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Yuniarti, Nunung, Sudi Mungkasi, Sri Hartati Yuliani, and Enade Perdana Istyastono. "Development of a Graphical User Interface Application to Identify Marginal and Potent Ligands for Estrogen Receptor Alpha." Indonesian Journal of Chemistry 19, no. 2 (April 9, 2019): 531. http://dx.doi.org/10.22146/ijc.34561.
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Employing ensemble Protein-Ligand Interaction Fingerprints (ensPLIF) as descriptors in post retrospective Structure-Based Virtual Screening (SBVS) campaigns Quantitative Structure-Activity Relationship (QSAR) analysis has been proven to significantly increase the predictive ability in the identification of potent ligands for estrogen receptor alpha (ERα). In the research presented in this article, similar approaches have been performed to construct and retrospectively validate an SBVS protocol to identify marginal ligands for ERα. Based on both validated SBVS protocols, a graphical-user-interface (GUI) application to identify if a compound is a non-, moderate or potent ligand for ERα was developed. The GUI application was subsequently used to virtually screen genistin, genistein, daidzin, and daidzein, followed by in vitro test employing a cytotoxic assay using 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide (MTT) method.
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Geanes, Alexander R., Hykeyung P. Cho, Kellie D. Nance, Kevin M. McGowan, P. Jeffrey Conn, Carrie K. Jones, Jens Meiler, and Craig W. Lindsley. "Ligand-based virtual screen for the discovery of novel M5 inhibitor chemotypes." Bioorganic & Medicinal Chemistry Letters 26, no. 18 (September 2016): 4487–91. http://dx.doi.org/10.1016/j.bmcl.2016.07.071.
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Chimenti, Michael S., Stacie L. Bulfer, R. Jeffrey Neitz, Adam R. Renslo, Matthew P. Jacobson, Thomas L. James, Michelle R. Arkin, and Mark J. S. Kelly. "A Fragment-Based Ligand Screen Against Part of a Large Protein Machine." Journal of Biomolecular Screening 20, no. 6 (February 17, 2015): 788–800. http://dx.doi.org/10.1177/1087057115570550.
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The ubiquitous AAA+ ATPase p97 functions as a dynamic molecular machine driving several cellular processes. It is essential in regulating protein homeostasis, and it represents a potential drug target for cancer, particularly when there is a greater reliance on the endoplasmic reticulum–associated protein degradation pathway and ubiquitin–proteasome pathway to degrade an overabundance of secreted proteins. Here, we report a case study for using fragment-based ligand design approaches against this large and dynamic hexamer, which has multiple potential binding sites for small molecules. A screen of a fragment library was conducted by surface plasmon resonance (SPR) and followed up by nuclear magnetic resonance (NMR), two complementary biophysical techniques. Virtual screening was also carried out to examine possible binding sites for the experimental hits and evaluate the potential utility of fragment docking for this target. Out of this effort, 13 fragments were discovered that showed reversible binding with affinities between 140 µM and 1 mM, binding stoichiometries of 1:1 or 2:1, and good ligand efficiencies. Structural data for fragment–protein interactions were obtained with residue-specific [U-2H] 13CH3-methyl-labeling NMR strategies, and these data were compared to poses from docking. The combination of virtual screening, SPR, and NMR enabled us to find and validate a number of interesting fragment hits and allowed us to gain an understanding of the structural nature of fragment binding.
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Mazzolari, Angelica, Silvia Gervasoni, Alessandro Pedretti, Laura Fumagalli, Rosanna Matucci, and Giulio Vistoli. "Repositioning Dequalinium as Potent Muscarinic Allosteric Ligand by Combining Virtual Screening Campaigns and Experimental Binding Assays." International Journal of Molecular Sciences 21, no. 17 (August 19, 2020): 5961. http://dx.doi.org/10.3390/ijms21175961.
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Structure-based virtual screening is a truly productive repurposing approach provided that reliable target structures are available. Recent progresses in the structural resolution of the G-Protein Coupled Receptors (GPCRs) render these targets amenable for structure-based repurposing studies. Hence, the present study describes structure-based virtual screening campaigns with a view to repurposing known drugs as potential allosteric (and/or orthosteric) ligands for the hM2 muscarinic subtype which was indeed resolved in complex with an allosteric modulator thus allowing a precise identification of this binding cavity. First, a docking protocol was developed and optimized based on binding space concept and enrichment factor optimization algorithm (EFO) consensus approach by using a purposely collected database including known allosteric modulators. The so-developed consensus models were then utilized to virtually screen the DrugBank database. Based on the computational results, six promising molecules were selected and experimentally tested and four of them revealed interesting affinity data; in particular, dequalinium showed a very impressive allosteric modulation for hM2. Based on these results, a second campaign was focused on bis-cationic derivatives and allowed the identification of other two relevant hM2 ligands. Overall, the study enhances the understanding of the factors governing the hM2 allosteric modulation emphasizing the key role of ligand flexibility as well as of arrangement and delocalization of the positively charged moieties.
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Ravi D, Chaitanya Kumar K, Mothilal K, and Mahender K. "High throughput virtual screening of cyclooxygenase-2 by using database." International Journal of Pharmaceutical Research and Life Sciences 8, no. 2 (December 10, 2020): 97–100. http://dx.doi.org/10.26452/ijprls.v8i2.1358.
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COX-2 is a type of Non-steroidal mitigating drug (NSAID) that legitimately targets COX-2, a protein liable for irritation and torment. Selectivity for COX-2 decreases the danger of peptic ulceration and is the fundamental component of celecoxib, rofecoxib and different individuals from this medication class. COX-2 selectivity doesn't appear to influence other antagonistic impacts of NSAIDs (most prominently an expanded danger of renal disappointment), and a few outcomes have excited the doubt that there may be an expansion in danger for cardiovascular failure, apoplexy and stroke by a relative increment in thromboxane. The target of this investigation is to screen drug-like compounds from Zinc database and to predict the potency and activity by using Virtual Screening and Molecular Docking Study. The scope of the study extends to predict the feasibility of the compounds for Drug development. Hence, this examination expresses the significance of little particle libraries and their utilization to upgrade drug revelation measure earlier amalgamation. This way to deal with screen original mixes as COX-2 inhibitors from ZINC information base relies upon different boundaries, for example, Lipinski's standard of 5, pharmacophoric bunches appended on the ligand, size of the dataset and compound libraries among others. Additional, exertion can be stretched out to consider the receptor-ligand associations tentatively, and assessment of their organic action would help in planning mixes dependent on simulated screening strategies.
