Ligand-based Pharmacophore Model Research Articles

In silico guided design of non-covalent inhibitors of DprE1: synthesis and biological evaluation

ABSTRACT DprE1 is a potential target of resistant tuberculosis (TB), especially multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB. 2-benzoxazolinone is a closely related bioisostere of some scaffolds such as benzoxazoles, benzimidazole, benzothiazolinone, and benzothiazoles that have been previously explored against DprE1. Thus, a ligand-based quantitative pharmacophore model (AHRR.8) of DprE1 was developed and this pharmacophore model was utilized in activity profiling of some 2-benzoxazolinones from an in-house database using virtual screening. Obtained hits were subject to molecular docking, molecular dynamics (MD), and MM/GBSA calculations, which resulted in benzoyl-substituted derivatives of 2-benzoxazolinone showing strong interactions with the key amino acid residues in the active site of DprE1. Based on in silico results, the top five hits were duly synthesized and evaluated against the XDR-TB strain. This study is an initial effort to explore 2-benzoxazolinones against XDR-TB, which can be submitted further to lead optimization for refining the results.

Phylogenic analysis of coronavirus genome and molecular studies on potential anti-COVID-19 agents from selected FDA-approved drugs

The emergence of 2019 novel Coronavirus (COVID-19 or 2019-nCoV) has caused significant global morbidity and mortality with no consensus specific treatment. We tested the hypothesis that FDA-approved antiretrovirals, antibiotics, and antimalarials will effectively inhibit COVID-19 two major drug targets, coronavirus nucleocapsid protein (NP) and hemagglutinin-esterase (HE). To test this hypothesis, we carried out a phylogenic analysis of coronavirus genome to understand the origins of NP and HE, and also modeled the proteins before molecular docking, druglikeness, toxicity assessment, molecular dynamics simulation (MDS) and ligand-based pharmacophore modeling of the selected FDA-approved drugs. Our models for NP and HE had over 95% identity with templates 5EPW and 3CL5 respectively in the PDB database, with majority of the amino acids occupying acceptable regions. The active sites of the proteins contained conserved residues that were involved in ligand binding. Lopinavir and ritonavir possessed greater binding affinities for NP and HE relative to remdesivir, while levofloxacin and hydroxychloroquine were the most notable among the other classes of drugs. The Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), Radius of gyration (Rg), and binding energy values obtained after 100 ns of MDS revealed good stability of these compounds in the binding sites of the proteins while important pharmacophore features were also identified. The study showed that COVID-19 likely originated from bat, owing to the over 90% genomic similarity observed, and that lopinavir, levofloxacin, and hydroxychloroquine might serve as potential anti-COVID-19 lead molecules for additional optimization and drug development for the treatment of COVID-19. Communicated by Ramaswamy H. Sarma

Identification of ACK1 inhibitors as anticancer agents by using computer-aided drug designing

ACK1, an intracellular non-receptor tyrosine kinase when abnormally activated and amplified causes numerous sorts of human cancers, therefore, act as a major target of cancer drug development. Currently, there are very small numbers of inhibitors reported for ACK1 inhibition and none of them are in clinical trials. In this study, to identify potential ACK1 inhibitors, a small dataset was prepared from already known inhibitors as a training set for ligand-based pharmacophore model generation using the Hip-Hop algorithm available in the Discovery Studio. Selected pharmacophore hypothesis, Hypo1 displayed the highest rank and consists of five features including two hydrogen bond acceptors (HBA), two-ring aromatic (RA) and one hydrophobic (HYP). Hypo1 was further validated by Receiver Operating Characteristic (ROC) curve and Güner-Henry (GH) approach which give an ROC value of 0.92 and highest GH score of 0.78. Drug-like database was generated from ZINC, ANX, NCI and Princeton database using Lipinski's rule of five and ADMET descriptors. Validated Hypo1 was used for searching new potential hit compounds from the generated drug-like database. From molecular docking analysis, ten potential inhibitor compounds were selected on the basis of Goldscore >67.72, the score of reference inhibitor Dasatinib. Furthermore, molecular dynamics simulation study was performed to study the stability of docking conformations. Subsequently, molecular dynamics simulation analyses revealed that the Hit1 and Hit2 compounds have desirable molecular interaction with key residues Thr205 and Ala208 in the hinge region of ACK1 with better binding affinity. Finally, various pharmacokinetic properties over the Hit1 and Hit2 compounds were analyzed using pkCSM tool and it was found that our hit compounds have shown comparable results with reference. Therefore, we propose the Hit1 and Hit2 compounds may be crucial against ACK1 as potential anticancer agents subjected to experimental validation.

