Identification Of Potential Lead Compounds Research Articles

Targeting human progesterone receptor (PR), through pharmacophore-based screening and molecular simulation revealed potent inhibitors against breast cancer

Breast cancer, the prevailing malignant tumor among women, is linked to progesterone and its receptor (PR) in both tumorigenesis and treatment responsiveness. Despite thorough investigation, the precise molecular mechanisms of progesterone in breast cancer remain unclear. The human progesterone receptor (PR) serves as an essential therapeutic target for breast cancer treatment, warranting the rapid design of small molecule therapeutics that can effectively inhibit HPR. By employing cutting-edge computational techniques like molecular screening, simulation, and free energy calculation, the process of identifying potential lead molecules from natural products has been significantly expedited. In this study, we employed pharmacophore-based virtual screening and molecular simulations to identify natural product-based inhibitors of human progesterone receptor (PR) in breast cancer treatment. High-throughput molecular screening of traditional Chinese medicine (TCM) and zinc databases was performed, leading to the identification of potential lead compounds. The analysis of binding modes for the top five compounds from both database provides valuable structural insights into the inhibition of HPR for breast cancer treatment. The top five hits exhibited enhanced stability and compactness compared to the reference compound. In conclusion, our study provides valuable insights for identifying and refining lead compounds as HPR inhibitors.

Identification of Potential Lead Compounds Targeting Novel Druggable Cavity of SARS-CoV-2 Spike Trimer by Molecular Dynamics Simulations.

The global pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become an urgent public health problem. Spike (S) protein mediates the fusion between the virus and the host cell membranes, consequently emerging as an important target of drug design. The lack of comparisons of in situ full-length S homotrimer structures in different states hinders understanding the structures and revealing the function, thereby limiting the discovery and development of therapeutic agents. Here, the steady-state structures of the in situ full-length S trimer in closed and open states (Sclosed and Sopen) were modeled with the constraints of density maps, associated with the analysis of the dynamic structural differences. Subsequently, we identified various regions with structure and property differences as potential binding pockets for ligands that promote the formation of inactive trimeric protein complexes. By using virtual screening strategy and a newly defined druggable cavity, five ligands were screened with potential bioactivities. Then molecular dynamic (MD) simulations were performed on apo protein structures and ligand bound complexes to reveal the conformational changes upon ligand binding. Our simulation results revealed that sulforaphane (SFN), which has the best binding affinity, could inhibit the conformational changes of S homotrimer that would occur during the viral membrane fusion. Our results could aid in the understanding of the regulation mechanism of S trimer aggregation and the structure-activity relationship, facilitating the development of potential antiviral agents.

Mechanistic insights into the role of vitamin D and computational identification of potential lead compounds for Parkinson's disease.

Parkinson's disease (PD) is the second most common neurodegenerative disorder that affects dopaminergic neurons in the midbrain. A recent study suggests that Orphan Nuclear Receptor 1 (NURR1) impairment may contribute to PD pathogenesis. Our study found three potent agonists for NURR1 protein based on structural and ligand-based screening methods. The pharmacophore is comprised of a hydrogen bond donor, a hydrophobic group, and two aromatic rings (DHRR). The Pharmacophore screening method screened 3142 compounds, of which 3 were screened using structure-based screening. An analysis of the molecules using Molecular Mechanics-Generalized Born Surface Area (binding free energy) revealed a range of -46.77 to -59.06 Kcal/mol. After that, chemical reactivity was investigated by density functional theory, and molecular dynamics simulation was performed (protein-ligand stability). Based on the computational studies, Lifechemical_16901310, Maybridge_2815310, and NPACT_392450 are promising agonists with respect to NURR1. To confirm the potency of the identified compounds, further validation and experiments must be conducted.

