Structure-based Pharmacophore Model Research Articles

On the search for COVID-19 therapeutics: identification of potential SARS-CoV-2 main protease inhibitors by virtual screening, pharmacophore modeling and molecular dynamics

COVID-19 also known as SARS-CoV-2 outbreak in late 2019 and its worldwide pandemic spread has taken the world by surprise. The minute-to-minute increasing coronavirus cases (>85 M) and progressive deaths (≈1.8 M) calls for finding a cure to this devastating pandemic. While there have been many attempts to find biologically active molecules targeting SARS-CoV-2 for treatment of this viral infection, none has found a way to the clinic yet. In this study, a 3-feature structure-based pharmacophore model was designed for SARS-CoV-2 main protease (MPro) that plays a vital role in the viral cellular penetration. High throughput virtual screening of the lead-like ZINC library was then performed to find a potent inhibitor employing the predesigned pharmacophore. In-silico pharmacokinetics/toxicity prediction study was subsequently applied towards the best hits. Finally, a 50 ns molecular dynamics simulation was carried out for the best hit and compared to the co-crystallized ligand where the hit compound displayed high binding and comparable interactions. The results identified new hits for SARS-CoV-2 MPro inhibition showing good docking score, pharmacokinetics and toxicity profile, drug-likeness, high binding energy in addition to a promising synthetic accessibility. Identifying new small compounds as potential leads for inhibiting SARS-CoV-2 is a very important step towards designing a synthesizing of promising drug candidates. Communicated by Ramaswamy H. Sarma

CHK1 kinase inhibition: identification of allosteric hits using MD simulations, pharmacophore modeling, docking and MM-PBSA calculations.

The CHK1 kinase plays a pivotal role in the DNA damage response pathway. Hence, inhibition of CHK1 appeared as a promising strategy to overcome the resistance problem of chemotherapeutic agents resulting from the overexpression of CHK1 that enables cancerous cells to repair their chemotherapy-induced DNA damage. In this study, different computational drug design techniques were employed to identify new CHK1 inhibitors targeting its allosteric pocket. A 1μs MD simulation of the apo form of the enzyme was run to study its native dynamics. The resulting trajectory was analyzed to select a frame where the ATP binding pocket is most occluded while its allosteric counterpart is most exposed to be used in the design of potential allosteric inhibitors that could trap the enzyme in such nearly inactive state. Besides the selected frame, another three crystal structures of CHK1 complexed with allosteric inhibitors were utilized to generate structure-based pharmacophore models. Seven pharmacophores were generated and utilized in virtual screening of different databases. The retrieved hits were filtered and then docked into the allosteric pocket. Finally, the binding energies of the top-ranked docked hits were calculated. Twenty compounds were selected as candidates for biological evaluation against CHK1 enzyme. The biological screening results showed moderate activities where the percentage of CHK1 inhibition ranged from zero to 28.26%. Four of the tested compounds showed percentage of CHK1 inhibition greater than 20%, of which, two compounds were identified as allosteric hits that upon further optimization could be converted into lead-like compounds.

Discovery of novel heterocyclic amide-based inhibitors: an integrative in-silico approach to targeting soluble epoxide hydrolase

Inhibition of soluble epoxide hydrolase (sEH) is considered as an emerging druggable target to reduce blood pressure, improve insulin sensitivity, and decrease inflammation. Despite the availability of different classes of sEH small molecule inhibitors for the potential treatment of hypertension, only a few candidates have reached clinical trials, making the optimal control of blood pressure presently unattainable. This necessity motivated us to explore a series of novel quinazoline-4(3H)-one and 4,6-disubstituted pyridin-2(1H)-one derivatives targeting sEH enzyme. Herein, comprehensive computational investigations were performed to probe the inhibition efficacy of these potent compounds in terms of inhibitor-enzyme interactions against sEH. In this study, the 39 in-house with a focused library comprising 39 in-house synthesized compounds were selected. The structure-based pharmacophore modeling was developed based on the crystal structure of sEH with its co-crystallized biologically active inhibitor. The generated hypotheses were applied for virtual screening-based PHASE fitness scores. Docking-based virtual screening workflows were used to generate lead compounds using HTVS, SP and XP based GLIDE G-score values. The candidate leads were filtered using ADMET pharmacological and physicochemical properties screening. A 100-ns of molecular dynamics simulations with Molecular dynamics simulations (100 ns) were performed to explore the binding affinities of the considered compounds. Our study identified four best candidates from quinazoline-4(3H)-one derivatives, which indicated that a quinazolinone ring serves as a suitable scaffold to develop novel small molecule sEH inhibitors.

