Amomum subulatum: A treasure trove of anti-cancer compounds targeting TP53 protein using in vitro and in silico techniques.

Cancer is a primary global health concern, and researchers seek innovative approaches to combat the disease. Clinical bioinformatics and high-throughput proteomics technologies provide powerful tools to explore cancer biology. Medicinal plants are considered effective therapeutic agents, and computer-aided drug design (CAAD) is used to identify novel drug candidates from plant extracts. The tumour suppressor protein TP53 is an attractive target for drug development, given its crucial role in cancer pathogenesis. This study used a dried extract of Amomum subulatum seeds to identify phytocompounds targeting TP53 in cancer. We apply qualitative tests to determine its phytochemicals (Alkaloid, Tannin, Saponin, Phlobatinin, and Cardic glycoside), and found that alkaloid composed of 9.4% ± 0.04% and Saponin 1.9% ± 0.05% crude chemical constituent. In the results of DPPH Analysis Amomum subulatum Seeds founded antioxidant activity, and then we verified via observing methanol extract (79.82%), BHT (81.73%), and n-hexane extract (51.31%) found to be positive. For Inhibition of oxidation, we observe BHT is 90.25%, and Methanol (83.42%) has the most significant proportion of linoleic acid oxidation suppression. We used diverse bioinformatics approaches to evaluate the effect of A. subulatum seeds and their natural components on TP53. Compound-1 had the best pharmacophore match value (53.92), with others ranging from 50.75 to 53.92. Our docking result shows the top three natural compounds had the highest binding energies (-11.10 to -10.3 kcal/mol). The highest binding energies (-10.9 to -9.2 kcal/mol) compound bonded to significant sections in the target protein's active domains with TP53. Based on virtual screening, we select top phytocompounds for targets which highly fit based on pharmacophore score and observe these compounds exhibited potent antioxidant activity and inhibited cancer cell inflammation in the TP53 pathway. Molecular Dynamics (MD) simulations indicated that the ligand was bound to the protein with some significant conformational changes in the protein structure. This study provides novel insights into the development of innovative drugs for the treatment of cancer disorders.

Virtual screening of Indonesian herbal compounds as COVID-19 supportive therapy: machine learning and pharmacophore modeling approaches.

BACKGROUND: The number of COVID-19 cases continues to grow in Indonesia. This phenomenon motivates researchers to find alternative drugs that function for prevention or treatment. Due to the rich biodiversity of Indonesian medicinal plants, one alternative is to examine the potential of herbal medicines to support COVID therapy. This study aims to identify potential compound candidates in Indonesian herbal using a machine learning and pharmacophore modeling approaches. METHODS: We used three classification methods that had different decision-making processes: support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF). For the pharmacophore modeling approach, we performed a structure-based analysis on the 3D structure of the main protease SARS-CoV-2 (3CLPro) and repurposed SARS, MERS, and SARS-CoV-2 drugs identified from the literature as datasets in the ligand-based method. Lastly, we used molecular docking to analyze the interactions between the 3CLpro and 14 hit compounds from the Indonesian Herbal Database (HerbalDB), with lopinavir as a positive control. RESULTS: From the molecular docking analysis, we found six potential compounds that may act as the main proteases of the SARS-CoV-2 inhibitor: hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4'-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside. CONCLUSIONS: Our layered virtual screening with machine learning and pharmacophore modeling approaches provided a more objective and optimal virtual screening and avoided subjective decision making of the results. Herbal compounds from the screening, i.e. hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4'-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside are potential antiviral candidates for SARS-CoV-2. Moringa oleifera and Psidium guajava that consist of those compounds, could be an alternative option as COVID-19 herbal preventions.

Combined Pharmacophore and Grid-Independent Molecular Descriptors (GRIND) Analysis to Probe 3D Features of Inositol 1,4,5-Trisphosphate Receptor (IP3R) Inhibitors in Cancer.

