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PMC:7755033 / 14592-30389 JSONTXT 2 Projects

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Id Subject Object Predicate Lexical cue
T123 0-2 Sentence denotes 2.
T124 4-25 Sentence denotes Materials and methods
T125 27-31 Sentence denotes 2.1.
T126 33-69 Sentence denotes Selection and preparation of ligands
T127 70-263 Sentence denotes The present study was carried out at Molecular Chemoinformatics Section, Cell and Tissue Culture Lab, Dept. of Biochemistry, Era’s Lucknow Medical College and Hospital, Era University, Lucknow.
T128 264-445 Sentence denotes The ligands (WS phytoconstituents) selected for the study were first evaluated for their ability to obey Lipinski’s rule of five (Lipinski et al., 1997) using Molinspiration server.
T129 446-592 Sentence denotes Lipinski's rules are used to determine the drug like characteristics of a compound with properties that would make it a potential drug for humans.
T130 593-695 Sentence denotes PubChem database was used to access the 3D structures of the WS phytoconstituents and reference drugs.
T131 696-872 Sentence denotes Prior to docking, energy minimization of ligands was carried out using Merck Molecular Force Field (MMFF94) in order to achieve a better relaxation in the arrangement of atoms.
T132 873-1382 Sentence denotes The PubChem IDs of the reference drugs and selected ligands were as follows: arbidol (CID-131411), losartan (CID-3961), procainamide (CID-4913), cinacalcet (CID-156419), oberadilol (CID-3047798), poziotinib (CID-25127713), hydroxychloroquine (CID-3652), anaferine (CID-443143), withanolide A (CID-11294368), withanolide B (CID-14236711), withanolide D (CID-161671), withanolide E (CID-301751), withaferin A (CID-265237), withasomnine (CID-442877), withanone (CID-21679027) and viscosalactone B (CID-57403080).
T133 1383-1584 Sentence denotes Prior to docking, the protonation states of the ligands were determined at pH 7.4 using Protoss, a fully automated hydrogen prediction online tool for protein–ligand complexes (https://proteins.plus/).
T134 1586-1590 Sentence denotes 2.2.
T135 1592-1653 Sentence denotes Prediction of activity spectra for substances (PASS) analysis
T136 1654-1744 Sentence denotes PASS is an online web tool hosted at http://195.178.207.233/PASS/index.html (Ahmad, 2019).
T137 1745-1894 Sentence denotes Based on the structure–activity relationship with a known chemical entity, PASS analysis server predicts biological activities of chemical compounds.
T138 1895-2053 Sentence denotes The tool predicts the pharmacological behavior, mechanism of action and side effects such as mutagenicity, carcinogenicity, embryotoxicity and teratogenicity.
T139 2054-2208 Sentence denotes In the present study, PASS analysis was performed using OSIRIS Property Explorer version 4.5.1. (http://www.openmolecules.org/propertyexplorer/index.html)
T140 2210-2216 Sentence denotes 2.2.1.
T141 2218-2241 Sentence denotes Lipinski’s rule of five
T142 2242-2394 Sentence denotes The druglikeness of WS phytoconstituents was also assessed using Lipinski’s rule of five (Ertl et al., 2000; Lipinski et al., 1997; Veber et al., 2002).
T143 2395-2638 Sentence denotes The parameters of druglikeness such as MW ≤500, logP ≤5, number of hydrogen bond donors (NOHNH) ≤5 and hydrogen bond acceptor sites (NON)≤10, topological polar surface area (TPSA) (≤140 Å2), and number of rotatable bonds (≤10) were determined.
T144 2639-2845 Sentence denotes In the present study, the druglikeness of selected WS phytoconstituents was analyzed using Molinspiration (http://www.molinspiration.com/cgi-bin/properties) and compared to that of standard reference drugs.
T145 2847-2853 Sentence denotes 2.2.2.
T146 2855-2865 Sentence denotes Veber rule
T147 2866-2937 Sentence denotes For oral bioavailability, membrane permeability is an important factor.
T148 2938-3074 Sentence denotes Polar surface area and number of rotatable bonds are two critical considerations for a compound to behave as a potential drug candidate.
T149 3075-3243 Sentence denotes With a reduction in polar surface area, permeation increases and with the increase in number of rotatable bonds permeation decreases significantly (Veber et al., 2002).