In silico screening of phytocompounds in Simarouba glauca towards identification of potential lead compounds to treat dengue viral infection

In silico screening of phytocompounds in Simarouba glauca towards identification of potential lead compounds to treat dengue viral infection

Identification of potential lead compounds against BACE1 through in-silico screening of phytochemicals of Medhya rasayana plants for Alzheimer's disease management

Alzheimer's disease is a progressive and irreversible neurodegenerative disease that accounts for 70–80% of dementia in the elderly. According to recent clinical data, the incidence of the disease is exponentially increasing with age. Beta-site amyloid precursor protein cleaving enzyme1 (BACE1) is an important molecule involved in the pathogenesis of Alzheimer's disease due to its early role in the amyloid cascade. Cleavage of amyloid precursor protein by BACE1 is the rate-limiting step leading to the production and aggregation of amyloid-beta plaques. A number of natural products are being identified as non-competitive BACE1 inhibitors. In Ayurveda, Medhya rasayana is a group of medicinal herbs, specifically used for managing neurological disorders and is known to be effective in improving cognitivity and intellect. This study aimed to analyze the pharmacological activity of bio-active compounds in Medhya rasayana plants against BACE1, employing structure-based docking approach. 11 compounds out of 876 were identified as potential hits, based on docking scores, binding energies, and interactions with the critical residues of BACE1. Possible neurological activities of these compounds were predicted using PASS server. Out of the 11 compounds screened, two compounds, ‘Convolidine’ from the plant Convolvulus pleuricaulis Choisy and ‘N-(4-hydroxybutyl) phthalimide’ from Glycyrrhiza glabra satisfied the pharmacological parameters of Lipinski rule of filtering and ADMET prediction. The binding stability of these compounds against BACE1 was confirmed by molecular dynamic simulation and post dynamic MM/GBSA calculations. Detailed analysis of the interaction with the critical amino acids in the active site revealed the possible inhibitory potential of these compounds of medicinal plant origin against BACE1.

In silico Screening of Natural Phytocompounds Towards Identification of Potential Lead Compounds to Treat COVID-19.

COVID-19 is one of the members of the coronavirus family that can easily assail humans. As of now, 10 million people are infected and above two million people have died from COVID-19 globally. Over the past year, several researchers have made essential advances in discovering potential drugs. Up to now, no efficient drugs are available on the market. The present study aims to identify the potent phytocompounds from different medicinal plants (Zingiber officinale, Cuminum cyminum, Piper nigrum, Curcuma longa, and Allium sativum). In total, 227 phytocompounds were identified and screened against the proteins S-ACE2 and Mpro through structure-based virtual screening approaches. Based on the binding affinity score, 30 active phytocompounds were selected. Amongst, the binding affinity for beta-sitosterol and beta-elemene against S-ACE2 showed −12.0 and −10.9 kcal/mol, respectively. Meanwhile, the binding affinity for beta-sitosterol and beta-chlorogenin against Mpro was found to be −9.7 and −8.4 kcal/mol, respectively. Further, the selected compounds proceeded with molecular dynamics simulation, prime MM-GBSA analysis, and ADME/T property checks to understand the stability, interaction, conformational changes, binding free energy, and pharmaceutical relevant parameters. Moreover, the hotspot residues such as Lys31 and Lys353 for S-ACE2 and catalytic dyad His41 and Cys145 for Mpro were actively involved in the inhibition of viral entry. From the in silico analyses, we anticipate that this work could be valuable to ongoing novel drug discovery with potential treatment for COVID-19.

A reliable and reproducible assay for determining the effect of natural product on macrophages lipid uptake and cholesterol efflux: A case study of maslinic acid