Structure based pharmacophore modeling, virtual screening, molecular docking and ADMET approaches for identification of natural anti-cancer agents targeting XIAP protein

X-linked inhibitor of apoptosis protein (XIAP) is a member of inhibitor of apoptosis protein (IAP) family responsible for neutralizing the caspases-3, caspases-7, and caspases-9. Overexpression of the protein decreased the apoptosis process in the cell and resulting development of cancer. Different types of XIAP antagonists are generally used to repair the defective apoptosis process that can eliminate carcinoma from living bodies. The chemically synthesis compounds discovered till now as XIAP inhibitors exhibiting side effects, which is making difficulties during the treatment of chemotherapy. So, the study has design to identifying new natural compounds that are able to induce apoptosis by freeing up caspases and will be low toxic. To identify natural compound, a structure-based pharmacophore model to the protein active site cavity was generated following by virtual screening, molecular docking and molecular dynamics (MD) simulation. Initially, seven hit compounds were retrieved and based on molecular docking approach four compounds has chosen for further evaluation. To confirm stability of the selected drug candidate to the target protein the MD simulation approach were employed, which confirmed stability of the three compounds. Based on the finding, three newly obtained compounds namely Caucasicoside A (ZINC77257307), Polygalaxanthone III (ZINC247950187), and MCULE-9896837409 (ZINC107434573) may serve as lead compounds to fight against the treatment of XIAP related cancer, although further evaluation through wet lab is necessary to measure the efficacy of the compounds.

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.

A combined structure-based pharmacophore modeling and 3D-QSAR study on a series of N-heterocyclic scaffolds to screen novel antagonists as human DHFR inhibitors

Dihydrofolate reductase (DHFR) is an essential enzyme that participates in folate metabolism and purine and thymidylate synthesis in cell proliferation. It converts dihydrofolate (DHF) to tetrahydrofolate (THF) in the presence of nicotinamide adenine dinucleotide phosphate (NADPH). This enzyme is found within all organisms adjusting the cellular level of THF. Negligible DHFR activity leads to a deficiency of THF and cell death. This trait is used to inhibit the cancer cells. DHFR has been extensively studied as the therapeutic target for cancer treatment. Accordingly, there is an urgent need for the identification and development of novel effective inhibitors with higher selectivity, lower toxicity, and better potency than currently available drugs. Hence, we aimed to identify new compounds by utilizing the alignment-independent three-dimensional quantitative structure-activity relationship (3D-QSAR) and structure-based pharmacophore modeling. Using the results obtained from these approaches, several antagonists have been retrieved from the virtual screening by applying some filters such as Lipinski’s rule of five. Selected compounds were then docked into the binding site of the receptor for identification of ligand-receptor interactions, binding affinity prediction, and refinement based on GoldScore fitness values. Eventually, pharmacokinetic/drug-likeness features and toxicity profiles of novel compounds were predicted and evaluated. Four hits with PubChem CIDs of 136138676, 94182663, 60219817, and 133300845 were finally proposed as new candidates with potential inhibitory activity against human DHFR.

Discovery of Potent Natural-Product-Derived SIRT2 Inhibitors Using Structure- Based Exploration of SIRT2 Pharmacophoric Space Coupled With QSAR Analyses.

SIRT2 belongs to a class III of Histone Deacetylase (HDAC) and has crucial roles in neurodegeneration and malignancy. The objective of this study is to discover structurally novel natural-product-derived SIRT2 inhibitors. Structure-based pharmacophore modeling integrated with validated QSAR analysis was implemented to discover structurally novel SIRT2 inhibitors from the natural products database. The targeted QSAR model combined molecular descriptors with structure-based pharmacophore capable of explaining bioactivity variation of structurally diverse SIRT2 inhibitors. Manually built pharmacophore model, validated with receiver operating characteristic curve, and selected using the statistically optimum QSAR equation, was applied as a 3Dsearch query to mine AnalytiCon Discovery database of natural products. Experimental in vitro testing of highest-ranked hits identified asperphenamate and salvianolic acid B as active SIRT2 inhibitors with IC50 values in low micromolar range. New chemical scaffolds of SIRT2 inhibitors have been identified that could serve as a starting point for lead-structure optimization.