Inositol 1, 4, 5-trisphosphate receptor (IP3R)-mediated Ca2+ signaling plays a pivotal role in different cellular processes, including cell proliferation and cell death. Remodeling Ca2+ signals by targeting the downstream effectors is considered an important hallmark in cancer progression. Despite recent structural analyses, no binding hypothesis for antagonists within the IP3-binding core (IBC) has been proposed yet. Therefore, to elucidate the 3D structural features of IP3R modulators, we used combined pharmacoinformatic approaches, including ligand-based pharmacophore models and grid-independent molecular descriptor (GRIND)-based models. Our pharmacophore model illuminates the existence of two hydrogen-bond acceptors (2.62 Å and 4.79 Å) and two hydrogen-bond donors (5.56 Å and 7.68 Å), respectively, from a hydrophobic group within the chemical scaffold, which may enhance the liability (IC50) of a compound for IP3R inhibition. Moreover, our GRIND model (PLS: Q2 = 0.70 and R2 = 0.72) further strengthens the identified pharmacophore features of IP3R modulators by probing the presence of complementary hydrogen-bond donor and hydrogen-bond acceptor hotspots at a distance of 7.6-8.0 Å and 6.8-7.2 Å, respectively, from a hydrophobic hotspot at the virtual receptor site (VRS). The identified 3D structural features of IP3R modulators were used to screen (virtual screening) 735,735 compounds from the ChemBridge database, 265,242 compounds from the National Cancer Institute (NCI) database, and 885 natural compounds from the ZINC database. After the application of filters, four compounds from ChemBridge, one compound from ZINC, and three compounds from NCI were shortlisted as potential hits (antagonists) against IP3R. The identified hits could further assist in the design and optimization of lead structures for the targeting and remodeling of Ca2+ signals in cancer.

ePharmer: An integrated and graphical software for pharmacophore-based virtual screening.

Pharmacophore-based virtual screening (VS) has emerged as an efficient computer-aided drug design technique when appraising multiple ligands with similar structures or targets with unknown crystal structures. Current pharmacophore modeling and analysis software suffers from inadequate integration of mainstream methods and insufficient user-friendly program interface. In this study, we propose a stand-alone, integrated, graphical software for pharmacophore-based VS, termed ePharmer. Both ligand-based and structure-based pharmacophore generation methods were integrated into a compact architecture. Fine-grained modules were carefully organized into the computing, integration, and visualization layers. Graphical design covered the global user interface and specific user operations including editing, evaluation, and task management. Metabolites prediction analysis with the chosen VS result is provided for preselection of wet experiments. Moreover, the underlying computing units largely adopted the preliminary work of our research team. The presented software is currently in client use and will be released for both professional and nonexpert users. Experimental results verified the favorable computing capability, user convenience, and case performance of the proposed software.

Pharmacophore-Based Virtual Screening, Quantum Mechanics Calculations, and Molecular Dynamics Simulation Approaches Identified Potential Natural Antiviral Drug Candidates against MERS-CoV S1-NTD.

Middle East respiratory syndrome coronavirus (MERS-CoV) is a highly infectious zoonotic virus first reported into the human population in September 2012 on the Arabian Peninsula. The virus causes severe and often lethal respiratory illness in humans with an unusually high fatality rate. The N-terminal domain (NTD) of receptor-binding S1 subunit of coronavirus spike (S) proteins can recognize a variety of host protein and mediates entry into human host cells. Blocking the entry by targeting the S1-NTD of the virus can facilitate the development of effective antiviral drug candidates against the pathogen. Therefore, the study has been designed to identify effective antiviral drug candidates against the MERS-CoV by targeting S1-NTD. Initially, a structure-based pharmacophore model (SBPM) to the active site (AS) cavity of the S1-NTD has been generated, followed by pharmacophore-based virtual screening of 11,295 natural compounds. Hits generated through the pharmacophore-based virtual screening have re-ranked by molecular docking and further evaluated through the ADMET properties. The compounds with the best ADME and toxicity properties have been retrieved, and a quantum mechanical (QM) based density-functional theory (DFT) has been performed to optimize the geometry of the selected compounds. Three optimized natural compounds, namely Taiwanhomoflavone B (Amb23604132), 2,3-Dihydrohinokiflavone (Amb23604659), and Sophoricoside (Amb1153724), have exhibited substantial docking energy >-9.00 kcal/mol, where analysis of frontier molecular orbital (FMO) theory found the low chemical reactivity correspondence to the bioactivity of the compounds. Molecular dynamics (MD) simulation confirmed the stability of the selected natural compound to the binding site of the protein. Additionally, molecular mechanics generalized born surface area (MM/GBSA) predicted the good value of binding free energies (ΔG bind) of the compounds to the desired protein. Convincingly, all the results support the potentiality of the selected compounds as natural antiviral candidates against the MERS-CoV S1-NTD.