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Riniker, Sereina, Gregory A. Landrum, Floriane Montanari, Santiago D. Villalba, Julie Maier, Johanna M. Jansen, W. Patrick Walters, and Anang A. Shelat. "Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria." F1000Research 6 (July 17, 2017): 1136. http://dx.doi.org/10.12688/f1000research.11905.1.
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The first challenge in the 2014 competition launched by the Teach-Discover-Treat (TDT) initiative asked for the development of a tutorial for ligand-based virtual screening, based on data from a primary phenotypic high-throughput screen (HTS) against malaria. The resulting Workflows were applied to select compounds from a commercial database, and a subset of those were purchased and tested experimentally for anti-malaria activity. Here, we present the two most successful Workflows, both using machine-learning approaches, and report the results for the 114 compounds tested in the follow-up screen. Excluding the two known anti-malarials quinidine and amodiaquine and 31 compounds already present in the primary HTS, a high hit rate of 57% was found.
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Riniker, Sereina, Gregory A. Landrum, Floriane Montanari, Santiago D. Villalba, Julie Maier, Johanna M. Jansen, W. Patrick Walters, and Anang A. Shelat. "Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria." F1000Research 6 (February 19, 2018): 1136. http://dx.doi.org/10.12688/f1000research.11905.2.
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The first challenge in the 2014 competition launched by the Teach-Discover-Treat (TDT) initiative asked for the development of a tutorial for ligand-based virtual screening, based on data from a primary phenotypic high-throughput screen (HTS) against malaria. The resulting Workflows were applied to select compounds from a commercial database, and a subset of those were purchased and tested experimentally for anti-malaria activity. Here, we present the two most successful Workflows, both using machine-learning approaches, and report the results for the 114 compounds tested in the follow-up screen. Excluding the two known anti-malarials quinidine and amodiaquine and 31 compounds already present in the primary HTS, a high hit rate of 57% was found.
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Bepari, Asim Kumar, and Hasan Mahmud Reza. "Identification of a novel inhibitor of SARS-CoV-2 3CL-PRO through virtual screening and molecular dynamics simulation." PeerJ 9 (April 13, 2021): e11261. http://dx.doi.org/10.7717/peerj.11261.
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Background The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has ravaged lives across the globe since December 2019, and new cases are still on the rise. Peoples’ ongoing sufferings trigger scientists to develop safe and effective remedies to treat this deadly viral disease. While repurposing the existing FDA-approved drugs remains in the front line, exploring drug candidates from synthetic and natural compounds is also a viable alternative. This study employed a comprehensive computational approach to screen inhibitors for SARS-CoV-2 3CL-PRO (also known as the main protease), a prime molecular target to treat coronavirus diseases. Methods We performed 100 ns GROMACS molecular dynamics simulations of three high-resolution X-ray crystallographic structures of 3CL-PRO. We extracted frames at 10 ns intervals to mimic conformational diversities of the target protein in biological environments. We then used AutoDock Vina molecular docking to virtual screen the Sigma–Aldrich MyriaScreen Diversity Library II, a rich collection of 10,000 druglike small molecules with diverse chemotypes. Subsequently, we adopted in silico computation of physicochemical properties, pharmacokinetic parameters, and toxicity profiles. Finally, we analyzed hydrogen bonding and other protein-ligand interactions for the short-listed compounds. Results Over the 100 ns molecular dynamics simulations of 3CL-PRO’s crystal structures, 6LZE, 6M0K, and 6YB7, showed overall integrity with mean Cα root-mean-square deviation (RMSD) of 1.96 (±0.35) Å, 1.98 (±0.21) Å, and 1.94 (±0.25) Å, respectively. Average root-mean-square fluctuation (RMSF) values were 1.21 ± 0.79 (6LZE), 1.12 ± 0.72 (6M0K), and 1.11 ± 0.60 (6YB7). After two phases of AutoDock Vina virtual screening of the MyriaScreen Diversity Library II, we prepared a list of the top 20 ligands. We selected four promising leads considering predicted oral bioavailability, druglikeness, and toxicity profiles. These compounds also demonstrated favorable protein-ligand interactions. We then employed 50-ns molecular dynamics simulations for the four selected molecules and the reference ligand 11a in the crystallographic structure 6LZE. Analysis of RMSF, RMSD, and hydrogen bonding along the simulation trajectories indicated that S51765 would form a more stable protein-ligand complexe with 3CL-PRO compared to other molecules. Insights into short-range Coulombic and Lennard-Jones potentials also revealed favorable binding of S51765 with 3CL-PRO. Conclusion We identified a potential lead for antiviral drug discovery against the SARS-CoV-2 main protease. Our results will aid global efforts to find safe and effective remedies for COVID-19.
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Dvorácskó, Szabolcs, László Lázár, Ferenc Fülöp, Márta Palkó, Zita Zalán, Botond Penke, Lívia Fülöp, Csaba Tömböly, and Ferenc Bogár. "Novel High Affinity Sigma-1 Receptor Ligands from Minimal Ensemble Docking-Based Virtual Screening." International Journal of Molecular Sciences 22, no. 15 (July 29, 2021): 8112. http://dx.doi.org/10.3390/ijms22158112.
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Sigma-1 receptor (S1R) is an intracellular, multi-functional, ligand operated protein that also acts as a chaperone. It is considered as a pluripotent drug target in several pathologies. The publication of agonist and antagonist bound receptor structures has paved the way for receptor-based in silico drug design. However, recent studies on this subject payed no attention to the structural differences of agonist and antagonist binding. In this work, we have developed a new ensemble docking-based virtual screening protocol utilizing both agonist and antagonist bound S1R structures. This protocol was used to screen our in-house compound library. The S1R binding affinities of the 40 highest ranked compounds were measured in competitive radioligand binding assays and the sigma-2 receptor (S2R) affinities of the best S1R binders were also determined. This way three novel high affinity S1R ligands were identified and one of them exhibited a notable S1R/S2R selectivity.
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Scotti, Marcus Tullius, Mateus Feitosa Alves, Chonny Alexander Herrera-Acevedo, and Luciana Scotti. "Virtual Screening Studies for Discovery of Novel Inhibitors of Inflammatory Process Targets." Current Pharmaceutical Design 24, no. 14 (July 13, 2018): 1617–38. http://dx.doi.org/10.2174/1381612824666180403122410.