Macrophage foam cell formation represents a key feature that contributes to the development of atherosclerotic lesions. Assessment of cardioprotective natural compounds targeting macrophage foam cell formation processes including lipid uptake and cholesterol efflux could lead to the identification of potential lead compounds for development into novel anti-atherosclerotic drugs. In this case study, maslinic acid, a natural product was used to study the effect on lipid uptake and cholesterol efflux in THP-1-derived macrophages.  Oil red O (ORO) staining and 1,1'-dioctadecyl-3,3,3',3'-tetramethylindocarbocyanine perchlorate-labeled oxidized low-density lipoprotein (Dil-labeled oxLDL) uptake assays were performed to determine lipid uptake by macrophages while cholesterol efflux was assessed using 3-hexanoyl-NBD labeled cholesterol. ORO-stained images were further analyzed using ImageJ analysis software to determine intracellular lipid droplets accumulation and flow cytometric analysis of mean fluorescence intensity were obtained to quantify Dil-labeled oxLDL uptake by macrophages. Meanwhile, 3-hexanoyl-NBD labeled cholesterol uptake and efflux from THP-1-derived macrophages were characterized. The fluorescence intensity values obtained from the medium and cell lysates were then converted into percentage of cholesterol efflux. The results have shown that incubation with maslinic acid suppressed oxLDL-induced macrophage foam cell formation which may be contributed from its effect in reducing lipid uptake and enhancing cholesterol efflux. In conclusion, the optimized ORO staining, Dil-labeled oxLDL uptake, and fluorescent-labeled cholesterol efflux assays provide reproducible and reliable results for assessment of foam cells formation.

Molecular Docking and In Silico Cogitation Validate Mefenamic Acid Prodrugs as Human Cyclooxygenase-2 Inhibitor.

In silico molecular docking is an efficient technique for drug design that predicts the optimized orientation of the ligand against a specific drug target. This is a cost-effective and time-saving technique that requires limited manpower. Nonsteroidal anti-inflammatory drugs (NSAIDs) are commonly prescribed drugs in various prescriptions. The drawbacks with NSAIDs in its long-term usage are gastric irritation, bleeding, and perforation. Prodrug approach is a commonly used method to overcome these side effects. In this study, the reported prodrugs of mefenamic acid were utilized to validate the molecular docking simulation process by comparing obtained in silico results with the reported in vivo results. The molecules were evaluated for their binding affinity against human cyclooxygenase-2 enzyme as well as their pharmacokinetics profile is predicted on the basis of Lipinski's and Veber rule. The in silico result showed high degree similarity with experimental results. This confirms the efficiency and reliability of the molecular docking technique for identification of potential lead compounds.

E7 oncoprotein of human papillomavirus: Structural dynamics and inhibitor screening study

Human papillomavirus (HPV) has been the primary causative agent of cervical cancer, the most threatening cancer affecting millions of women worldwide. HPV, a small non enveloped DNA virus of high and low risk types contain intrinsically disordered region and it also plays significant role in the development of cervical cancer. HPV E7 contains an ordered Zinc finger motif that binds to pRB and alters its function. It utilizes both disordered N-terminal and structured C-terminal regions for cellular transformation. In this study, we have focused extensively on the evolutionary relationships of E7 among various HPV types and generated a 3D homology model of full length HPV 16 E7, since the structure have not been solved till date. We also analysed the stable conformation and atomic flexibility of modelled E7 through molecular dynamics simulation at 100 ns. To understand the disordered based binding sites of E7 oncoprotein, Molecular recognition features (MoRFs) analysis was carried out on the E7 oncoprotein. The validated model was taken forward for the identification of potential lead compounds and the most prominent compounds were selected for the molecular dynamics simulation of the 100 ns for the stability analysis. Overall, this study highlights the holistic E7 regions including important disordered based binding sites analysed through the MoRFs. The potential inhibitor compound that targets the structured C-terminal region of E7 oncoprotein were subjected for the pharmacological properties analysis and further validated for the binding modes of the compounds with the target structure. This study helps in providing a better intuition to develop a potent anti-HPV agent.

Modelling and receptor-based virtual screening studies of GPR139

GPR139 belongs to Class A of GPCRs family and plays key role in molecular signalling through activation of receptors and promotes exchange of Guanosine Di-phosphate (GDP) to Guanosine Tri-phosphate (GTP). GPR139 is specifically involved in neuropeptide signalling pathway, phospholipase C-activating G-protein-coupled receptor signalling pathway and is coupled to IP3 second messenger (phospholipase C activating). In this article, we report 3D structure prediction of GPR139 using threading and ab initio methods. The models were validated and optimised. Molecular dynamics simulation of GPR139 was performed for 300 ns to investigate the dynamic perturbations in predicted model, particularly variations in seven transmembrane domains and active site residues. The active site residues were identified and the 3D model was screened against large databases of chemical libraries for identification of potential lead compounds which could bind to GPR139 receptor and activate signal transduction. The screened compounds were further refined through free binding energy calculations (ΔGbind) using generalised born and surface area continuum solvation for estimating ligand-binding affinities to GPR139.