Pharmacophore Modelling of Vanillin Derivatives, Favipiravir, Chloroquine, Hydroxychloroquine, Monolaurin and Tetrodotoxin as MPro inhibitors of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2)

Ligand-based pharmacophore modelling approach using four established antiviral drugs, namely remdesivir, lopinavir, ritonavir and hydroxychloroquine were analysed for COVID-19 inhibitors as training sets. Twenty vanillin derivatives together with monolaurin were used as test sets to evaluate potential as SARS-CoV-2 inhibitors. Structure-based pharmacophore modelling approach was also performed using Protein Data Bank information: PDB-5RE6, 5REX and 5RFZ in order to analyse the binding site and ligand-protein complex interactions. The pharmacophore modelling mode of 5RE6 displayed two Hydrogen Bond Acceptors (HBA) and one Hydrophobic (HY) interaction. Besides, the pharmacophore model of 5REX showed two HBA and two HY interactions. Finally, the pharmacophore model of 5RFZ showed three HBA and one HY interaction. Based on ligand-based approach, 20 Schiff-based vanillin derivatives, namely vanillin associated with methyl-6- aminopyridine-3-carboxylate (1), sepiapterin (2), 6-aminopyridine-3-carboxylic acid (3), 6-aminopyridine-2-carboxylic acid (4), pemoline (5), α-phenylglycine (6), 2-amino-4-hydroxy-3-methylpentanoic acid (7), 4-hydroxyphenylglycine (8), β-homoserine (9), allylglycine (10), oxamic acid (11) benzophenone hydrazine (12), 2-aminoadipic acid (13), D-alanyl-D-alanine (14), p-bromophenylalanine (15), nicotinic hydrazide (16), 4-hydroxybenzhydrazide (17), benzohydrazide (18), isonicotinic hydrazide (19), and phenylhydrazine (20) showed strong MPro inhibition activity. This was due to their good alignment and common features to PDB-5RE6. Similarly, monolaurin and tetrodotoxin displayed some significant activity against SARS-CoV-2. From structure-based approach, vanillin derivatives (1) to (12) displayed some potent MPro inhibition against SARS-CoV-2. Favipiravir, chloroquine and hydroxychloroquine also showed some significant MPro inhibition. Favipiravir showed good alignment and common pharmacophore features to PDB- 5RFZ, whereas chloroquine and hydroxychloroquine showed good alignment and common pharmacophore features to PDB-5REX.

IDENTIFIKASI SENYAWA BAHAN ALAM SEBAGAI INHBITOR TIROSIN KINASE EGFR: SKRINING IN SILICO BERBASIS FARMAKOFOR DAN MOLECULAR DOCKING

Epidermal growth factor receptor (EGFR) is one of tyrosine kinase that overexpressed in many types of cancer . The ihbiition of EGFR activity has known as a rational target for cancer therapy . However, several EGFR inhibitors have limitations related to resistance problems or reduced tumor response. Therefore, the effort to develop new inhibitors with greater efficacy is very important to overcome the problem s. In the present study, we designed a computational method by combining ligand based pharmacophore modeling and molecular docking for screening the potential natural compounds as EGFR inhibitors. Validation result of ligand based pharmacophore modeling and docking molecular by ROC curves showed that both of these protocols had AUC ≥ 0.7. The Redocking erlotinib to EGFR in our docking protocols gave the RMSD < 2 A. Therefore, our computational method could be applied for virtual screening. Initial screening based on pharmacophore modeling obtained 86 out of 179.816 compounds that could be mapped with the pharmacophore features. Further screening through molecular docking showed two compounds had a lower docking score than erlotinib, i.e ZINC00941342 and ZINC72325782. However, by careful analysis of their interaction profiles, only ZINC00941342 which formed hydrogen bonds with Met769, which is known as an EGFR key residue. Additionally, it was also found to form hydrogen with Cys773. Therefore, we recommend ZINC00941342 as a potential EGFR inhibitor.

Ligand-based pharmacophore modeling and molecular dynamic simulation approaches to identify putative MMP-9 inhibitors.