Non-Extensive Fragmentation of Natural Products and Pharmacophore-Based Virtual Screening as a Practical Approach to Identify Novel Promising Chemical Scaffolds.

Fragment-based drug design (FBDD) and pharmacophore modeling have proven to be efficient tools to discover novel drugs. However, these approaches may become limited if the collection of fragments is highly repetitive, poorly diverse, or excessively simple. In this article, combining pharmacophore modeling and a non-classical type of fragmentation (herein called non-extensive) to screen a natural product (NP) library may provide fragments predicted as potent, diverse, and developable. Initially, we applied retrosynthetic combinatorial analysis procedure (RECAP) rules in two versions, extensive and non-extensive, in order to deconstruct a virtual library of NPs formed by the databases Traditional Chinese Medicine (TCM), AfroDb (African Medicinal Plants database), NuBBE (Nuclei of Bioassays, Biosynthesis, and Ecophysiology of Natural Products), and UEFS (Universidade Estadual de Feira de Santana). We then developed a virtual screening (VS) using two groups of natural-product-derived fragments (extensive and non-extensive NPDFs) and two overlapping pharmacophore models for each of 20 different proteins of therapeutic interest. Molecular weight, lipophilicity, and molecular complexity were estimated and compared for both types of NPDFs (and their original NPs) before and after the VS proceedings. As a result, we found that non-extensive NPDFs exhibited a much higher number of chemical entities compared to extensive NPDFs (45,355 vs. 11,525 compounds), accounting for the larger part of the hits recovered and being far less repetitive than extensive NPDFs. The structural diversity of both types of NPDFs and the NPs was shown to diminish slightly after VS procedures. Finally, and most interestingly, the pharmacophore fit score of the non-extensive NPDFs proved to be not only higher, on average, than extensive NPDFs (56% of cases) but also higher than their original NPs (69% of cases) when all of them were also recognized as hits after the VS. The findings obtained in this study indicated that the proposed cascade approach was useful to enhance the probability of identifying innovative chemical scaffolds, which deserve further development to become drug-sized candidate compounds. We consider that the knowledge about the deconstruction degree required to produce NPDFs of interest represents a good starting point for eventual synthesis, characterization, and biological activity studies.

Deciphering the Interactions of Bioactive Compounds in Selected Traditional Medicinal Plants against Alzheimer's Diseases via Pharmacophore Modeling, Auto-QSAR, and Molecular Docking Approaches.