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Inflammation has been very evident in infectious diseases, but in recent times research has increasingly shown that a range of non-infectious diseases may present with inflammatory conditions. This fact becomes important as new anti-inflammatory drugs emerge with different targets for treatment of diseases. Virtual screening (VS) involves applying computational methods to discover new ligands for biological structures from the formation of large libraries composed of a large number of compounds. This review aims to report several studies employing a variety of VS: ligand-based and structure-based VS are being used more frequently in combination to decrease the probability of choosing false positive candidates. There are also studies that use only one approach. Docking is widely employed as structure-based VS methodology, however pharmacophore models based on the structure are becoming more prevalent. Molecular dynamics simulations, despite their computational cost, are still utilized to validate docking scores and analyze the stability of the complex ligand-structure. It is important to note that several studies employed several drug-like rules to screen structures, as well, decoys and PAINS to validate the models. Natural product databases, despite the lower number of the compounds compared to other databases that are available, are commonly referred to as a source of drug-like molecules. There is a literal explosion of software being released for a variety of purposes and several of them are free tools and/or web tools. Overall, VS studies are nowadays a normal part of medicinal chemistry to determine novel potential inhibitors for targets of inflammatory diseases.
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Xue, Xin, Gang Bao, Hai-Qing Zhang, Ning-Yi Zhao, Yuan Sun, Yue Zhang, and Xiao-Long Wang. "An Application of Fit Quality to Screen MDM2/p53 Protein-Protein Interaction Inhibitors." Molecules 23, no. 12 (December 1, 2018): 3174. http://dx.doi.org/10.3390/molecules23123174.
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: The judicious application of ligand or binding efficiency (LE) metrics, which quantify the molecular properties required to obtain binding affinity for a drug target, is gaining traction in the selection and optimization of fragments, hits and leads. Here we report for the first time the use of LE based metric, fit quality (FQ), in virtual screening (VS) of MDM2/p53 protein-protein interaction inhibitors (PPIIs). Firstly, a Receptor-Ligand pharmacophore model was constructed on multiple MDM2/ligand complex structures to screen the library. The enrichment factor (EF) for screening was calculated based on a decoy set to define the screening threshold. Finally, 1% of the library, 335 compounds, were screened and re-filtered with the FQ metric. According to the statistical results of FQ vs activity of 156 MDM2/p53 PPIIs extracted from literatures, the cut-off was defined as FQ = 0.8. After the second round of VS, six compounds with the FQ > 0.8 were picked out for assessing their antitumor activity. At the cellular level, the six hits exhibited a good selectivity (larger than 3) against HepG2 (wt-p53) vs Hep3B (p53 null) cell lines. On the further study, the six hits exhibited an acceptable affinity (range of Ki from 102 to 103 nM) to MDM2 when comparing to Nutlin-3a. Based on our work, FQ based VS strategy could be applied to discover other PPIIs.
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Zhou, Wei-Neng, Yan-Min Zhang, Xin Qiao, Jing Pan, Ling-Feng Yin, Lu Zhu, Jun-Nan Zhao, et al. "Virtual Screening Strategy Combined Bayesian Classification Model, Molecular Docking for Acetyl-CoA Carboxylases Inhibitors." Current Computer-Aided Drug Design 15, no. 3 (April 10, 2019): 193–205. http://dx.doi.org/10.2174/1573409914666181109110030.
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Introduction: Acetyl-CoA Carboxylases (ACC) have been an important target for the therapy of metabolic syndrome, such as obesity, hepatic steatosis, insulin resistance, dyslipidemia, non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), type 2 diabetes (T2DM), and some other diseases. Methods: In this study, virtual screening strategy combined with Bayesian categorization modeling, molecular docking and binding site analysis with protein ligand interaction fingerprint (PLIF) was adopted to validate some potent ACC inhibitors. First, the best Bayesian model with an excellent value of Area Under Curve (AUC) value (training set AUC: 0.972, test set AUC: 0.955) was used to screen compounds of validation library. Then the compounds screened by best Bayesian model were further screened by molecule docking again. Results: Finally, the hit compounds evaluated with four percentages (1%, 2%, 5%, 10%) were verified to reveal enrichment rates for the compounds. The combination of the ligandbased Bayesian model and structure-based virtual screening resulted in the identification of top four compounds which exhibited excellent IC 50 values against ACC in top 1% of the validation library. Conclusion: In summary, the whole strategy is of high efficiency, and would be helpful for the discovery of ACC inhibitors and some other target inhibitors.</P>
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Zhang, Beina, Mao Shu, Chunmei Xu, Chunhong An, Rui Wang, and Zhihua Lin. "Virtual Screening, Docking, Synthesis and Bioactivity Evaluation of Thiazolidinediones as Potential PPARγ Partial Agonists for Preparation of Antidiabetic Agents." Letters in Drug Design & Discovery 16, no. 6 (May 24, 2019): 608–17. http://dx.doi.org/10.2174/1570180815666180827123512.
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Background:Peroxisome proliferator-activated receptor gamma (PPARγ) is one of the key targets of insulin resistance research, in addition to being ligand-activated transcription factors of the nuclear hormone receptor superfamily with a leading role in adiposeness activation and insulin sensitivity. They regulate cholesterol and carbohydrate metabolism through direct actions on gene expression. Despite their therapeutic importance, there are dose limiting side effects associated with PPARγ drug treatments, thus a new generation of safer PPARγ drugs are being actively sought after treatment.Methods:In this study, we used computer aided drug design to screen new series of PPARγ ligands, and synthesized a series of potential thiazolidinedione derivatives such as 5,7- dibenzyloxybenzyl-3-hydroxymethyl-4H-coumarin-4-ketone, using 4-steps to synthesize the target compounds and built streptozotocin (STZ) induced insulin resistance rat model to measure their antidiabetic activity.Results:We found that 10 mg/kg concentration of compound 0701C could significantly decrease blood glucose and serum PPARγ, serum insulin levels in insulin resistance model rat.Conclusion:We would conclude that compound 0701C might serve as a potential PPARγ partial agonist.
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Barros, Renata P. C., Luciana Scotti, and Marcus T. Scotti. "Exploring Secondary Metabolites Database of Apocynaceae, Menispermaceae, and Annonaceae to Select Potential Anti-HCV Compounds." Current Topics in Medicinal Chemistry 19, no. 11 (July 25, 2019): 900–913. http://dx.doi.org/10.2174/1568026619666190510094228.