ABBV-744 as a potential inhibitor of SARS-CoV-2 main protease enzyme against COVID-19

A new pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide and become pandemic with thousands new deaths and infected cases globally. To address coronavirus disease (COVID-19), currently no effective drug or vaccine is available. This necessity motivated us to explore potential lead compounds by considering drug repurposing approach targeting main protease (Mpro) enzyme of SARS-CoV-2. This enzyme considered to be an attractive drug target as it contributes significantly in mediating viral replication and transcription. Herein, comprehensive computational investigations were performed to identify potential inhibitors of SARS-CoV-2 Mpro enzyme. The structure-based pharmacophore modeling was developed based on the co-crystallized structure of the enzyme with its biological active inhibitor. The generated hypotheses were applied for virtual screening based PhaseScore. Docking based virtual screening workflow was used to generate hit compounds using HTVS, SP and XP based Glide GScore. The pharmacological and physicochemical properties of the selected lead compounds were characterized using ADMET. Molecular dynamics simulations were performed to explore the binding affinities of the considered lead compounds. Binding energies revealed that compound ABBV-744 binds to the Mpro with strong affinity (ΔGbind −45.43 kcal/mol), and the complex is more stable in comparison with other protein–ligand complexes. Our study classified three best compounds which could be considered as promising inhibitors against main protease SARS-CoV-2 virus.

Structure- and ligand- based studies to gain insight into the pharmacological implications of histamine H3 receptor

In the context of unknowing the experimental structure for histamine H3 receptor, which is a potential therapeutical target for numerous disorders and diseases, the theoretical methods can bring light and necessary information for a better understanding of the pharmacological implications of the aforementioned receptor. Thus, a homology model of human histamine H3 receptor was built, and SiteMap was used for the identification of potential binding sites. The top-ranked site (the putative binding site) is found in a niche surrounded by TM3, TM5, TM6, and TM7 transmembranes, and it contains the following important amino acids, Asp114 (3.32) and Trp371 (6.48), which are believed to be the key residues, responsible for ligands binding and receptor activation. The docking programs (Glide and Induced Fit) were involved and by the superposition of the best poses obtained, a structure-based protein-ligand pharmacophore model with one hydrogen bond donor, one hydrogen bond acceptor, and one positive ionic feature was developed. The PHASE software, a ligand-based method, was useful in order to validate the 3D pharmacophore hypothesis, by getting a significant Quantitative Structure Activity Relationship (QSAR) model (with R2 = 0.918 for training set and Q2 = 0.828 for test set) for the compounds taken in the study. Further, the pharmacophore model can be used in virtual screening experiments to identify new potent ligands for histamine H3 receptor.

Computational prediction of hERG blockers using homology modelling, molecular docking and QuaSAR studies

A full-length three-dimensional structure of the tetrameric potassium ion channel (hERG Kv11.1) including the N- and C-terminal domains was built, with a diameter of 6 Å and 12 Å between the K + selectivity filter and the pore cavity residue Tyr652 of opposite subunits. Further docking studies with a set of 233 structurally known blockers have shown that compounds bind near the inner vestibule of the pore channel, as well as the helix-IV region of the voltage sensor domain (VSD) in the alpha subunit. The residues of hERG, Gly626, Phe627, Gly628, Tyr652 and Phe656 of the pore channel and Arg488 of VSD plays an important role in ligand binding and hERG blockage. The conducted QuaSAR model is statistically significant, with R2 of 0.72 in predicting the hERG blocking activity. Furthermore, QuaSAR descriptors employing computer-assisted multiple regression procedure reveal that increase in hydrophobicity with higher number of aromatic rings are favorable for the binding affinity of hERG blockers. Additionally, the pIC50 values of 25 commercial compounds screened using structure-based pharmacophore model also show binding to the selectivity filter and pore cavity of hERG potassium channel like the known hERG blockers with a wide range of inhibition from weak to strong blockage predicting to have proarrhythmic potential.

In silico study of medicinal plants with cyclodextrin inclusion complex as the potential inhibitors against SARS-CoV-2 main protease (Mpro) and spike (S) receptor.