MMP-9 is a calcium-dependent zinc endopeptidase that plays a crucial role in various diseases and is a ubiquitous target for many classes of drugs. The availability of MMP-9 crystal structure in combination with aryl sulfonamide anthranilate hydroxamate inhibitor facilitates to accentuate the computer-aided screening of MMP-9 inhibitors with the presumed binding mode. In the current study, ligand-based pharmacophore modeling and 3D-QSAR analysis were performed using 67 reported MMP-9 inhibitors possessing pIC50 in the range of 5.221 to 9.000. The established five-point hypothesis model DDHRR_1 was statistically validated using various parameters R2 (0.9076), Q2 (0.8170), and F value (83.5) at a partial least square of four. Hypothesis validation and enrichment analysis were performed for the generated hypothesis. Further, Y-scrambling and Xternal validation using mean-absolute error-based criteria were performed to evaluate the reliability of the model. Docking in the XP mode and binding free energy was calculated for 67 selected ligands to explore the key binding interactions and binding affinity against the MMP-9 enzyme. Additionally, high-throughput virtual screening was carried out for 2.3 million chemical molecules to explore the potential virtual hits, and their predicted activity was calculated. Thus, the results obtained aid in developing novel MMP-9 inhibitors with significant activity and binding affinity.

Ligand-based pharmacophore modeling, virtual screening and biological evaluation to identify novel TGR5 agonists.

Takeda G-protein-coupled receptor 5 (TGR5) is emerging as an important and promising target for the development of anti-diabetic drugs. To understand the structural characteristics of TGR5 agonists, the common feature pharmacophore models were generated and molecular docking was performed. The ligand-based virtual screening combined with pharmacophore mapping and molecular docking was performed to identify novel nonsteroidal TGR5 agonists. Finally, 20 compounds were screened for in vitro TGR5 agonistic activity assay, and results showed most compounds exhibiting TGR5 agonistic activity at 40 μM. Among these compounds, V12 and V14 displayed obvious TGR5 agonist activity, with the EC50 values of 19.5 μM and 7.7 μM, respectively. Compounds V12 and V14 could be considered potential TGR5 agonist candidates and also may be used as initial hits for developing novel TGR5 agonists.

Identification of potential TNF-\u03b1 inhibitors: from in silico to in vitro studies

Tumor Necrosis Factor Alpha (TNF-α) is a pleiotropic pro-inflammatory cytokine. It act as central biological regulator in critical immune functions, but its dysregulation has been linked with a number of diseases. Inhibition of TNF-α has considerable therapeutic potential for diseases such as cancer, diabetes, and especially autoimmune diseases. Despite the fact that many small molecule inhibitors have been identified against TNF-α, no orally active drug has been reported yet which demand an urgent need of a small molecule drug against TNF-α. This study focuses on the development of ligand-based selective pharmacophore model to perform virtual screening of plant origin natural product database for the identification of potential inhibitors against TNF-α. The resultant hits, identified as actives were evaluated by molecular docking studies to get insight into their potential binding interaction with the target protein. Based on pharmacophore matching, interacting residues, docking score, more affinity towards TNF-α with diverse scaffolds five compounds were selected for in vitro activity study. Experimental validation led to the identification of three chemically diverse potential compounds with the IC50 32.5 ± 4.5 µM, 6.5 ± 0.8 µM and 27.4 ± 1.7 µM, respectively.

Ligand Based Pharmacophore Modeling, Virtual Screening, and Molecular Docking Studies of Asymmetrical Hexahydro-2H-Indazole Analogs of Curcumin (AIACs) to Discover Novel Estrogen Receptors Alpha (ERα) Inhibitor

The estrogen receptor alpha (ERα) plays an important role in breast development and pro-proliferation signal activation in the normal and cancerous breast. The ERα inhibitors were potentially active as cytotoxic agents against breast cancer. This study was conducted in order to find Asymmetrical Hexahydro-2H-Indazole Analogs of Curcumin (AIACs) as hits of ERα inhibitor. A training set of 17 selected ERα inhibitors was used to create 10 pharmacophore models using LigandScout 4.2. The pharmacophore models were validated using 383 active compounds as positive data and 20674 decoys as negative data obtained from DUD.E. Model 2 was found as the best pharmacophore model and consisted of three types of pharmacophore features, viz. one hydrophobic, one hydrogen bond acceptor, and aromatic interactions. Model 2 was utilized for ligand-based virtual screening 186 of AIACs, AMACs, intermediates, and Mannich base derivative compounds. The hits obtained were further screened using molecular docking, analyzed using drug scan, and tested for its synthesis accessibility. Fourteen compounds were fulfilled as hits in pharmacophore modeling, in which 10 hits were selected by molecular docking, but only seven hits met Lipinski’s rule of five and had medium synthesis accessibility. In conclusion, seven compounds were suggested to be potentially active as ERα inhibitors and deserve to be synthesized and further investigated.