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Background: Hepatitis C is a disease that constitutes a serious global health problem, is often asymptomatic and difficult to diagnose and about 60-80% of infected patients develop chronic diseases over time. As there is no vaccine against hepatitis C virus (HCV), developing new cheap treatments is a big challenge. Objective: The search for new drugs from natural products has been outstanding in recent years. The aim of this study was to combine structure-based and ligand-based virtual screening (VS) techniques to select potentially active molecules against four HCV target proteins from in-house secondary metabolite dataset (SistematX). Materials and Methods: From the ChEMBL database, we selected four sets of 1199, 355, 290 and 237chemical structures with inhibitory activity against different targets of HCV to create random forest models with an accuracy value higher than 82% for cross-validation and test sets. Afterward, a ligandbased virtual screen of the entire 1848 secondary metabolites database stored in SistematX was performed. In addition, a structure-based virtual screening was also performed for the same set of secondary metabolites using molecular docking. Results: Finally, using consensus analyses approach combining ligand-based and structure-based VS, three alkaloids were selected as potential anti-HCV compounds. Conclusion: The selected structures are a starting point for further studies in order to develop new anti- HCV compounds based on natural products.
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Wang, Yuwei, Rui Han, Huimin Zhang, Hongli Liu, Jiazhong Li, Huanxiang Liu, and Paola Gramatica. "Combined Ligand/Structure-Based Virtual Screening and Molecular Dynamics Simulations of Steroidal Androgen Receptor Antagonists." BioMed Research International 2017 (2017): 1–18. http://dx.doi.org/10.1155/2017/3572394.
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The antiandrogens, such as bicalutamide, targeting the androgen receptor (AR), are the main endocrine therapies for prostate cancer (PCa). But as drug resistance to antiandrogens emerges in advanced PCa, there presents a high medical need for exploitation of novel AR antagonists. In this work, the relationships between the molecular structures and antiandrogenic activities of a series of 7α-substituted dihydrotestosterone derivatives were investigated. The proposed MLR model obtained high predictive ability. The thoroughly validated QSAR model was used to virtually screen new dihydrotestosterones derivatives taken from PubChem, resulting in the finding of novel compounds CID_70128824, CID_70127147, and CID_70126881, whose in silico bioactivities are much higher than the published best one, even higher than bicalutamide. In addition, molecular docking, molecular dynamics (MD) simulations, and MM/GBSA have been employed to analyze and compare the binding modes between the novel compounds and AR. Through the analysis of the binding free energy and residue energy decomposition, we concluded that the newly discovered chemicals can in silico bind to AR with similar position and mechanism to the reported active compound and the van der Waals interaction is the main driving force during the binding process.
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Hajbabaie, Roxanna, Matthew T. Harper, and Taufiq Rahman. "Establishing an Analogue Based In Silico Pipeline in the Pursuit of Novel Inhibitory Scaffolds against the SARS Coronavirus 2 Papain-Like Protease." Molecules 26, no. 4 (February 20, 2021): 1134. http://dx.doi.org/10.3390/molecules26041134.
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The ongoing coronavirus pandemic has been a burden on the worldwide population, with mass fatalities and devastating socioeconomic consequences. It has particularly drawn attention to the lack of approved small-molecule drugs to inhibit SARS coronaviruses. Importantly, lessons learned from the SARS outbreak of 2002–2004, caused by severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1), can be applied to current drug discovery ventures. SARS-CoV-1 and SARS-CoV-2 both possess two cysteine proteases, the main protease (Mpro) and the papain-like protease (PLpro), which play a significant role in facilitating viral replication, and are important drug targets. The non-covalent inhibitor, GRL-0617, which was found to inhibit replication of SARS-CoV-1, and more recently SARS-CoV-2, is the only PLpro inhibitor co-crystallised with the recently solved SARS-CoV-2 PLpro crystal structure. Therefore, the GRL-0617 structural template and pharmacophore features are instrumental in the design and development of more potent PLpro inhibitors. In this work, we conducted scaffold hopping using GRL-0617 as a reference to screen over 339,000 ligands in the chemical space using the ChemDiv, MayBridge, and Enamine screening libraries. Twenty-four distinct scaffolds with structural and electrostatic similarity to GRL-0617 were obtained. These proceeded to molecular docking against PLpro using the AutoDock tools. Of two compounds that showed the most favourable predicted binding affinities to the target site, as well as comparable protein-ligand interactions to GRL-0617, one was chosen for further analogue-based work. Twenty-seven analogues of this compound were further docked against the PLpro, which resulted in two additional hits with promising docking profiles. Our in silico pipeline consisted of an integrative four-step approach: (1) ligand-based virtual screening (scaffold-hopping), (2) molecular docking, (3) an analogue search, and, (4) evaluation of scaffold drug-likeness, to identify promising scaffolds and eliminate those with undesirable properties. Overall, we present four novel, and lipophilic, scaffolds obtained from an exhaustive search of diverse and uncharted regions of chemical space, which may be further explored in vitro through structure-activity relationship (SAR) studies in the search for more potent inhibitors. Furthermore, these scaffolds were predicted to have fewer off-target interactions than GRL-0617. Lastly, to our knowledge, this work contains the largest ligand-based virtual screen performed against GRL-0617.
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Muttaqin, Fauzan Zein, Dina Kharisma, Aiyi Asnawi, and Fransiska Kurniawan. "Pharmacophore and Molecular Docking-Based Virtual Screening of B-Cell Lymphoma 2 (BCL 2) Inhibitor from Zinc Natural Database as Anti-Small Cell Lung Cancer." Journal of Drug Delivery and Therapeutics 10, no. 2 (March 15, 2020): 143–47. http://dx.doi.org/10.22270/jddt.v10i2.3923.
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Cancer is a disease involving genetic factors in its pathogenesis. The increase of cell survival as a result of genetic changes, which prevent apoptosis such as Bcl2 (B-cell lymphoma-2) activation, will cause the tumor to grow. The overexpression of Bcl2 in small cell lung cancer should be inhibited. This study aims to screen natural products that can inhibit Bcl2 overexpression in lung cancer using pharmacophore- and molecular docking-based virtual screening to ZINC Natural Product database. The validation of pharmacophore-based virtual screening to the three features of the pharmacophore model (2 hydrophobic interactions and 1 hydrogen bond donor) showed that the AUC, EF, Se, Sp, ACC, and GH values were 0.57, 3.8, 0.101, 0.957, 0.936, and 0.149, respectively. On the other hand, the validation of molecular docking-based virtual screening showed that the RMSD values of Vina Wizard and AutoDock Wizard were 1.3Å and 1.9Å, respectively. The pharmacophore model virtual screening first obtained 6,615 compounds, and then the molecular docking-based virtual screening finally gained 255 compounds whose values of ΔG and Ki were lower than those of the native ligand. It was concluded that the virtual screening could yield as many as 255 potential anti-lung cancer drug candidates.