Neurodegenerative diseases, for example Alzheimer's, are perceived as driven by hereditary, cellular, and multifaceted biochemical actions. Numerous plant products, for example flavonoids, are documented in studies for having the ability to pass the blood-brain barrier and moderate the development of such illnesses. Computer-aided drug design (CADD) has achieved importance in the drug discovery world; innovative developments in the aspects of structure identification and characterization, bio-computational science, and molecular biology have added to the preparation of new medications towards these ailments. In this study we evaluated nine flavonoid compounds identified from three medicinal plants, namely T. diversifolia, B. sapida, and I. gabonensis for their inhibitory role on acetylcholinesterase (AChE), butyrylcholinesterase (BChE) and monoamine oxidase (MAO) activity, using pharmacophore modeling, auto-QSAR prediction, and molecular studies, in comparison with standard drugs. The results indicated that the pharmacophore models produced from structures of AChE, BChE and MAO could identify the active compounds, with a recuperation rate of the actives found near 100% in the complete ranked decoy database. Moreso, the robustness of the virtual screening method was accessed by well-established methods including enrichment factor (EF), receiver operating characteristic curve (ROC), Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC), and area under accumulation curve (AUAC). Most notably, the compounds' pIC50 values were predicted by a machine learning-based model generated by the AutoQSAR algorithm. The generated model was validated to affirm its predictive model. The best models achieved for AChE, BChE and MAO were models kpls_radial_17 (R2 = 0.86 and Q2 = 0.73), pls_38 (R2 = 0.77 and Q2 = 0.72), kpls_desc_44 (R2 = 0.81 and Q2 = 0.81) and these externally validated models were utilized to predict the bioactivities of the lead compounds. The binding affinity results of the ligands against the three selected targets revealed that luteolin displayed the highest affinity score of -9.60 kcal/mol, closely followed by apigenin and ellagic acid with docking scores of -9.60 and -9.53 kcal/mol, respectively. The least binding affinity was attained by gallic acid (-6.30 kcal/mol). The docking scores of our standards were -10.40 and -7.93 kcal/mol for donepezil and galanthamine, respectively. The toxicity prediction revealed that none of the flavonoids presented toxicity and they all had good absorption parameters for the analyzed targets. Hence, these compounds can be considered as likely leads for drug improvement against the same.

In Silico Strategy for Targeting the mTOR Kinase at Rapamycin Binding Site by Small Molecules.

Computer aided drug-design methods proved to be powerful tools for the identification of new therapeutic agents. We employed a structure-based workflow to identify new inhibitors targeting mTOR kinase at rapamycin binding site. By combining molecular dynamics (MD) simulation and pharmacophore modelling, a simplified structure-based pharmacophore hypothesis was built starting from the FKBP12-rapamycin-FRB ternary complex retrieved from RCSB Protein Data Bank (PDB code 1FAP). Then, the obtained model was used as filter to screen the ZINC biogenic compounds library, containing molecules derived from natural sources or natural-inspired compounds. The resulting hits were clustered according to their similarity; moreover, compounds showing the highest pharmacophore fit-score were chosen from each cluster. The selected molecules were subjected to docking studies to clarify their putative binding mode. The binding free energy of the obtained complexes was calculated by MM/GBSA method and the hits characterized by the lowest ΔGbind values were identified as potential mTOR inhibitors. Furthermore, the stability of the resulting complexes was studied by means of MD simulation which revealed that the selected compounds were able to form a stable ternary complex with FKBP12 and FRB domain, thus underlining their potential ability to inhibit mTOR with a rapamycin-like mechanism.

Using Chou's 5-steps rule to study pharmacophore-based virtual screening of SARS-CoV-2 Mpro inhibitors.

Recently emerged SARS-CoV-2 is the cause of the ongoing outbreak of COVID-19. It is responsible for the deaths of millions of people and has caused global economic and social disruption. The numbers of COVID-19 cases are increasing exponentially across the world. Control of this pandemic disease is challenging because there is no effective drug or vaccine available against this virus and this situation demands an urgent need for the development of anti-SARS-CoV-2 potential medicines. In this regard, the main protease (Mpro) has emerged as an essential drug target as it plays a vital role in virus replication and transcription. In this research, we have identified two novel potent inhibitors of the Mpro (PubChem3408741 and PubChem4167619) from PubChem database by pharmacophore-based high-throughput virtual screening. The molecular docking, toxicity, and pharmacophore analysis indicate that these compounds may act as potential anti-viral candidates. The molecular dynamic simulation along with the binding free energy calculation by MMPBSA showed that these compounds bind to Mpro enzyme with high stability over 50 ns. Our results showed that two compounds: PubChem3408741 and PubChem4167619 had the binding free energy of - 94.02 kJ mol-1 and - 122.75 kJ mol-1, respectively, as compared to reference X77 (- 76.48 kJ mol-1). Based on our work's findings, we propose that these compounds can be considered as lead molecules for targeting Mpro enzyme and they can be potential SARS-CoV-2 inhibitors. These inhibitors could be tested in vitro and explored for effective drug development against COVID-19.