Pharmacophore-based identification and in vitro validation of apoptosis inducers as anticancer agents

ABSTRACT Ligand-based pharmacophore modelling and virtual screening along with in vitro screening were performed as a rational strategy for the identification of novel compounds as apoptosis inducers and anticancer agents from the chemical database. Known apoptosis inducers were selected from the literature for generation of pharmacophore models, which were subjected to validation using Receiver operating characteristic (ROC) and Günere-Henry (GH) scoring methods. Based on highest fitness score of 4680.61, ROC value of 0.872 and GH score of 0.758, pharmacophore model-2 was selected as the best model. Model-2 as 3D search query was searched against the IBS database to find novel compounds as hits. Three hits were selected with a QFIT value more than 82 for in vitro screening as apoptosis inducers and anticancer agents. In vitro anticancer activity was performed using resazurin cell variability assay, and apoptosis inducing activity was determined using caspase-3 activation and annexin-FITC assays. One of the retrieved hit, STOCK5S-44056 demonstrated IC50 value of 23.56 µM in cell variability assay, and had EC50 value of 26.95 µM in caspase-3 activation assay. STOCK5S-44056 also indicated late stage induction of apoptosis in annexin assay. The results of in vitro activity revealed that STOCK5S-44056 has a potential to become anticancer agents.

Ligand-Based Pharmacophore Modeling, Molecular Docking, and Molecular Dynamic Studies of Dual Tyrosine Kinase Inhibitor of EGFR and VEGFR2.

Epidermal growth factor receptor (EGFR) and vascular endothelial growth factor receptor 2 (VEGFR2) play an important role in cancer growth. Both of them have close relationships. Expression of EGFR will induce an angiogenic factor (VEGF) release for binding with VEGFR2. However, the existence of VEGF up-regulation independent of EGFR leads to cancer cell resistance to anti-EGFR. Therefore, a therapeutic approach targeting EGFR and VEGFR2 simultaneously may improve the outcome of cancer treatment. The present study was designed to identify potential compounds as a dual inhibitor of EGFR and VEGFR2 by the computational method. Firstly, the ligand-based pharmacophore model for each target was setup to screen of ZINC database of purchasable compounds. The hit compounds obtained by pharmacophore screening were then further screened by molecular docking studies. Taking erlotinib (EGFR inhibitor) and axitinib (VEGFR2 inhibitor) as reference drugs, six potential compounds (ZINC08398597, ZINC12047553, ZINC16525481, ZINC17418102, ZINC21942954, and ZINC38484632) were selected based on their docking scores and binding interaction. However, molecular dynamics simulations demonstrated that only ZINC16525481 and ZINC38484632 which have good binding free energy and stable hydrogen bonding interactions with EGFR and VEGFR2. The result represents a promising starting point for developing potent dual tyrosine kinases inhibitor of EGFR and VEGFR2.

Design, Synthesis, and in vitro Evaluation of P2X7 Antagonists.

The P2X7 receptor is a promising target for the treatment of various diseases due to its significant role in inflammation and immune cell signaling. This work describes the design, synthesis, and in vitro evaluation of a series of novel derivatives bearing diverse scaffolds as potent P2X7 antagonists. Our approach was based on structural modifications of reported (adamantan-1-yl)methylbenzamides able to inhibit the receptor activation. The adamantane moieties and the amide bond were replaced, and the replacements were evaluated by a ligand-based pharmacophore model. The antagonistic potency of the synthesized analogues was assessed by two-electrode voltage clamp experiments, using Xenopus laevis oocytes that express the human P2X7 receptor. SAR studies suggested that the replacement of the adamantane ring by an aryl-cyclohexyl moiety afforded the most potent antagonists against the activation of the P2X7 cation channel, with analogue 2-chloro-N-[1-(3-(nitrooxymethyl)phenyl)cyclohexyl)methyl]benzamide (56) exhibiting the best potency with an IC50 value of 0.39 μM.