Keywords: B-cell lymphoma 2 inhibitors, molecular docking, pharmacophore modeling, virtual screening
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Ballester, Pedro J., Isaac Westwood, Nicola Laurieri, Edith Sim, and W. Graham Richards. "Prospective virtual screening with Ultrafast Shape Recognition: the identification of novel inhibitors of arylamine N -acetyltransferases." Journal of The Royal Society Interface 7, no. 43 (July 8, 2009): 335–42. http://dx.doi.org/10.1098/rsif.2009.0170.
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There is currently a shortage of chemical molecules that can be used as bioactive probes to study molecular targets and potentially as starting points for drug discovery. One inexpensive way to address this problem is to use computational methods to screen a comprehensive database of small molecules to discover novel structures that could lead to alternative and better bioactive probes. Despite that pleasing logic the results have been somewhat mixed. Here we describe a virtual screening technique based on ligand–receptor shape complementarity, Ultrafast Shape Recognition (USR). USR is specifically applied to identify novel inhibitors of arylamine N -acetyltransferases by computationally screening almost 700 million molecular conformers in a time- and resource-efficient manner. A small number of the predicted active compounds were purchased and tested obtaining a confirmed hit rate of 40 per cent which is an outstanding result for a prospective virtual screening.
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Jiang, Lili, Zhongmin Zhang, Zhen Wang, and Yong Liu. "Discovery of novel potential KIT inhibitors for the treatment of gastrointestinal stromal tumor." Open Life Sciences 16, no. 1 (January 1, 2021): 303–10. http://dx.doi.org/10.1515/biol-2021-0036.
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Abstract Numerous inhibitors of tyrosine-protein kinase KIT, a receptor tyrosine kinase, have been explored as a viable therapy for the treatment of gastrointestinal stromal tumor (GIST). However, drug resistance due to acquired mutations in KIT makes these drugs almost useless. The present study was designed to screen the novel inhibitors against the activity of the KIT mutants through pharmacophore modeling and molecular docking. The best two pharmacophore models were established using the KIT mutants’ crystal complexes and were used to screen the new compounds with possible KIT inhibitory activity against both activation loop and ATP-binding mutants. As a result, two compounds were identified as potential candidates from the virtual screening, which satisfied the potential binding capabilities, molecular modeling characteristics, and predicted absorption, distribution, metabolism, excretion, toxicity (ADMET) properties. Further molecular docking simulations showed that two compounds made strong hydrogen bond interaction with different KIT mutant proteins. Our results indicated that pharmacophore models based on the receptor–ligand complex had excellent ability to screen KIT inhibitors, and two compounds may have the potential to develop further as the future KIT inhibitors for GIST treatment.
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Ambre, Premlata K., Raghuvir R. S. Pissurlenkar, Evans C. Coutinho, and Radhakrishnan P. Iyer. "Identification of new checkpoint kinase-1 (Chk1) inhibitors by docking, 3D-QSAR, and pharmacophore-modeling methods." Canadian Journal of Chemistry 90, no. 8 (August 2012): 675–92. http://dx.doi.org/10.1139/v2012-047.
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Inhibition of checkpoint kinase-1 (Chk1) by small molecules is of great therapeutic interest in the field of oncology and for understanding cell-cycle regulations. This paper presents a model with elements from docking, pharmacophore mapping, the 3D-QSAR approaches CoMFA, CoMSIA and CoRIA, and virtual screening to identify novel hits against Chk1. Docking, 3D-QSAR (CoRIA, CoMFA and CoMSIA), and pharmacophore studies delineate crucial site points on the Chk1 inhibitors, which can be modified to improve activity. The docking analysis showed residues in the proximity of the ligands that are involved in ligand–receptor interactions, whereas CoRIA models were able to derive the magnitude of these interactions that impact the activity. The ligand-based 3D-QSAR methods (CoMFA and CoMSIA) highlight key areas on the molecules that are beneficial and (or) detrimental for activity. The docking studies and 3D-QSAR models are in excellent agreement in terms of binding-site interactions. The pharmacophore hypotheses validated using sensitivity, selectivity, and specificity parameters is a four-point model, characterized by a hydrogen-bond acceptor (A), hydrogen-bond donor (D), and two hydrophobes (H). This map was used to screen a database of 2.7 million druglike compounds, which were pruned to a small set of potential inhibitors by CoRIA, CoMFA, and CoMSIA models with predicted activity in the range of 8.5–10.5 log units.
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Aman, La Ode, Rahmana Emran Kartasasmita, and Daryono Hadi Tjahjono. "Virtual screening of curcumin analogues as DYRK2 inhibitor: Pharmacophore analysis, molecular docking and dynamics, and ADME prediction." F1000Research 10 (May 17, 2021): 394. http://dx.doi.org/10.12688/f1000research.28040.1.
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Background: Curcumin reduces the proliferation of cancer cells through inhibition of the DYRK2 enzyme, which is a positive regulator of the 26S proteasome. Methods: In the present work, curcumin analogues have been screened from the MolPort database using a pharmacophore model that comprised a ligand-based approach. The result of the screening was then evaluated by molecular docking and molecular dynamics based on binding the free energy of the interaction between each compound with the binding pocket of DYRK2. The hit compounds were then confirmed by absorption, distribution, metabolism, and excretion (ADME) prediction. Results: Screening of 7.4 million molecules from the MolPort database afforded six selected hit compounds. By considering the ADME prediction, three prospective curcumin analogues have been selected. These are: 2‐[2‐(1‐methylpyrazol‐4‐yl)ethyl]‐1H,5H,6H,7H,8H‐imidazo[4,5‐c]azepin‐4‐one (Molport-035-369-361), methyl 4‐(3‐hydroxy‐1,2‐oxazol‐5‐yl)piperidine‐1‐carboxylate (Molport-000-004-273) and (1S)‐1‐[5‐(furan‐3‐carbonyl)‐4H,6H,7H‐pyrazolo[1,5‐a]pyrazin‐2‐yl]ethanol (MolPort-035-585-822). Conclusion: Pharmacophore modelling, combined with molecular docking and molecular dynamics simulation, as well as ADME prediction were successfully applied to screen curcumin analogues from the MolPort database as DYRK2 inhibitors. All selected compounds that have better predicted pharmacokinetic properties than that of curcumin are considered for further study.
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Liang, Jing-wei, Shan Wang, Ming-yang Wang, Shi-long Li, Wan-qiu Li, and Fan-hao Meng. "Identification of Novel PI3Kδ Selective Inhibitors by SVM-Based Multistage Virtual Screening and Molecular Dynamics Simulations." International Journal of Molecular Sciences 20, no. 23 (November 28, 2019): 6000. http://dx.doi.org/10.3390/ijms20236000.