T150 3244-3364 Sentence denotes The following two criteria should be met by a potential drug candidate in order to obey Veber rules:≤10 rotatable bonds;
T151 3365-3436 Sentence denotes Polar surface area ≤140Å2 (or 12 or fewer H-bond donors and acceptors).
T152 3438-3444 Sentence denotes 2.2.3.
T153 3446-3458 Sentence denotes Ghose filter
T154 3459-3604 Sentence denotes Receptor binding, cellular uptake and bioavailability of drug molecules is strongly influenced by molecular lipophilicity and molar refractivity.
T155 3605-3855 Sentence denotes Both of them signify hydrophobic and dispersive (van der Waals) interactions (Ghose & Crippen, 1987) of a drug molecule and are employed in 3D-QSAR studies to evaluate the drug-like character of molecules under study (Viswanadhan et al., 1990, 1991).
T156 3856-4014 Sentence denotes The following are the qualifying parameters for a putative drug candidate as per Ghose filter:clogP range between -0.4 and 5.6, with an average value of 2.52;
T157 4015-4074 Sentence denotes MW range between 160 and 480, with an average value of 357;
T158 4075-4148 Sentence denotes Molar refractivity range between 40 and 130, with an average value of 97;
T159 4149-4218 Sentence denotes Total number of atoms between 20 and 70, with an average value of 48.
T160 4219-4391 Sentence denotes The above parameters should be kept in mind for testing hypothetically proposed compounds before any in vitro and in vivo experimentation (Ghose, 1987; Ghose et al., 1999).
T161 4393-4399 Sentence denotes 2.2.4.
T162 4401-4413 Sentence denotes Leadlikeness
T163 4414-4572 Sentence denotes According to Teague et al. (1999) compounds with MW in the range 250–350, a XLOGP3 value of <3.5 and <7 rotatable bonds satisfy the criteria for leadlikeness.
T164 4574-4580 Sentence denotes 2.2.5.
T165 4582-4591 Sentence denotes Egan rule
T166 4592-4717 Sentence denotes It is defined as compounds having TPSA > 131.6 Å or log p > 5.88 have drug-like character and properties (Egan et al., 2000).
T167 4719-4725 Sentence denotes 2.2.6.
T168 4727-4738 Sentence denotes Muegge rule
T169 4739-5063 Sentence denotes It states that compounds having MW between 200 and 600, XLogP between −2 and 5, TPSA < 150, no. of rings < 7, no. of carbon atoms >4, no. of heteroatoms > 1, no. of rotatable bonds < 15, no. of H-bond acceptors < 10, no. of H-bond donors < 5 are found to obey Muegge rule and behave as potential drugs (Muegge et al., 2001).
T170 5065-5069 Sentence denotes 2.3.
T171 5071-5113 Sentence denotes Pharmacokinetic (PK) parameters prediction
T172 5114-5211 Sentence denotes Drug discovery process requires early prediction of ADMET properties of candidate drug molecules.
T173 5212-5367 Sentence denotes The fate of a therapeutic drug in an organism can be predicted conveniently by employing a user-friendly interface of SwissADME (http://www.swissadme.ch.).
T174 5368-5533 Sentence denotes The server predicts important properties like lipophilicity (LIPO), flexibility (FLEX), TPSA, size, unsaturation (INSATU), insolubility (INSOLU) and bioavailability.
T175 5534-5809 Sentence denotes Another online program admetSAR v1.0 (http://lmmd.ecust.edu.cn/admetsar2/) calculates and predicts physicochemical properties like lipophilicity (LIPO) of a query compound (XLOGP3) by using a known logP value of a reference compound as a starting point (Teague et al., 1999).
T176 5810-5967 Sentence denotes The percentage of sp-hybridized carbons in the overall carbon count (Fraction Csp3) in the saturation percentage should be at least 0.25 (Tian et al., 2015).
T177 5968-6386 Sentence denotes For solubility, log S (calculated with the ESOL model) should not exceed 6 (Delaney, 2004). admetSAR is also used to predict physiological and biochemical properties of a prospective drug candidate like human intestinal absorption (HIA), blood–brain barrier (BBB) permeability, Caco-2 penetration, P-glycoprotein inhibitor, Ames test-based mutagenesis, subcellular localization, biodegradation and acute oral toxicity.