The current outbreak of novel coronavirus disease (COVID-19) causes an alarming number of deaths in 221 countries around the world. Nowadays, there is no specific and effective drug regimen for curing COVID-19. Since the COVID-19 pandemic, several medicinal plants with promising results in the previous SARS-CoV could be used to treat SARS-CoV-2 infected patients. This work assesses proven medicinal plants as potential inhibitors against SARS-CoV-2 main protease (Mpro) and spike (S) receptors by employing in silico methods. Molecular docking studies and 3D structure-based pharmacophore modeling were performed to identify the molecular interactions of potential active molecules with the Mpro and (S) receptor of SARS-CoV-2. The drug-likeness and ADME properties were also predicted to support the drug-like nature of the selected active molecules. The results indicated that the most favorable ligand was Terrestriamide with (ΔG: ─8.70 kcal/mol; Ki: 0.417 μM) and (ΔG: ─7.02 kcal/mol; Ki: 7.21 μM) for Mpro and (S) receptor, respectively. Terrestriamide is also supported with a high drug-likeness value and appropriate ADME profile. Furthermore, to improve drug delivery, the cyclodextrin inclusion complex was calculated based on semi-empirical quantum mechanical methods. Terrestriamide/γ−cyclodextrin is the most favorable pathway of inclusion complex formation and could be used to treat COVID-19.

Design and structural optimization of novel 2H-benzo[h]chromene derivatives that target AcrB and reverse bacterial multidrug resistance.

Drug efflux pumps have emerged as a new drug targets for the treatment of bacterial infections in view of its critical role in promoting multidrug resistance. Herein, novel chromanone and 2H-benzo[h]chromene derivatives were designed by means of integrated molecular design and structure-based pharmacophore modeling in an attempt to identify improved efflux pump inhibitors that target Escherichia coli AcrB. The compounds were tested for their efflux inhibitory activity, ability to inhibit efflux, and the effect on bacterial outer and inner membranes. Twenty-three novel structures were identified that synergized with antibacterials tested, inhibited Nile Red efflux, and acted specifically on the AcrB. Among them, WK2, WL7 and WL10 exhibiting broad-spectrum and high-efficiency efflux inhibitory activity were identified as potential ideal AcrB inhibitors. Molecular modeling further revealed that the strong π-π stacking interactions and hydrogen bond networks were the major contributors to tight binding of AcrB.

Structure-based identification of potential SARS-CoV-2 main protease inhibitors

To address coronavirus disease (COVID-19), currently, no effective drug or vaccine is available. In this regard, molecular modeling approaches are highly useful to discover potential inhibitors of the main protease (Mpro) enzyme of SARS-CoV-2. Since, the Mpro enzyme plays key roles in mediating viral replication and transcription; therefore, it is considered as an attractive drug target to control SARS-CoV-2 infection. By using structure-based drug design, pharmacophore modeling, and virtual high throughput drug screening combined with docking and all-atom molecular dynamics simulation approach, we have identified five potential inhibitors of SARS-CoV-2 Mpro. MD simulation studies revealed that compound 54035018 binds to the Mpro with high affinity (ΔG bind −37.40 kcal/mol), and the complex is more stable in comparison with other protein-ligand complexes. We have identified promising leads to fight COVID-19 infection as these compounds fulfill all drug-likeness properties. However, experimental and clinical validations are required for COVID-19 therapy. Communicated by Ramaswamy H. Sarma

An Integrated in silico Approach and in vitro Study for the Discovery of Small-Molecule USP7 Inhibitors as Potential Cancer Therapies.

The ubiquitin-specific protease 7 (USP7) is a highly promising well-validated target for a variety of malignancies. USP7 is critical in regulating the tumor suppressor p53 along with numerous epigenetic modifiers and transcription factors. Previous studies showed that USP7 inhibitors led to increased levels of p53 and anti-proliferative effects in hematological and solid tumor cell lines. Thus, this study aimed to identify potent and safe USP7 hit inhibitors as potential anti-cancer therapeutics via an integrated computational approach that combines pharmacophore modeling, molecular docking, molecular dynamics (MD) simulations and post-MD free energy calculations. In this study, the crystal structure of USP7 has been extensively investigated using a combination of three different chemical pharmacophore modeling approaches. We then screened ∼220.000 drug-like small molecule library and the hit ligands predicted to be nontoxic were evaluated further. The identified hits from each pharmacophore modeling study were further examined by 1-ns short MD simulations and MM/GBSA free energy analysis. In total, we ran 1 ns MD simulations for 1137 selected on small compounds. Based on their average MM/GBSA scores, 18 ligands were selected for 50 ns MD simulations along with one highly potent USP7 inhibitor used as a positive control. The in vitro enzymatic inhibition assay testing of our lead 18 molecules confirmed that 7 of these molecules were successful in USP7 inhibition. Screening results showed that within the used screening approaches, the most successful one was structure-based pharmacophore modeling with the success rate of 75 %. The identification of potent and safe USP7 small molecules as potential inhibitors is a step closer to finding appropriate effective therapies for cancer. Our lead ligands can be used as a scaffold for further structural optimization and development, enabling further research in this promising field.