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Phosphoinositide 3 kinase delta (PI3Kδ) is a lipid kinase that has been implicated in a variety of immune mediated disorders. The research on isoform selectivity was crucial for reducing side effects. In the current study, an optimized hierarchical multistage virtual screening method was utilized for screening the PI3Kδ selective inhibitors. The method sequentially applied a support vector machine (SVM), a protein ligand interaction fingerprint (PLIF) pharmacophore, and a molecular docking approach. The evaluation of the validation set showed a high hit rate and a high enrichment factor of 75.1% and 301.66, respectively. This multistage virtual screening method was then utilized to screen the NCI database. From the final hit list, Compound 10 has great potential as the PI3Kδ inhibitor with micromolar inhibition in the PI3Kδ kinase activity assay. This compound also shows selectivity against PI3Kδ kinase. The method combining SVM, pharmacophore, and docking was capable of screening out the compounds with potential PI3Kδ selective inhibitors. Moreover, structural modification of Compound 10 will contribute to investigating the novel scaffold and designing novel PI3Kδ inhibitors.
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Coimbra, Judite R. M., Salete J. Baptista, Teresa C. P. Dinis, Maria M. C. Silva, Paula I. Moreira, Armanda E. Santos, and Jorge A. R. Salvador. "Combining Virtual Screening Protocol and In Vitro Evaluation towards the Discovery of BACE1 Inhibitors." Biomolecules 10, no. 4 (April 1, 2020): 535. http://dx.doi.org/10.3390/biom10040535.
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The treatment options for a patient diagnosed with Alzheimer’s disease (AD) are currently limited. The cerebral accumulation of amyloid-β (Aβ) is a critical molecular event in the pathogenesis of AD. When the amyloidogenic β-secretase (BACE1) is inhibited, the production of Aβ peptide is reduced. Henceforth, the main goal of this study is the discovery of new small bioactive molecules that potentially reach the brain and inhibit BACE1. The work was conducted by a customized molecular modelling protocol, including pharmacophore-based and molecular docking-based virtual screening (VS). Structure-based (SB) and ligand-based (LB) pharmacophore models were designed to accurately screen several drug-like compound databases. The retrieved hits were subjected to molecular docking and in silico filtered to predict their ability to cross the blood–brain barrier (BBB). Additionally, 34 high-scoring compounds structurally distinct from known BACE1 inhibitors were selected for in vitro screening assay, which resulted in 13 novel hit-compounds for this relevant therapeutic target. This study disclosed new BACE1 inhibitors, proving the utility of combining computational and in vitro approaches for effectively predicting anti-BACE1 agents in the early drug discovery process.
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Halim, Sobia Ahsan, Almas Gul Sikandari, Ajmal Khan, Abdul Wadood, Muhammad Qaiser Fatmi, René Csuk, and Ahmed Al-Harrasi. "Structure-Based Virtual Screening of Tumor Necrosis Factor-α Inhibitors by Cheminformatics Approaches and Bio-Molecular Simulation." Biomolecules 11, no. 2 (February 22, 2021): 329. http://dx.doi.org/10.3390/biom11020329.
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Tumor necrosis factor-α (TNF-α) is a drug target in rheumatoid arthritis and several other auto-immune disorders. TNF-α binds with TNF receptors (TNFR), located on the surface of several immunological cells to exert its effect. Hence, the use of inhibitors that can hinder the complex formation of TNF-α/TNFR can be of medicinal significance. In this study, multiple chem-informatics approaches, including descriptor-based screening, 2D-similarity searching, and pharmacophore modelling were applied to screen new TNF-α inhibitors. Subsequently, multiple-docking protocols were used, and four-fold post-docking results were analyzed by consensus approach. After structure-based virtual screening, seventeen compounds were mutually ranked in top-ranked position by all the docking programs. Those identified hits target TNF-α dimer and effectively block TNF-α/TNFR interface. The predicted pharmacokinetics and physiological properties of the selected hits revealed that, out of seventeen, seven compounds (4, 5, 10, 11, 13–15) possessed excellent ADMET profile. These seven compounds plus three more molecules (7, 8 and 9) were chosen for molecular dynamics simulation studies to probe into ligand-induced structural and dynamic behavior of TNF-α, followed by ligand-TNF-α binding free energy calculation using MM-PBSA. The MM-PBSA calculations revealed that compounds 4, 5, 7 and 9 possess highest affinity for TNF-α; 8, 11, 13–15 exhibited moderate affinities, while compound 10 showed weaker binding affinity with TNF-α. This study provides valuable insights to design more potent and selective inhibitors of TNF-α, that will help to treat inflammatory disorders.
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Tutone, Marco, Giulia Culletta, Luca Livecchi, and Anna M. Almerico. "A Definitive Pharmacophore Modelling Study on CDK2 ATP Pocket Binders: Tracing the Path of New Virtual High-Throughput Screenings." Current Drug Discovery Technologies 17, no. 5 (December 23, 2020): 740–47. http://dx.doi.org/10.2174/1570163816666190620113944.
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Cyclin Dependent Kinases-2 (CDK2) are members of serine/threonine protein kinases family. They play an important role in the regulation events of the eukaryotic cell division cycle, especially during the G1 to S phase transition. Experimental evidence indicate that excessive expression of CDK2s should cause abnormal cell cycle regulation. Therefore, since a long time, CDK2s have been considered potential therapeutic targets for cancer therapy. In this work, onehundred and forty-nine complexes of inhibitors bound in the CDK2-ATP pocket were submitted to short MD simulations (10ns) and free energy calculation. Comparison with experimental data (K<sub>i</sub>, K<sub>d</sub> and pIC<sub>50</sub>) revealed that short simulations are exhaustive to examine the crucial ligand-protein interactions within the complexes. Information collected on MD simulations of protein-ligand complexes has been used to perform a molecular modelling approach that incorporates flexibility into structure-based pharmacophore modelling (Common Hits Approach, CHA). The high number of pharmacophore models resulting from the MD simulation was thus reduced to a few representative groups of pharmacophore models. The performance of the models has been assessed by using the ROC curves analysis. This definitive set of validated pharmacophore models could be used to screen in-house and/or commercial datasets for detection of new CDK-2 inhibitors. We provide the models to all the researchers involved in this field.
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Wang, Xing, Yuhong Xiang, Zhenzhen Ren, Yanling Zhang, and Yanjiang Qiao. "Rational questing for inhibitors of endothelin converting enzyme-1 from Salvia miltiorrhiza by combining ligand- and structure-based virtual screening." Canadian Journal of Chemistry 91, no. 6 (June 2013): 448–56. http://dx.doi.org/10.1139/cjc-2012-0523.