T178 6388-6392 Sentence denotes 2.4.
T179 6394-6438 Sentence denotes Selection and preparation of protein targets
T180 6439-6621 Sentence denotes The available X-ray crystal structures of human ACE2 receptor, SARS-CoV and SARS-CoV-2 protein targets were downloaded from Protein Data Bank in PDB format (http://www.rcsb.org/pdb).
T181 6622-6723 Sentence denotes Before docking analyses, the protein structures were subjected to refinement and energy minimization.
T182 6724-6884 Sentence denotes The refinement involved the addition of missing atoms, polar hydrogen atoms and Kollman charges to the residues and removal of crystallographic water-molecules.
T183 6885-6991 Sentence denotes These structures were visualized in Accelrys Biovia Discovery Studio 2017 R2 (Biovia, San Diego, CA, USA).
T184 6992-7043 Sentence denotes The PDB IDs of the target proteins were as follows:
T185 7044-7082 Sentence denotes Angiotensin converting enzyme (PDB ID:
T186 7083-7126 Sentence denotes 1O8A), SARS-CoV spike glycoprotein (PDB ID:
T187 7127-7172 Sentence denotes 5WRG), SARS-CoV-2 spike glycoprotein (PDB ID:
T188 7173-7213 Sentence denotes 6VXX), SARS-CoV-2 main protease (PDB ID:
T189 7214-7323 Sentence denotes 6LU7), SARS-CoV main protease (3CL-pro) structure (PDB ID: IP9U), papain like protease of SARS-CoV-2 (PDB ID:
T190 7324-7377 Sentence denotes 6W9C), Nsp-10/Nsp-16 complex from SARS-CoV-2 (PDB ID:
T191 7378-7434 Sentence denotes 6W75), SARS-CoV-2 spike receptor-binding domain (PDB ID:
T192 7435-7516 Sentence denotes 6M0J) and SARS-CoV-2 spike receptor-binding domain (RBD) bound with ACE2 (PDB ID:
T193 7517-7523 Sentence denotes 6M0J).
T194 7524-7711 Sentence denotes The identification of protein ligand-binding sites was carried out using online server Metapocket 2.0 (http://metapocket.eml.org) which combines prediction of sites from four methods viz.
T195 7712-7782 Sentence denotes LIGSITE csc, PASS, Q-SiteFinder and SURFNET to improve the prediction.
T196 7783-7995 Sentence denotes The active site residues of enzymes 3CL-pro, PL-pro of SARS-CoV, SARS-CoV-2 and human ACE2 were found from review of literature (Báez-Santos et al., 2015; Chen et al., 2020; Guy et al., 2005; Zhang et al., 2020).
T197 7997-8001 Sentence denotes 2.5.
T198 8003-8028 Sentence denotes Molecular docking studies
T199 8030-8036 Sentence denotes 2.5.1.
T200 8038-8046 Sentence denotes AutoDock
T201 8047-8308 Sentence denotes Molecular docking of selected phytoconstituents of WS against human ACE2 receptor, SARS-CoV and SARS-CoV-2 target proteins was performed using AutoDock 4.0/ADT version 4.2.6 program (Morris et al., 1998) and further validated using two additional softwares viz.
T202 8309-8431 Sentence denotes AutoDock vina and iGEMDOCK version 2.1 in order to investigate binding kinetics and binding modes to the refined proteins.
T203 8432-8532 Sentence denotes Grid spacing was set at 0.375 Å and the grid points in the X, Y and Z axes were set to 60 × 60 × 60.
T204 8533-8711 Sentence denotes The quest was based on the Lamarckian genetic algorithm (Miyamoto & Kollman, 1992; Oprea et al., 2001) and the binding energies of the results were subjected to further analysis.
T205 8712-8947 Sentence denotes Molecular docking computation and visualization of binding interactions of withanolide analogs to human ACE2 receptor and selected SARS-CoV and SARS-CoV-2 protein targets was done using Accelrys Biovia Discovery Studio version 2017 R2.
T206 8948-9125 Sentence denotes The best possible orientation of the ligand(s) in the protein binding pocket was selected for analysis on the basis of lowest binding energy (BE) and dissociation constant (Kd).