Structural insights on identification of potential lead compounds targeting WbpP in Vibrio vulnificus through structure-based approaches

WbpP encoding UDP-GlcNAC C4 epimerase is responsible for the activation of virulence factor in marine pathogen Vibrio vulnificus (V. vulnificus) and it is linked to many aquatic diseases, thus making it a potential therapeutic target. There are few reported compounds that include several natural products and synthetic compounds targeting Vibrio sp, but specific inhibitor targeting WbpP are unavailable. Here, we performed structure-based virtual screening using chemical libraries such as Binding, TOSLab and Maybridge to identify small molecule inhibitors of WbpP with better drug-like properties. Deficient structural information forced to model the structure and the stable protein structure was obtained through 30 ns of MD simulations. Druggability regions are focused for new lead compounds and our screening protocol provides fast docking of entire small molecule library with screening criteria of ADME/Lipinski filter/Docking followed by re-docking of top hits using a method that incorporates both ligand and protein flexibility. Docking conformations of lead molecules interface displays strong H-bond interactions with the key residues Gly101, Ser102, Val195, Tyr165, Arg298, Val209, Ser142, Arg233 and Gln200. Subsequently, the top-ranking compounds were prioritized using the molecular dynamics simulation-based conformation and stability studies. Our study suggests that the proposed compounds may aid as a starting point for the rational design of novel therapeutic agents.

A quantitative affinity-based technique for the identification of potential lead compounds

Here, we describe the development and validation of a quantitative analytical method for rapid evaluation of protein–compound interactions. The method uses size-exclusion chromatography in a 96-well format with liquid chromatography/mass spectrometry (qSEC–LC/MS) by which the amount of a compound that was originally in complex with a target protein is determined as an indicator of the binding affinity. Proof of concept of this new analytical approach was performed using a thrombin-inhibitor model. The results showed that the qSEC–LC/MS could be developed into an effective affinity-based analytical technique, despite a few limitations such as difficulty in determining the Kd value accurately.

Pharmacophore modeling and virtual screening for designing potential 5-Lipoxygenase inhibitors

Inhibitors of the 5-Lipoxygenase (5-LOX) pathway have a therapeutic potential in a variety of inflammatory disorders such as asthma. In this study, chemical feature based pharmacophore models of inhibitors of 5-LOX have been developed with the aid of HipHop and HypoGen modules within Catalyst program package. The best quantitative pharmacophore model, Hypo1, which has the highest correlation coefficient (0.97), consists of two hydrogen-bond acceptors, one hydrophobic feature and one ring aromatic feature. Hypo1 was further validated by test set and cross validation method. The application of the model shows great success in predicting the activities of 65 known 5-LOX inhibitors in our test set with a correlation coefficient of 0.85 with a cross validation of 95% confidence level, proving that the model is reliable in identifying structurally diverse compounds for inhibitory activity against 5-LOX. Furthermore, Hypo1 was used as a 3D query for screening Maybridge and NCI databases within catalyst and also drug like compounds obtained from Enamine Ltd, which follow Lipinski’s rule of five. The hit compounds were subsequently subjected to filtering by docking and visualization, to identify the potential lead molecules. Finally 5 potential lead compounds, identified in the above process, were evaluated for their inhibitory activities. These studies resulted in the identification of two compounds with potent inhibition of 5-LOX activity with IC 50 of 14 μM and 35 μM, respectively. These studies thus validate the pharmacophore model generated and suggest the usefulness of the model in screening of various small molecule libraries and identification of potential lead compounds for 5-LOX inhibition.