Identification of potential COVID-19 main protease inhibitors using structure-based pharmacophore approach, molecular docking and repurposing studies.

The current outbreak of novel coronavirus (COVID-19) infections urges the need to identify potential therapeutic agents. Therefore, the repurposing of FDA-approved drugs against today's diseases involves the use of de-risked compounds with potentially lower costs and shorter development timelines. In this study, the recently resolved X-ray crystallographic structure of COVID-19 main protease (Mpro) was used to generate a pharmacophore model and to conduct a docking study to capture antiviral drugs as new promising COVID-19 main protease inhibitors. The developed pharmacophore successfully captured five FDA-approved antiviral drugs (lopinavir, remdesivir, ritonavir, saquinavir and raltegravir). The five drugs were successfully docked into the binding site of COVID-19 Mpro and showed several specific binding interactions that were comparable to those tying the co-crystallized inhibitor X77 inside the binding site of COVID-19 Mpro. Three of the captured drugs namely, remdesivir, lopinavir and ritonavir, were reported to have promising results in COVID-19 treatment and therefore increases the confidence in our results. Our findings suggest an additional possible mechanism of action for remdesivir as an antiviral drug inhibiting COVID-19 Mpro. Additionally, a combination of structure-based pharmacophore modeling with a docking study is expected to facilitate the discovery of novel COVID-19 Mpro inhibitors.

Multi-conformational frame from molecular dynamics as a structure-based pharmacophore model for mapping, screening and identifying ligands against PPAR-γ: a new protocol to develop promising candidates

Despite intensive research on clinical and molecular factors, the development of antidiabetic drugs in the last few decades is decelerating and as a result, the number of drugs approved by the US FDA is reduced. Hence, there is a persistent need for the innovative development of novel anti-diabetic drugs. Recent studies have provided ample proof that the peroxisome proliferator-activated receptor gamma (PPARγ), a ligand-activated transcription factor and its co-activator PGC-1 alpha may serve as good candidates for the treatment of several metabolic disorders. Therefore, in this study, 50 ns molecular dynamics (MD) simulations of the ligand-receptor complex were carried out and the most populated cluster of rosiglitazone bound to crucial amino acids during dynamics studies were selected to generate multi-conformation frame and further dynamic pharmacophore models. Finally, three pharmacophore models were generated, and 10 hits were retrieved as final lead candidates by virtual screening of ZINC database and molecular docking. The study reveals that the amino acids Met364, Lys367, His449, Leu453, Leu469, and Tyr473 play a crucial role in the binding of the compounds at the active site of PPARγ and the selected compounds from the ZINC database showed promising binding as compared to rosiglitazone. Further, ADMET studies were carried out to define the pharmacokinetic properties of promising PPARγ ligand candidates. Communicated by Ramaswamy H. Sarma

Discovery of potentially biased agonists of mu-opioid receptor (MOR) through molecular docking, pharmacophore modeling, and MD simulation

Opioids are well known for their potent analgesic efficacy and severe side effects. Studies have shown that analgesic effects are mediated by the downstream G-protein-dependent pathway of the μ-opioid receptor (MOR), and another β-arrestin-dependent pathway mediates side effects such as respiratory depression, constipation and tolerance etc. TRV130 is a biased ligand for G-protein-dependent pathway, which has high analgesia and has fewer side effects than morphine. In this study, the structure similarity search was performed on the IBSSC database using Oliceridine (TRV130) and PZM21 as templates. The 3D structure-based pharmacophore model was built and combined molecular docking prediction mode was selected to filter out small molecules, Finally, based on affinity prediction, four candidate molecules were obtained. Molecular dynamics simulations explored the detailed interaction mechanism of proteins with small molecules under dynamics. These results suggest that these candidate molecules are potential MOR agonists.

