mined for information relevant to the prediction of curcumin targets and SLE-ONFH-related targets. Second, the curcumin target gene, SLE-ONFH shared gene, and curcumin-SLE-ONFH target gene networks were created in Cytoscape software followed by collecting the candidate targets of each component by R software. Third, the targets and enriched pathways were examined by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Eventually, a gene-pathway network was constructed and visualized by Cytoscape software; key potential central targets were verified and checked by molecular docking and literature review.

Results

201 potential targets of curcumin and 170 related targets involved in SLE-ONFH were subjected to network analysis, and the 36 intersection targets indicated the potential targets of curcumin for the treatment of SLE-ONFH. Additionally, for getting more comprehensive and accurate candidate genes, the 36 potential targets were determined to be analyzed by network topology and 285 candidate genes were obtained finally. The top 20 biological processes, cellular components, and molecular functions were identified, when corrected by a P value ≤ 0.05. 20 related signaling pathways were identified by KEGG analysis, when corrected according to a Bonferroni P value ≤ 0.05. Molecular docking showed that the top three genes (TP53, IL6, VEGFA) have good binding force with curcumin; combined with literature review, some other genes such as TNF, CCND1, CASP3, and MMP9 were also identified.

Conclusion

The present study explored the potential targets and signaling pathways of curcumin against SLE-ONFH, which could provide a better understanding of its effects in terms of regulating cell cycle, angiogenesis, immunosuppression, inflammation, and bone destruction.

Targeting p53-MDM2 interactions to identify small molecule inhibitors for cancer therapy: beyond "Failure to rescue"

At present, disrupting p53-MDM2 interactions through small molecule ligands is a promising approach to safe treatment and management of human cancer. Tumor cells unlike the normal cells, are rapidly evolving affecting the efficacy of many approved anti-cancer agents due to drug resistance. Therefore, identifying a potential anticancer compound is crucial. Pharmacophore based virtual screening, followed by molecular docking, ADMET evaluation, and molecular dynamics studies against MDM2 protein was investigated to identify potential ligands that may act as inhibitors. The model (AHRR_1) with survival score (4.176) was selected among the top ranked generated Pharmacophore hypothesis. Validation of the model hypothesis by an external dataset of actives and inactive compounds produced significant validation attributes including; AUC = 0.85, BEDROC = 0.56 at α = 20.0, RIE = 8.18, AUAC = 0.88, and EF of 6.2 at the top 2% of the dataset. The model was use for screening the ZINC database, and the top 1375 hits satisfying the model hypothesis were subjected to molecular docking studies to understand the molecular and structural basis of selectivity of compounds for MDM2 protein. A sub-set of 25 compounds with binding energy lower than the reference inhibitors were evaluated for pharmacokinetic properties. Four compounds (ZINC02639178, ZINC06752762, ZINC38933175, and ZINC77969611) showed the most desired pharmacokinetic profile. Lastly, investigation of the dynamic behaviour of leads-protein complexes through MD simulation showed similar RMSD, RMSF, and H-bond occupancy profile compared to a reference inhibitor, suggesting stability throughout the simulation time. However, ZINC02639178 was found to satisfy the molecular enumeration the most compared to the other three leads. It may emerge as potential treatment option after extensive experimental studies. Communicated by Ramaswamy H. Sarma.

Structural basis for tailor-made selective PI3K α/β inhibitors: a computational perspective

PI3K α and β are Class IA PI3K isoforms that share a highly homologous ATP binding site, differing only in a few residues around the binding site.

Discovery of Potential Chemical Probe as Inhibitors of CXCL12 Using Ligand-Based Virtual Screening and Molecular Dynamic Simulation

CXCL12 are small pro-inflammatory chemo-attractant cytokines that bind to a specific receptor CXCR4 with a role in angiogenesis, tumor progression, metastasis, and cell survival. Globally, cancer metastasis is a major cause of morbidity and mortality. In this study, we targeted CXCL12 rather than the chemokine receptor (CXCR4) because most of the drugs failed in clinical trials due to unmanageable toxicities. Until now, no FDA approved medication has been available against CXCL12. Therefore, we aimed to find new inhibitors for CXCL12 through virtual screening followed by molecular dynamics simulation. For virtual screening, active compounds against CXCL12 were taken as potent inhibitors and utilized in the generation of a pharmacophore model, followed by validation against different datasets. Ligand based virtual screening was performed on the ChEMBL and in-house databases, which resulted in successive elimination through the steps of pharmacophore-based and score-based screenings, and finally, sixteen compounds of various interactions with significant crucial amino acid residues were selected as virtual hits. Furthermore, the binding mode of these compounds were refined through molecular dynamic simulations. Moreover, the stability of protein complexes, Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), and radius of gyration were analyzed, which led to the identification of three potent inhibitors of CXCL12 that may be pursued in the drug discovery process against cancer metastasis.

Understanding the binding affinity of noscapines with protease of SARS-CoV-2 for COVID-19 using MD simulations at different temperatures

The current outbreak of a novel coronavirus, named as SARS-CoV-2 causing COVID-19 occurred in 2019, is in dire need of finding potential therapeutic agents. Recently, ongoing viral epidemic due to coronavirus (SARS-CoV-2) primarily affected mainland China that now threatened to spread to populations in most countries of the world. In spite of this, there is currently no antiviral drug/ vaccine available against coronavirus infection, COVID-19. In the present study, computer-aided drug design-based screening to find out promising inhibitors against the coronavirus (SARS-CoV-2) leads to infection, COVID-19. The lead therapeutic molecule was investigated through docking and molecular dynamics simulations. In this, binding affinity of noscapines(23B)-protease of SARS-CoV-2 complex was evaluated through MD simulations at different temperatures. Our research group has established that noscapine is a chemotherapeutic agent for the treatment of drug resistant cancers; however, noscapine was also being used as anti-malarial, anti-stroke and cough-suppressant. This study suggests for the first time that noscapine exerts its antiviral effects by inhibiting viral protein synthesis.