T207 9127-9133 Sentence denotes 2.5.2.
T208 9135-9148 Sentence denotes AutoDock Vina
T209 9149-9304 Sentence denotes AutoDock Vina is a free platform designed to be significantly faster than AutoDock 4, yet at the same time more accurate in predictions of binding pockets.
T210 9305-9490 Sentence denotes It calculates grid maps and clusters automatically, in contrast to AutoDock 4 and as a result of multithreading on multicore machines, faster results are obtained (Trott & Olson, 2010).
T211 9492-9508 Sentence denotes 2.5.3. iGEMDOCK
T212 9509-9734 Sentence denotes Institute of Bioinformatics in Taiwan's National Chiao Tung University developed iGEMDOCK version 2.1, a graphical, user-friendly and automated software for integrated docking, screening and post-analysis (Yang & Chen, 2004).
T213 9735-9979 Sentence denotes Binding sites for a particular ligand were established with the help of the software. iGEMDOCK employs a generic evolutionary method (GA) in order to calculate ligand conformation and orientation with respect to the target protein binding site.
T214 9980-10131 Sentence denotes The parameters selected for GA were as follows: population size = 200, generations = 70, solution number = 2 and docking feature as 'standard docking'.
T215 10132-10293 Sentence denotes Once a set of poses is generated, the software recalculates the energy of each pose and the interaction data represents the individual as well as overall energy.
T216 10294-10495 Sentence denotes Best fit is selected, representing the total energy viz. vdW (van der Waals energy), H-bond (hydrogen bonding energy) and Elect (electrostatic energy) of the predicted pose at the protein binding site.
T217 10497-10501 Sentence denotes 2.6.
T218 10503-10537 Sentence denotes Bioactivity score (BAS) prediction
T219 10538-10615 Sentence denotes Bioactivity score values also predict the overall druglikeness of a compound.
T220 10616-10764 Sentence denotes Molinspiration version 2016.10 was used to predict the drug score of WS phytoconstituents with respect to several human receptors (Proudfoot, 2002).
T221 10765-10902 Sentence denotes As a general rule, the higher the bioactivity score, the greater is the probability of the compound under investigation for being active.
T222 10904-10908 Sentence denotes 2.7.
T223 10910-10934 Sentence denotes Toxicity risk prediction
T224 10935-11175 Sentence denotes Toxicity prediction was done using OSIRIS Property Explorer version 4.5.1. (Information Management Drug Discovery, Actelion Ltd, Allschwil, Switzerland) in order to identify possible side effects of WS phytoconstituents (Khan et al., 2018).
T225 11177-11181 Sentence denotes 2.8.
T226 11183-11206 Sentence denotes Swiss Target Prediction
T227 11207-11359 Sentence denotes Computational approaches are key players in narrowing down the dataset of potential drug targets and suggesting alternative targets for known molecules.
T228 11360-11538 Sentence denotes Molecular insight of the bioactive molecules and their mode of actions are important for understanding the observed phenotypes, prediction and optimization of existing compounds.
T229 11539-11776 Sentence denotes Swiss Target Prediction (http://www.swisstargetprediction.ch.) is an online web-based interface which helps in finding bioactive molecules having similar configuration with related or similar biochemical targets (Campillos et al., 2008).
T230 11777-11886 Sentence denotes The primary goal of the tool is to identify biochemical targets of molecules which are known to be bioactive.
T231 11887-12300 Sentence denotes In the present study, Swiss Target Prediction online server was used for predicting the percentage proportion activity of each selected WS phytoconstituent with known intracellular targets like kinases, nuclear receptors, transcription factors, phosphodiesterases, oxidoreductases, cytochrome P450, voltage gated-ion channels, hydrolases, phosphatases, G-protein coupled receptors and primary active transporters.
T232 12302-12306 Sentence denotes 2.9.
T233 12308-12372 Sentence denotes Prediction of cytochrome P450 mediated sites of metabolism (SOM)
T234 12373-12521 Sentence denotes Most of the FDA-approved drugs are known to be metabolized by a ubiquitous protein family of heme-thiolate enzymes known as cytochrome P450s (CYPs).