Exploration and identification of novel inhibitors against Knowpain-4 of P. knowlesi using a combinatorial 3D pharmacophore modeling approach

Papain-like cysteine protease of P. knowlesi is a significant target for the treatment of malaria infection. P. knowlesi poses a new threat to malaria eradication programs as it starts infecting humans. We have targeted P. knowlesi Knowpain-4 protein for inhibitor designing based on the molecular modeling simulations and structure-based pharmacophore modeling. In-depth sequence analysis reveals its crucial role in host hemoglobin degradation. Dihedral angles and QMEAN score analysis successfully validate the modeled structure of Knowpain4. The docking simulation leads to the prediction of E64 poses at the binding site of Knowpain4 protein. The ligand–receptor interaction information is used for generating five different pharmacophore models taking a diverse set of parameters. A combined 3D features-based, a pharmacophore model is assembled to screen in-house database. The presence of E64, along with 2,128 compounds in the output cross-confirms our screening model. Later on, top 22 screened compounds with higher relative pharmacophore score to E64 is selected for interaction study. The docking simulation experiments result in the identification of 6 compounds that occupies the functional, active site of Knowpain4 protein with high binding affinity. Further, pharmacokinetic studies showed that the identified compounds are potential bioactive molecules with unique scaffolds, and may have the potential to block the expression of Knowpain4 protease.

Exploring the SARS-CoV-2 Proteome in the Search of Potential Inhibitors via Structure-Based Pharmacophore Modeling/Docking Approach

To date, SARS-CoV-2 infectious disease, named COVID-19 by the World Health Organization (WHO) in February 2020, has caused millions of infections and hundreds of thousands of deaths. Despite the scientific community efforts, there are currently no approved therapies for treating this coronavirus infection. The process of new drug development is expensive and time-consuming, so that drug repurposing may be the ideal solution to fight the pandemic. In this paper, we selected the proteins encoded by SARS-CoV-2 and using homology modeling we identified the high-quality model of proteins. A structure-based pharmacophore modeling study was performed to identify the pharmacophore features for each target. The pharmacophore models were then used to perform a virtual screening against the DrugBank library (investigational, approved and experimental drugs). Potential inhibitors were identified for each target using XP docking and induced fit docking. MM-GBSA was also performed to better prioritize potential inhibitors. This study will provide new important comprehension of the crucial binding hot spots usable for further studies on COVID-19. Our results can be used to guide supervised virtual screening of large commercially available libraries.

Structure based pharmacophore modelling approach for the design of azaindole derivatives as DprE1 inhibitors for tuberculosis

Cell wall of mycobacterium acts as a primary interface which helps in the regulation of important functions and also aids to pathogenicity and virulence of the organism, making it a crucial target for drug discovery. Decaprenylphosphoryl-d-ribose 2′-epimerase (DprE), is important for the growth and survival of Mycobacterium tuberculosis. DprE1 is a donor of arabinose sugars which helps in the formation of cell wall components-lipoarabinomannan and arabinogalactan through Decaprenyl-phosphoryl d-arabinose (DPA) pathway. In our study, we have chosen Azaindole derivatives as DprE1 inhibitors which possess non-covalent property. TBA7371 (azaindole derivative, non-covalent inhibitor) is currently in first phase of clinical trials as DprE1 inhibitor. Azaindoles have been found to be equally potent against drug-sensitive and isoniazid/rifampin-resistant strains. Hence, azaindoles are an attractive class for further optimization as potential DprE1 inhibitors for TB. Structure-based pharmacophore model was generated to investigate the compounds with similar molecular features. Compounds having a good fitness score and pharmacophoric features were compared with the molecules in clinical trial and were proceeded for molecular docking studies to identify the binding affinity of the compounds with target protein DprE1. Energy based calculations using Prime MM-GBSA of Schrodinger was further executed to examine free binding energy of the ligands. The prediction of pharmacokinetic parameters (ADME) plays an important role to identify safe and potent molecules which may further have potential to become drug candidates. Induced-fit docking approach and Molecular Dynamics integrated with Prime MM-GBSA calculations of both hit compounds has further confirmed the binding affinity and stability. All the results obtained from our study were interpreted and compared with DprE1 inhibitor in clinical trials. Study identified ZINC000170252277 as a potential hit compound for further biological evaluation as DprE1 inhibitor.