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In this study, a virtual screening approach based on pharmacophore and molecular docking was proposed to identify endothelin converting enzyme-1 (ECE-1) (EC 3.4.24.71) inhibitors from Salvia miltiorrhiza. First, the pharmacophore models were generated to recognize the common features of the ECE-1 inhibitors. The models were validated by a test database composed by a set of compounds known as ECE-1 inhibitors and nonactive compounds and proven to be successful in discriminating active and inactive inhibitors. Then, the best pharmacophore model was used to screen the compounds from S. miltiorrhiza. Furthermore, the Surflex-Dock procedure was used for molecular docking. All compounds from S. miltiorrhiza were docked into the active site of the target protein. An empirical scoring function was used to evaluate the affinity of the compounds and the target protein. Comparing the virtual screening results based on pharmacophore and molecular docking, respectively, 11 communal compounds with higher QFIT and docking score were hit, and the activity of some compounds was validated in the literature. The binding modes between these compounds and the ECE-1 binding site were predicted and used to identify the key interactions that contribute to the inhibitory activity of ECE-1 activity. The results show that the two methods have good consistency and can be validated and supplemented with each other.
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Zhang, Changsheng, and Luhua Lai. "Towards structure-based protein drug design." Biochemical Society Transactions 39, no. 5 (September 21, 2011): 1382–86. http://dx.doi.org/10.1042/bst0391382.
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Structure-based drug design for chemical molecules has been widely used in drug discovery in the last 30 years. Many successful applications have been reported, especially in the field of virtual screening based on molecular docking. Recently, there has been much progress in fragment-based as well as de novo drug discovery. As many protein–protein interactions can be used as key targets for drug design, one of the solutions is to design protein drugs based directly on the protein complexes or the target structure. Compared with protein–ligand interactions, protein–protein interactions are more complicated and present more challenges for design. Over the last decade, both sampling efficiency and scoring accuracy of protein–protein docking have increased significantly. We have developed several strategies for structure-based protein drug design. A grafting strategy for key interaction residues has been developed and successfully applied in designing erythropoietin receptor-binding proteins. Similarly to small-molecule design, we also tested de novo protein-binder design and a virtual screen of protein binders using protein–protein docking calculations. In comparison with the development of structure-based small-molecule drug design, we believe that structure-based protein drug design has come of age.
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Mansi, Iman A., Mahmoud A. Al-Sha'er, Nizar M. Mhaidat, Mutasem O. Taha, and Rand Shahin. "Investigation of Binding Characteristics of Phosphoinositide-dependent Kinase-1 (PDK1) Co-crystallized Ligands Through Virtual Pharmacophore Modeling Leading to Novel Anti-PDK1 Hits." Medicinal Chemistry 16, no. 7 (November 6, 2020): 860–80. http://dx.doi.org/10.2174/1573406415666190724131048.
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Background: 3-Phosphoinositide Dependent Protein Kinase-1 (PDK1) is being lately considered as an attractive and forthcoming anticancer target. A Protein Data Bank (PDB) cocrystallized crystal provides not only rigid theoretical data but also a realistic molecular recognition data that can be explored and used to discover new hits. Objective: This incited us to investigate the co-crystallized ligands' contacts inside the PDK1 binding pocket via a structure-based receptor-ligand pharmacophore generation technique in Discovery Studio 4.5 (DS 4.5). Methods: Accordingly, 35 crystals for PDK1 were collected and studied. Every single receptorligand interaction was validated and the significant ones were converted into their corresponding pharmacophoric features. The generated pharmacophores were scored by the Receiver Operating Characteristic (ROC) curve analysis. Results: Consequently, 169 pharmacophores were generated and sorted, 11 pharmacophores acquired good ROC-AUC results of 0.8 and a selectivity value above 8. Pharmacophore 1UU3_2_01 was used in particular as a searching filter to screen NCI database because of its acceptable validity criteria and its distinctive positive ionizable feature. Several low micromolar PDK1 inhibitors were revealed. The most potent hit illustrated anti-PDK1 IC50 values of 200 nM with 70% inhibition against SW480 cell lines. Conclusion: Eventually, the active hits were docked inside the PDK1 binding pocket and the recognition points between the active hits and the receptor were analyzed that led to the discovery of new scaffolds as potential PDK1 inhibitors.
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Milanos, Sinem, Katharina Kuenzel, Daniel F. Gilbert, Dieter Janzen, Manju Sasi, Andrea Buettner, Thomas M. Frimurer, and Carmen Villmann. "Structural changes at the myrtenol backbone reverse its positive allosteric potential into inhibitory GABAA receptor modulation." Biological Chemistry 399, no. 6 (May 24, 2018): 549–63. http://dx.doi.org/10.1515/hsz-2017-0262.
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Abstract GABAA receptors are ligand-gated anion channels that form pentameric arrangements of various subunits. Positive allosteric modulators of GABAA receptors have been reported as being isolated either from plants or synthesized analogs of known GABAA receptor targeting drugs. Recently, we identified monoterpenes, e.g. myrtenol as a positive allosteric modulator at α1β2 GABAA receptors. Here, along with pharmacophore-based virtual screening studies, we demonstrate that scaffold modifications of myrtenol resulted in the loss of modulatory activity. Two independent approaches, fluorescence-based compound analysis and electrophysiological recordings in whole-cell configurations were used for analysis of transfected cells. C-atoms 1 and 2 of the myrtenol backbone were identified as crucial to preserve positive allosteric potential. A modification at C-atom 2 and lack of the hydroxyl group at C-atom 1 exhibited significantly reduced GABAergic currents at α1β2, α1β2γ, α2β3, α2β3γ and α4β3δ receptors. This effect was independent of the γ2 subunit. A sub-screen with side chain length and volume differences at the C-atom 1 identified two compounds that inhibited GABAergic responses but without receptor subtype specificity. Our combined approach of pharmacophore-based virtual screening and functional readouts reveals that side chain modifications of the bridged six-membered ring structure of myrtenol are crucial for its modulatory potential at GABAA receptors.
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Wang, Wei, Minghui Wan, Dongjiang Liao, Guilin Peng, Xin Xu, Weiqiang Yin, Guixin Guo, Funeng Jiang, Weide Zhong, and Jianxing He. "Identification of Potent Chloride Intracellular Channel Protein 1 Inhibitors from Traditional Chinese Medicine through Structure-Based Virtual Screening and Molecular Dynamics Analysis." BioMed Research International 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/4751780.