T235 12522-12770 Sentence denotes Regioselectivity-WebPredictor (http://reccr.chem.rpi.edu/Software/RS-WebPredicto/) is an algorithm for accurate prediction of isozyme-specific cytochrome P450 (CYP)-mediated sites of metabolism (SOM) on drug like molecules (Nebert & Russell, 2002).
T236 12771-13034 Sentence denotes This is the very first repository that makes metabolic predictions for nine isozymes (1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1 and 3A4) available to the public and uses models trained on the largest set of CYP substrate and metabolite data (Zaretzki et al., 2013).
T237 13036-13041 Sentence denotes 2.10.
T238 13043-13077 Sentence denotes Principal component analysis (PCA)
T239 13078-13303 Sentence denotes The process of drug discovery has come to involve a critical concept known as ‘Chemical space’ which is defined as a multidimensional space projection of the number of property descriptors calculated for each chemical entity.
T240 13304-13443 Sentence denotes PCA is a method to visualize chemical space in lower dimensions in order to identify and underline dominant patterns of drug like entities.
T241 13444-13571 Sentence denotes The term PCA was first coined by Karl Pearson in 1901 and is an application of linear algebra (Ahmad, 2019, Khan et al., 2018).
T242 13572-13915 Sentence denotes Osiris Property Explorer 4.5.1 was used for defining and visualizing multivariate datasets of prospective drug candidates from WS and standard reference drugs through comparison of properties like TPSA, percent absorption, MW, hydrogen bond donor, hydrogen bond acceptor, number of rotatable bonds, Lipinski’s violations, leadlikeness and BAS.
T243 13916-14003 Sentence denotes PCA helps in reducing the dimensionality of the dataset and increases interpretability.
T244 14004-14095 Sentence denotes It does so by creating new uncorrelated variables which maximize the variance successively.
T245 14096-14297 Sentence denotes Another added advantage of PCA is a 3D visualization in chemical space of how ‘drug-like’ are the molecules under study to known standard drugs in terms of their proximity to them in 3D chemical space.
T246 14299-14304 Sentence denotes 2.11.
T247 14306-14340 Sentence denotes Molecular dynamics (MD) simulation
T248 14342-14349 Sentence denotes 2.11.1.
T249 14351-14375 Sentence denotes Playmolecule open server
T250 14376-14606 Sentence denotes Two of the WS phytoconstituents viz. withanolides A and B showing significant binding to selected viral target proteins were subjected to molecular dynamics simulation studies with SARS CoV-2 spike receptor binding domain (PDB ID:
T251 14607-14657 Sentence denotes 6M0J) and SARS CoV-2 papain like protease (PDB ID:
T252 14658-14678 Sentence denotes 6W9C), respectively.
T253 14679-14990 Sentence denotes The playmolecule web platform (https://www.playmolecule.com/SimpleRun/) is publicly available at www.playmolecule.org and uses high-throughput molecular dynamics (HTMD), a python-based framework in order to perform simple molecular-simulation-based drug discovery (Raimondas et al., 2019; Rossell et al., 2017).
T254 14991-15148 Sentence denotes The MD simulation was run for 3 ns for both SARS-Cov-2 glycoprotein–withanolide A complex and SARS CoV-2 papain like protease-withanolide B complex at 300 K.
T255 15150-15157 Sentence denotes 2.11.2.
T256 15159-15219 Sentence denotes Ligand and receptor molecular dynamics (LARMD) online server
T257 15220-15286 Sentence denotes The MD simulation analyses of SARS-CoV spike glycoprotein (PDB ID:
T258 15287-15349 Sentence denotes 5WRG) with withanolide B and SARS-CoV-2 main protease (PDB ID:
T259 15350-15538 Sentence denotes 6LU7) with withanolide A were performed using Ligand and Receptor Molecular Dynamics (LARMD, http://chemyang.ccnu.edu.cn/ccb/server/LARMD/;http://agroda.gzu.edu.cn:9999/ccb/server/LARMD/).
T260 15539-15635 Sentence denotes It is an online bioinformatics tool to investigate and visualize ligand-driven protein dynamics.
T261 15636-15797 Sentence denotes LARMD comprises of three computational modules out of which Int_mod, which aids in the investigation of protein fluctuation, was implemented (Yang et al., 2019).