Discovery of new potential CDK2/VEGFR2 type II inhibitors by fragmentation and virtual screening of natural products.

Cyclin-Dependent Kinase 2 (CDK2) and Vascular Endothelial Growth Factor Receptor (VEGFR2) have largely been considered as attractive targets for developing anticancer agents. However, there is no dual inhibitor commercially available in the market that interacts simultaneously with the allosteric back pocket of these enzymes. We applied a combined computational strategy that started with the generation of two overlapping pharmacophore models of both kinases at 'inactive' conformation. Next, several virtual libraries of natural products, including the databases TCM (Traditional Chinese Medicine), UEFS (Universidade Estadual de Feira de Santana), NuBBE (Nuclei of Bioassays, Biosynthesis, and Ecophysiology of Natural Products) and AfroDb (African Medicinal Plants Database) were deconstructed using a non-extensive version of the approach RECAP (retrosynthetic combinatorial analysis procedure). These natural-product-derived fragments (NPDFs) were screened and merged into drug-sized compounds, which were filtered by Lipinski's Rule-of-five (Ro5) and docking. As a result, two pharmacophore models, namely Hypo1 and Hypo2, were developed with an accuracy of 0.94 and 0.84, respectively. Deconstruction of natural products produced a set of 16655 unique non-extensive NPDFs that were screened against both pharmacophore models. Finally, after merging, Ro5-filtering and docking, we obtained a set of 20 hit compounds predicted to be diverse, developable, synthesizable and potent. The computational strategy proved successful to find virtual candidates of kinase inhibitors and therefore contributes to the identification of innovative multi-target compounds with potential anticancer activity. Communicated by Ramaswamy H. Sarma.

Discovery of two novel hetero-tricyclic lead scaffolds as PDE5A inhibitor: virtual screening, molecular docking and pharmacophore modeling approach.

Phosphodiesterase 5A enzyme has been the upcoming and promising target in hypertension management. In this research, reported 270 bioactive natural products having antihypertensive potential were selected and docked against PDE5A using vLife MDS 4.6 software. Based on docking score, π-stacking, H-bond and ionic interactions with PDE5A, 82 tricyclic compounds were selected for further study. Protein residue Gln817A was associated in H-boding, Leu804A in ionic interaction whereas Val782A and Phe820A were associated in π-stacking interaction with ligand. In silico docking studies resulted in discovery of oxygen containing naphthofuran and nitrogen and oxygen containing pyrano quinolizine tricyclic lead scaffolds as novel PDE5A inhibitors. Additionally, developed pharmacophore model suggested that one centre of hydrogen bond acceptor, one aromatic centre and two aliphatic centres are minimum pharmacophoric features required in the molecule so as to show sildenafil like activity. The identified lead scaffolds would provide novel platform for drug discovery of bioactive natural products.

Antiproliferative and Carbonic Anhydrase II Inhibitory Potential of Chemical Constituents from Lycium shawii and Aloe vera: Evidence from In Silico Target Fishing and In Vitro Testing.

Lycium shawii Roem. & Schult and resin of Aloe vera (L.) BURM. F. are commonly used in Omani traditional medication against various ailments. Herein, their antiproliferative and antioxidant potential was explored. Bioassay-guided fractionation of the methanol extract of both plants led to the isolation of 14 known compounds, viz., 1-9 from L. shawii and 10-20 from A. vera. Their structures were confirmed by combined spectroscopic techniques including 1D (1H and 13C) and 2D (HMBC, HSQC, COSY) nuclear magnetic resonance (NMR), and electrospray ionization-mass spectrometry (ESI-MS). The cytotoxic potential of isolates was tested against the triple-negative breast cancer cell line (MDA-MB-231). Compound 5 exhibited excellent antiproliferative activity in a range of 31 µM, followed by compounds 1-3, 7, and 12, which depicted IC50 values in the range of 35-60 µM, while 8, 6, and 9 also demonstrated IC50 values >72 µM. Subsequently, in silico target fishing was applied to predict the most potential cellular drug targets of the active compounds, using pharmacophore modeling and inverse molecular docking approach. The extensive in silico analysis suggests that our compounds may target carbonic anhydrase II (CA-II) to exert their anticancer activities. When tested on CA-II, compounds 5 (IC50 = 14.4 µM), 12 (IC50 = 23.3), and 2 (IC50 = 24.4 µM) showed excellent biological activities in vitro. Additionally, the ethyl acetate fraction of both plants showed promising antioxidant activity. Among the isolated compounds, 4 possesses the highest antioxidant (55 µM) activity followed by 14 (241 µM). The results indicated that compound 4 can be a promising candidate for antioxidant drugs, while compound 5 is a potential candidate for anticancer drugs.