Ligand-based pharmacophore modeling of TNF-α to design novel inhibitors using virtual screening and molecular dynamics

Tumor necrosis factor-α (TNF-α) is one of the promising targets for treating inflammatory (Crohn disease, psoriasis, psoriatic arthritis, rheumatoid arthritis) and various other diseases. Commercially available TNF-α inhibitors are associated with several risks and limitations. In the present study, we have identified small TNF-α inhibitors using in silico approaches, namely pharmacophore modeling, virtual screening, molecular docking, molecular dynamics simulation and free binding energy calculations. The study yielded better and potent hits that bind to TNF-α with significant affinity. The best pharmacophore model generated using LigandScout has an efficient hit rate and Area Under the operating Curve. High throughput virtual screening of SPECS database molecules against crystal structure of TNF-α protein, coupled with physicochemical filtration, PAINS test. Virtual hit compounds used for molecular docking enabled the identification of 20 compounds with better binding energies when compared with previously known TNF-α inhibitors. MD simulation analysis on 20 virtual identified hits showed that ligand binding with TNF-α protein is stable and protein-ligand conformation remains unchanged. Further, 16 compounds passed ADMET analysis suggesting these identified hit compounds are suitable for designing a future class of potent TNF-α inhibitors. Communicated by Ramaswamy H. Sarma

Ligand-based discovery of small molecules suppressing cancer cell proliferation via autophagic flux inhibition.

Autophagy is a conserved self-degradation system closely related to cancer progression. Small molecule inhibitors of autophagy have proven to be efficient tools in cancer therapy and are in high demand. Here we report the discovery of two compounds (LZ02/01) capable of suppressing cancer cell proliferation via inhibiting autophagy flux and promoting apoptosis. Potential autophagy inhibitors were selected based on the pharmacophore model derived from the structures of known autophagy inhibitors. LZ02/01-mediated autophagy flux disruption and apoptosis promotion in breast and hepatocellular carcinoma cells (MCF-7 and Hep3B) were examined using a combination of molecular methods in vitro and in vivo. The synergistic tumor-suppressing effects of LZ02 and chloroquine were validated by adopting a xenograft mice model of human breast cancer. Two potential inhibitors (LZ02/01) targeting an autophagy pathway were discovered from the Enamine database. In both MCF-7 and Hep3B cells, LZ02 and LZ01 had the effect of causing the co-occurrence of autophagic flux inhibition and apoptosis induction, robustly suppressing the growth, proliferation, and cell cycle progression. Further tests revealed that FoxO3a and its downstream target genes regulating autophagy, apoptosis, and cell cycle progression were activated and overexpressed, suggesting such effects of LZ02/01 on autophagy and apoptosis were associated with the activation and overexpression of FoxO3a. In addition, LZ02/01-mediated apoptosis is not independent; it was verified to be promoted by autophagic flux inhibition. Meanwhile, synergistic effects on tumor growth reduction were detected in the xenograft mice model of human breast cancer simultaneously treated with LZ02 and chloroquine. Our findings suggest that LZ01 and LZ02 are potent in suppressing cancer cell proliferation and tumor growth through autophagic flux inhibition and apoptosis promotion. The synergistic anti-cancer effects of LZ02 with chloroquine may provide a rational basis for prospective cancer therapy. KEY MESSAGES: A ligand-based pharmacophore model of high quality is constructed to query hits and two novel scaffold lead compounds LZ01/02 were identified by high-throughput virtual screening. LZ01/02 works to inhibit autophagic flux by attenuating lysosome function. LZ01/02 induces apoptosis through autophagic flux inhibition and apoptosis is the main mechanism to inhibit MCF-7 and Hep3B cancer cell proliferation. The synergistic antitumor growth effects of LZ02 and chloroquine are verified in human xenograft model.

In Silico-Guided Identification of New Potent Inhibitors of Carbonic Anhydrases Expressed in Vibrio cholerae.

Carbonic anhydrases from Vibrio cholerae (VchCAs) play a significant role in bacterial pathophysiological processes. Therefore, their inhibition leads to a reduction of gene expression virulence and bacterial growth impairment. Herein, we report the first ligand-based pharmacophore model as a computational tool to study selective inhibitors of the β-class of VchCA. By a virtual screening on a collection of sulfonamides, we retrieved 9 compounds that were synthesized and evaluated for their inhibitory effects against VchCAβ as well as α- and γ-classes of VchCAs and selectivity over human ubiquitous isoforms hCA I and II. Notably, all tested compounds were active inhibitors of VchCAs. The N-(4-sulfamoylbenzyl)-[1,1'-biphenyl]-4-carboxamide (20e) stood out as the most exciting inhibitor toward the β-class (K i = 95.6 nM), also showing a low affinity against the tested human isoforms. By applying docking procedures, we described the binding mode of the inhibitor 20e within the catalytic cavity of the modeled open conformation of VchCAβ.