Structure–Function Relation of Efomycines, a Family of Small-Molecule Inhibitors of Selectin Functions

Selectin-mediated leukocyte adhesion to endothelia, the crucial first step initiating the pathogenic cascade of inflammation, is an attractive target for specific therapies. The small-molecule macrolide, efomycine M, inhibits selectin-mediated leukocyte adhesion in vitro and in vivo, and effectively alleviates inflammatory disorders in vivo. To define the molecular basis of the therapeutically relevant antiadhesive properties of efomycines, several new species of this family were purified and/or synthesized. Efomycines E and G were isolated from Steptomyces BS1261. Efomycine O was synthesized by Lewis acid-catalyzed acetalization and efomycine M was generated by base-catalyzed deglycosylation. Efomycine S resulted from ester cleavage of the macrolide ring system, and efomycine T represents the peracetylated form of efomycine M. When the functional activity of efomycines on adhesion of leukocytes to vascular endothelium was studied, some remarkable differences between the compounds became apparent, inasmuch as efomycines E, G, M, and O significantly inhibited adhesion of both human and porcine leukocytes to the vascular endothelium, whereas efomycines S and T did not show any biological activity. A novel docking engine (ProPose), generating an improved, fully configurable protein-ligand interaction model, demonstrated that biological activities of efomycines can be predicted in silico, thus highlighting the utility of such combinatorial approaches.

Bioprospecting Rumphius's Ambonese Herbal: Volume I

Collaborating with traditional healers remains a valid method for the identification of potential lead compounds for novel pharmaceuticals. However, the knowledge of these traditional healers is rapidly being lost. Historic herbal texts provide a unique window to identify plants whose specific uses are no longer known. We have identified nine plants in the 17th century Ambonese Herbal: Volume I which were documented as having medicinal properties but which have not been examined in the current literature. We demonstrate that by evaluating plant and herb efficacy reports from historic texts, in the context of comprehensive modern databases such as NAPRALERT™, we can ultimately identify candidate specimens deserving further pharmacological study.

Web‐Based Tools for Mining the NCI Databases for Anticancer Drug Discovery.

In this paper, we describe the development of a set of integrated Web-based tools for mining the National Cancer Institute's (NCI) anticancer databases for anticancer drug discovery. For data mining, three different correlation algorithms were implemented, which included the commonly used Pearson's correlation algorithm available from the NCI's COMPARE program, the Spearman's and Kendall's correlation algorithms. In addition, we implemented the p-value test to evaluate the significance of the correlation results. These Web-based data mining tools allow robust analysis of the correlation between the in vitro anticancer activity of the drugs in the NCI anticancer database, the protein levels and mRNA levels of molecular targets (genes) in the NCI 60 human cancer cell lines for identification of potential lead compounds for a specific molecular target and for study of the molecular mechanism action of a drug. Examples were provided to identify PKC ligands using a lead compound and to identify potential ErbB-2 inhibitors using the mRNA levels of ErbB-2 in the NCI 60 tumor cell lines.

Web-based tools for mining the NCI databases for anticancer drug discovery.

In this paper, we describe the development of a set of integrated Web-based tools for mining the National Cancer Institute's (NCI) anticancer databases for anticancer drug discovery. For data mining, three different correlation algorithms were implemented, which included the commonly used Pearson's correlation algorithm available from the NCI's COMPARE program, the Spearman's and Kendall's correlation algorithms. In addition, we implemented the p-value test to evaluate the significance of the correlation results. These Web-based data mining tools allow robust analysis of the correlation between the in vitro anticancer activity of the drugs in the NCI anticancer database, the protein levels and mRNA levels of molecular targets (genes) in the NCI 60 human cancer cell lines for identification of potential lead compounds for a specific molecular target and for study of the molecular mechanism action of a drug. Examples were provided to identify PKC ligands using a lead compound and to identify potential ErbB-2 inhibitors using the mRNA levels of ErbB-2 in the NCI 60 tumor cell lines.