Pharmacophore-Based Virtual Screening Toward the Discovery of Novel Anti-echinococcal Compounds.

Echinococcosis is a serious helminthic zoonosis with a great impact on human health and livestock husbandry. However, the clinically used drugs (benzimidazoles) have a low cure rate, so alternative drugs are urgently needed. Currently, drug screenings for echinococcosis are mainly phenotype-based, and the efficiency of identifying active compounds is very low. With a pharmacophore model generated from the structures of active amino alcohols, we performed a virtual screening to discover novel compounds with anti-echinococcal activity. Sixty-two compounds from the virtual screening were tested on Echinococcus multilocularis protoscoleces, and 10 of these compounds were found to be active. After further evaluation of their cytotoxicity, S6 was selected along with two active amino alcohols for in vivo pharmacodynamic and pharmacokinetic studies. At the two tested doses (50 and 25 mg/kg), S6 inhibited the growth of E. multilocularis in mice (14.43 and 9.53%), but no significant difference between the treatment groups and control group was observed. Treatment with BTB4 and HT3 was shown to be ineffective. During the 28 days of treatment, the death of mice in the mebendazole, HT3, and BTB4 groups indicated their toxicity. The plasma concentration of S6 administered by both methods was very low, with the Cmax being only 1 ng/ml after oral administration and below the detection limit after intramuscular administration. In addition, the plasma concentrations of BTB4 and HT3 in vitro did not reach high enough levels to kill the parasites. The toxicities of these two amino alcohols indicated that they are not suitable for further development as anti-echinococcal drugs. However, further attempts should be made to increase the bioavailability of S6 and modify its structure. In this study, we demonstrate that pharmacophore-based virtual screenings with high drug identification efficiency could be used to find novel drugs for treating echinococcosis.

Discovery of Small-Molecule Activators for Glucose-6-Phosphate Dehydrogenase (G6PD) Using Machine Learning Approaches.

Glucose-6-Phosphate Dehydrogenase (G6PD) is a ubiquitous cytoplasmic enzyme converting glucose-6-phosphate into 6-phosphogluconate in the pentose phosphate pathway (PPP). The G6PD deficiency renders the inability to regenerate glutathione due to lack of Nicotine Adenosine Dinucleotide Phosphate (NADPH) and produces stress conditions that can cause oxidative injury to photoreceptors, retinal cells, and blood barrier function. In this study, we constructed pharmacophore-based models based on the complex of G6PD with compound AG1 (G6PD activator) followed by virtual screening. Fifty-three hit molecules were mapped with core pharmacophore features. We performed molecular descriptor calculation, clustering, and principal component analysis (PCA) to pharmacophore hit molecules and further applied statistical machine learning methods. Optimal performance of pharmacophore modeling and machine learning approaches classified the 53 hits as drug-like (18) and nondrug-like (35) compounds. The drug-like compounds further evaluated our established cheminformatics pipeline (molecular docking and in silico ADMET (absorption, distribution, metabolism, excretion and toxicity) analysis). Finally, five lead molecules with different scaffolds were selected by binding energies and in silico ADMET properties. This study proposes that the combination of machine learning methods with traditional structure-based virtual screening can effectively strengthen the ability to find potential G6PD activators used for G6PD deficiency diseases. Moreover, these compounds can be considered as safe agents for further validation studies at the cell level, animal model, and even clinic setting.

Repurposing of Streptomyces antibiotics as adenosine deaminase inhibitors by pharmacophore modeling, docking, molecular dynamics, and in vitro studies.