Ligand-based pharmacophore modelling and virtual screening for the identification of amyloid-beta diagnostic molecules

Currently, only three molecules, flutemetamol, florbetaben and florbetapir, have been approved for clinical use towards the definitive diagnosis of Alzheimer’s disease (AD). Despite the clinically approved drugs’ advantages, there still exists a need for new diagnostic molecules with improved properties (physicochemical and pharmacokinetic) in comparison to the current molecules in clinical use and research. In this work, we report a pharmacophore model and a quantitative structure activity relationship (QSAR) model, constructed from a series of 166 amyloid beta diagnostic compounds (targeting Alzheimer’s disease) with the purpose of identifying functional groups influencing and predicting bioactivity. Subsequently, pharmacophore based virtual screening and QSAR predictions were used to identify new amyloid beta diagnostic molecules. In addition, docking and molecular dynamics simulations were conducted to explore the type and nature of interactions required for ligands to bind effectively in the binding regions of amyloid beta fibrils (PDB 2MXU). In our findings, the highest-ranked 4 feature pharmacophore model possessed one hydrogen bond acceptor, one hydrophobic feature and two ring features (AHRR). Systematically, the same dataset of molecules used for pharmacophore modelling was used to generate an atom-based 3D QSAR hypothesis to illustrate the activity relationship of amyloid-beta diagnostic molecules. The partial least squares (PLS) 3D QSAR model obtained showed good correlation as indicated by respective statistical parameters, R^2, Q^2 and Pearson values of 0.76, 0.72 and 0.86 respectively. Virtual screening against ZINC15 database and the ChemBridge CNS-Set yielded 7 molecules, 4 of which had satisfactory ADME properties. Docking and molecular dynamics simulations showed that hydrogen bonding, hydrophobic and π-π interactions are crucial towards the binding of ligands (as predicted by our pharmacophore and QSAR models) to amyloid beta fibrils. In conclusion, the findings of this work present a wealth of information that can be useful in future research towards identifying and design of new amyloid diagnostic molecules. The pharmacophore presented here can be used to filter independent databases to identify new structurally related molecules with improved activity whereas the QSAR model can be useful in predicting bioactivities of the predicted hits.

Identification of novel Aedes aegypti odorant-binding protein 1 modulators by ligand and structure-based approaches and bioassays

Arboviruses are a group of viruses (e.g. Dengue, Chikungunya and Yellow fever virus) that are transmitted by arthropod vectors, which Aedes aegipty is the vector of main viruses in Americas. This vector is responsible to 2.4 millions of arboviruses cases in Brazil with less than a thousand deaths annually. Despite of epidemiological data, arboviruses treatment is symptomatic and the vaccine control is not effective, which makes the vector control against A. aegipty a promising strategy to diseases control. One way to achieve this goal is to development of A. aegipty sensitive olfactory modulators. Odorant binding protein 1 from A. aegypti (AaegOBP1) is essential in sensory communication, and is the first filter in odorant selection, which makes this target promising to development of new repellents. For this reason, hierarchical virtual screening (ligand-based pharmacophore model and molecular docking) together volatility filter was applied at Sigma-Aldrich database (n = 126.851) to prioritize potential molecules to repellency assays. Three compounds showed adequate stereo-electronic requirements (QFIT> 81.53), score to AaegOBP1 binding site (Score > 36.0) and volatile properties and it was chosen for repellency assays. ZINC00170981 and ZINC00131924 showed a dose-response behavior, while ZINC01621824 did not showed activity in repellency assays. Finally, Molecular Dynamics (MD) was employed to hypothesize the stability of protein-ligand complexes. According to RMSD, RMSF and binding free energy data, ZINC00170981 and ZINC00131924 were able to stabilize AaegOBP1 binding-site during the trajectory by interactions with key residues such as His77, Leu89 and Trp114). Communicated by Ramaswamy H. Sarma

Ligand-based pharmacophore modelling, in silico virtual screening, molecular docking and molecular dynamic simulation study to identify novel Francisella tularensis ParE inhibitors