Adenosine deaminase (ADA) is an enzyme present in purine metabolic pathway. Its inhibitors are considered to be potent drug lead compounds against inflammatory and malignant diseases. This study aimed to test ADA inhibitory activity of some Streptomyces secondary metabolites by using computational and in vitro methods. The in silico screening of the inhibitory properties has been carried out using pharmacophore modeling, docking, and molecular dynamics studies. The in vitro validation of the selected antibiotics has been carried out by enzyme kinetics and fluorescent spectroscopic studies. The results indicated that novobiocin, an aminocoumarin antibiotic from Streptomyces niveus, has significant inhibition on ADA activity. Hence, the antibiotic can be used as a lead compound for the development of potential ADA inhibitors.

Identification of novel NAD(P)H dehydrogenase [quinone] 1 antagonist using computational approaches.

NAD(P)H: quinone oxidoreductase 1 (NQO1) inhibitors are proved as promising therapeutic agents against cancer. This study is to determine potent NAD(P)H-dependent NQO1 inhibitors with new scaffold. Pharmacophore-based three-dimensional (3D) QSAR model has been built based on 45 NQO1 inhibitors reported in the literature. The structure-function correlation coefficient graph represents the relationship between phase activity and phase predicted activity for training and test sets. A QSAR model statistics shows the excellent correlation of the generated model. Pharmacophore hypothesis (AARR) yielded a statistically significant 3D QSASR model with a correlation coefficient of r2 = 0.99 as well as an excellent predictive power. From the analysis of pharmacophore-based virtual screening using by SPEC database, 4093 hits were obtained and were further filtered using virtual screening filters (HTVS, SP, XP) through structure based molecular docking. Based on glide energy and docking score, seven lead compounds show better binding affinity compared to the co-crystal inhibitor. The results of induced fit docking and prime/MM-GBSA suggest that leads AN-153/J117103 and AT-138/KB09997 binding with the catalytic site. Further, to understanding the stability of identified lead compounds MD simulations were done. The lead AN-153/J117103 showed the strong binding stable of the protein-ligand complex. Also the computed drug likeness reveals potential of this compound to treat cancer. AbbreviationsNQO1NAD(P)H-quinine oxidoreductase 1CPHcommon pharmacophore hypothesisPLSpartial least squireHBDhydrogen bond donorSDstandard deviationXPextra precisionIFDinduced fit dockingMM-GBSAmolecular mechanics generalized born surface areaMDSmolecular dynamics simulationRMSDroot mean square deviationRMSFroot mean square fluctuationRMSEroot mean square errorADMEabsorption distribution metabolism excretionsCommunicated by Ramaswamy H. Sarma.

Hits Discovery on the Androgen Receptor: In Silico Approaches to Identify Agonist Compounds.

The androgen receptor (AR) is a transcription factor that plays a key role in sexual phenotype and neuromuscular development. AR can be modulated by exogenous compounds such as pharmaceuticals or chemicals present in the environment, and particularly by AR agonist compounds that mimic the action of endogenous agonist ligands and whether restore or alter the AR endocrine system functions. The activation of AR must be correctly balanced and identifying potent AR agonist compounds is of high interest to both propose treatments for certain diseases, or to predict the risk related to agonist chemicals exposure. The development of in silico approaches and the publication of structural, affinity and activity data provide a good framework to develop rational AR hits prediction models. Herein, we present a docking and a pharmacophore modeling strategy to help identifying AR agonist compounds. All models were trained on the NR-DBIND that provides high quality binding data on AR and tested on AR-agonist activity assays from the Tox21 initiative. Both methods display high performance on the NR-DBIND set and could serve as starting point for biologists and toxicologists. Yet, the pharmacophore models still need data feeding to be used as large scope undesired effect prediction models.

Structure-Based Pharmacophore Design and Virtual Screening for Novel Tubulin Inhibitors with Potential Anticancer Activity.

Tubulin inhibitors have been considered as potential drugs for cancer therapy. However, their drug resistance and serious side-effects are the main reasons for clinical treatment failure. Therefore, there is still an urgent need to develop effective therapeutic drugs. Herein, a structure-based pharmacophore model was developed based on the co-crystallized structures of the tubulin with a high resolution. The model including one hydrogen-bond acceptor feature, two aromatic features, and one hydrophobic feature was further validated using the Gunner-Henry score method. Virtual screening was performed by an integrated protocol that combines drug-likeness analysis, pharmacophore mapping, and molecular docking approaches. Finally, five hits were selected for biological evaluation. The results indicated that all these hits at the concentration of 40 µM showed an inhibition of more than 50% against five human tumor cells (MCF-7, U87MG, HCT-116, MDA-MB-231, and HepG2). Particularly, hit 1 effectively inhibited the proliferation of these tumor cells, with inhibition rates of more than 80%. The results of tubulin polymerization and colchicine-site competition assays suggested that hit 1 significantly inhibited tubulin polymerization by binding to the colchicine site. Thus, hit 1 could be used as a potential chemotherapeutic agent for cancer treatment. This work also demonstrated the potential of our screening protocol to identify biologically active compounds.

Computational evaluation of natural compounds as potential inhibitors of human PEPCK-M: an alternative for lung cancer therapy.

BACKGROUND: Lung cancer is the leading cause of cancer-related death worldwide. Among its subtypes, non-small cell lung cancer (NSCLC) is the most common. Recently, the mitochondrial isoform of the enzyme phosphoenolpyruvate carboxykinase (HsPEPCK-M) was identified as responsible for the metabolic adaptation in the NSCLC allowing tumor growth even under conditions of glucose deficiency. This adaptation is possible due to the role of HsPEPCK-M in gluconeogenesis, converting the oxaloacetate to phosphoenolpyruvate in the presence of GTP, which plays an important role in the energetic support of these tumors. In this context, it was shown that the inhibition or knockdown of this enzyme was able to induce apoptosis in NSCLC under low glucose conditions. PURPOSE: In this study, novel putative inhibitors were proposed for the human PEPCK-M (HsPEPCK-M) based on a computer-aided approach. METHODS: Comparative modeling was used to generate 3D models for HsPEPCK-M. Subsequently, the set of natural compounds of the ZINC database was screened against HsPEPCK-M models using structure-based pharmacophore modeling and molecular docking approaches. The selected compounds were evaluated according to its chemical diversity and clustered based on chemical similarity. RESULTS: The pharmacophore hypotheses, generated based on known PEPCK inhibitors, were able to select 7,124 candidate compounds. These compounds were submitted to molecular docking studies using three conformations of HsPEPCK-M generated by comparative modeling. The aim was to select compounds with high predicted binding affinity for at least one of the conformations of HsPEPCK-M. After molecular docking, 612 molecules were selected as potential inhibitors of HsPEPCK-M. These compounds were clustered according to their structural similarity. Chemical profiling and binding mode analyses of these compounds allowed the proposal of four promising compounds: ZINC01656421, ZINC895296, ZINC00895535 and ZINC02571340. CONCLUSION: These compounds may be considered as potential candidates for HsPEPCK-M inhibitors and may also be used as lead compounds for the development of novel HsPEPCK-M inhibitors.

Discovery of potent p38α MAPK inhibitors through a funnel like workflow combining in silico screening and in vitro validation.

This work describes the rational discovery of novel chemotypes of p38α MAPK inhibitors using a funnel approach consisting of several computer-aided drug discovery methods and biological experiments. Among the identified hits, four compounds belonging to different chemical families showed IC50 values lower than 10 µM. In particular, the 1,4-benzodioxane derivative 5 turned out to be a potent and efficient p38α MAPK inhibitor having IC50 = 0.07 µM, and LEexp and LipE values of 0.38 and 4.8, respectively; noteworthy, the compound had also a promising kinase selectivity profile and the capability to suppress p38α MAPK effects in human immune cells. Overall, the collected findings highlight that the applied strategy has been successful in generating chemical novelty in the inhibitor kinase field, providing suitable chemical candidates for further inhibitor optimization.