ParE, a subunit of topoisomerase IV, is involved in the management of DNA topology and validated to be an attractive target for the development of antibacterial agents. Availability of the crystal structure of Francisella tularensis in combination with one of the pyrrolo [2,3-d] pyrimidine-2-thiolinhibitor facilitated us to emphasize the combined computational approach for discovering the presumed binding mode of selected inhibitors into the binding pocket of ParE (pdb. 4HY1). In the current study, pharmacophore modelling and 3D-QSAR studies were performed using 33 reported F. tularensis ParE inhibitors having pKi ranging from 5.06 to 9.00. The developed five featured pharmacophore model, DHHRR_1 was statistically validated with different parameters like Q2 (0.66), R2 (0.99) and F value (682) at four-component partial least squares factor. Enrichment analysis was performed to validate the generated pharmacophore model. Extra-precision molecular docking, binding free energy calculation using PRIME MM-GBSA were performed for the selected inhibitors. Induced fit docking was performed for the highest active inhibitor 16. The highest-ranked induced fit docked complex 16/4HY1 was used to run a 50 ns molecular dynamic simulation to validate the stability. Further, in silico High Throughput Virtual Screening was performed using 22 lakhs chemical database molecules to identify the potential virtual hits and predicted activity was found for the virtual hits. These results provide promising strategies for the development of novel molecules with better inhibitory activity against F. tularensis ParE.

In silico drug repositioning of FDA-approved drugs to predict new inhibitors for alpha-synuclein aggregation

One of the hallmarks of Parkinson’s disease (PD), a long-term neurodegenerative syndrome, is the accumulation of alpha-synuclein (α-syn) fibrils. Despite numerous studies and efforts, inhibition of α-syn protein aggregation is still a challenge. To overcome this issue, we propose an in silico pharmacophore-based repositioning strategy, to find a pharmaceutical drug that, in addition to their defined role, can be used to prevent aggregation of the α-syn protein. Ligand-based pharmacophore modeling was developed and the best model was selected with validation parameters including 72 % sensitivity, 98 % specificity and goodness score about 0.7. The optimal model has three groups of hydrogen bond donor (HBD), three groups of hydrogen bond acceptor (HBA), and two aromatic rings (AR). The FDA-Approved reports in the ZINC15 database were screened with the pharmacophore model taken from inhibitor compounds. The model identified 22 hits, as promising candidate drugs for Parkinson's therapy. It is noteworthy that among these, 10 drugs have been reported to inhibition of α-syn aggregation or treat/reduce Parkinson's pathogenesis. This model was used to virtual screen ZINC, NCI databases, and natural products from the pomegranate. The results of this screen were filtered for their inability to cross the blood-brain barrier, poor oral bioavailability, etc. Finally, the selected compounds of two ZINC and NCI databases were combined and structurally clustered. Remained compounds were clustered in 28 different clusters, and the 17 compounds were introduced as final candidates.

Combiphore (Structure and Ligand Based Pharmacophore) - Approach for the Design of GPR40 Modulators in the Management of Diabetes.

Pharmacophore mapping and molecular docking can be synergistically integrated to improve the drug design and discovery process. A rational strategy, combiphore approach, derived from the combined study of Structure and Ligand based pharmacophore has been described to identify novel GPR40 modulators. DISCOtech module from Discovery studio was used for the generation of the Structure and Ligand based pharmacophore models which gave hydrophobic aromatic, ring aromatic and negative ionizable as essential pharmacophoric features. The generated models were validated by screening active and inactive datasets, GH scoring and ROC curve analysis. The best model was exposed as a 3D query to screen the hits from databases like GLASS (GPCR-Ligand Association), GPCR SARfari and Mini-Maybridge. Various filters were applied to retrieve the hit molecules having good drug-like properties. A known protein structure of hGPR40 (pdb: 4PHU) having TAK-875 as ligand complex was used to perform the molecular docking studies; using SYBYL-X 1.2 software. Clustering both the models gave RMSD of 0.89. Therefore, the present approach explored the maximum features by combining both ligand and structure based pharmacophore models. A common structural motif as identified in combiphore for GPR40 modulation consists of the para-substituted phenyl propionic acid scaffold. Therefore, the combiphore approach, whereby maximum structural information (from both ligand and biological protein) is explored, gives maximum insights into the plausible protein-ligand interactions and provides potential lead candidates as exemplified in this study.