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Chloride intracellular channel 1 (CLIC1) is involved in the development of most aggressive human tumors, including gastric, colon, lung, liver, and glioblastoma cancers. It has become an attractive new therapeutic target for several types of cancer. In this work, we aim to identify natural products as potent CLIC1 inhibitors from Traditional Chinese Medicine (TCM) database using structure-based virtual screening and molecular dynamics (MD) simulation. First, structure-based docking was employed to screen the refined TCM database and the top 500 TCM compounds were obtained and reranked by X-Score. Then, 30 potent hits were achieved from the top 500 TCM compounds using cluster and ligand-protein interaction analysis. Finally, MD simulation was employed to validate the stability of interactions between each hit and CLIC1 protein from docking simulation, and Molecular Mechanics/Generalized Born Surface Area (MM-GBSA) analysis was used to refine the virtual hits. Six TCM compounds with top MM-GBSA scores and ideal-binding models were confirmed as the final hits. Our study provides information about the interaction between TCM compounds and CLIC1 protein, which may be helpful for further experimental investigations. In addition, the top 6 natural products structural scaffolds could serve as building blocks in designing drug-like molecules for CLIC1 inhibition.
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Chitranshi, Nitin, Ashutosh Kumar, Samran Sheriff, Veer Gupta, Angela Godinez, Danit Saks, Soumalya Sarkar, et al. "Identification of Novel Cathepsin B Inhibitors with Implications in Alzheimer’s Disease: Computational Refining and Biochemical Evaluation." Cells 10, no. 8 (July 31, 2021): 1946. http://dx.doi.org/10.3390/cells10081946.
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Amyloid precursor protein (APP), upon proteolytic degradation, forms aggregates of amyloid β (Aβ) and plaques in the brain, which are pathological hallmarks of Alzheimer’s disease (AD). Cathepsin B is a cysteine protease enzyme that catalyzes the proteolytic degradation of APP in the brain. Thus, cathepsin B inhibition is a crucial therapeutic aspect for the discovery of new anti-Alzheimer’s drugs. In this study, we have employed mixed-feature ligand-based virtual screening (LBVS) by integrating pharmacophore mapping, docking, and molecular dynamics to detect small, potent molecules that act as cathepsin B inhibitors. The LBVS model was generated by using hydrophobic (HY), hydrogen bond acceptor (HBA), and hydrogen bond donor (HBD) features, using a dataset of 24 known cathepsin B inhibitors of both natural and synthetic origins. A validated eight-feature pharmacophore hypothesis (Hypo III) was utilized to screen the Maybridge chemical database. The docking score, MM-PBSA, and MM-GBSA methodology was applied to prioritize the lead compounds as virtual screening hits. These compounds share a common amide scaffold, and showed important interactions with Gln23, Cys29, His110, His111, Glu122, His199, and Trp221. The identified inhibitors were further evaluated for cathepsin-B-inhibitory activity. Our study suggests that pyridine, acetamide, and benzohydrazide compounds could be used as a starting point for the development of novel therapeutics.
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Majeed, Arshia, Waqar Hussain, Farkhanda Yasmin, Ammara Akhtar, and Nouman Rasool. "Virtual Screening of Phytochemicals by Targeting HR1 Domain of SARS-CoV-2 S Protein: Molecular Docking, Molecular Dynamics Simulations, and DFT Studies." BioMed Research International 2021 (May 20, 2021): 1–19. http://dx.doi.org/10.1155/2021/6661191.
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The recent COVID-19 pandemic has impacted nearly the whole world due to its high morbidity and mortality rate. Thus, scientists around the globe are working to find potent drugs and designing an effective vaccine against COVID-19. Phytochemicals from medicinal plants are known to have a long history for the treatment of various pathogens and infections; thus, keeping this in mind, this study was performed to explore the potential of different phytochemicals as candidate inhibitors of the HR1 domain in SARS-CoV-2 spike protein by using computer-aided drug discovery methods. Initially, the pharmacological assessment was performed to study the drug-likeness properties of the phytochemicals for their safe human administration. Suitable compounds were subjected to molecular docking to screen strongly binding phytochemicals with HR1 while the stability of ligand binding was analyzed using molecular dynamics simulations. Quantum computation-based density functional theory (DFT) analysis was constituted to analyze the reactivity of these compounds with the receptor. Through analysis, 108 phytochemicals passed the pharmacological assessment and upon docking of these 108 phytochemicals, 36 were screened passing a threshold of -8.5 kcal/mol. After analyzing stability and reactivity, 5 phytochemicals, i.e., SilybinC, Isopomiferin, Lycopene, SilydianinB, and Silydianin are identified as novel and potent candidates for the inhibition of HR1 domain in SARS-CoV-2 spike protein. Based on these results, it is concluded that these compounds can play an important role in the design and development of a drug against COVID-19, after an exhaustive in vitro and in vivo examination of these compounds, in future.
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Leffler, Abba E., Alexander Kuryatov, Henry A. Zebroski, Susan R. Powell, Petr Filipenko, Adel K. Hussein, Juliette Gorson, et al. "Discovery of peptide ligands through docking and virtual screening at nicotinic acetylcholine receptor homology models." Proceedings of the National Academy of Sciences 114, no. 38 (September 5, 2017): E8100—E8109. http://dx.doi.org/10.1073/pnas.1703952114.
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Venom peptide toxins such as conotoxins play a critical role in the characterization of nicotinic acetylcholine receptor (nAChR) structure and function and have potential as nervous system therapeutics as well. However, the lack of solved structures of conotoxins bound to nAChRs and the large size of these peptides are barriers to their computational docking and design. We addressed these challenges in the context of the α4β2 nAChR, a widespread ligand-gated ion channel in the brain and a target for nicotine addiction therapy, and the 19-residue conotoxin α-GID that antagonizes it. We developed a docking algorithm, ToxDock, which used ensemble-docking and extensive conformational sampling to dock α-GID and its analogs to an α4β2 nAChR homology model. Experimental testing demonstrated that a virtual screen with ToxDock correctly identified three bioactive α-GID mutants (α-GID[A10V], α-GID[V13I], and α-GID[V13Y]) and one inactive variant (α-GID[A10Q]). Two mutants, α-GID[A10V] and α-GID[V13Y], had substantially reduced potency at the human α7 nAChR relative to α-GID, a desirable feature for α-GID analogs. The general usefulness of the docking algorithm was highlighted by redocking of peptide toxins to two ion channels and a binding protein in which the peptide toxins successfully reverted back to near-native crystallographic poses after being perturbed. Our results demonstrate that ToxDock can overcome two fundamental challenges of docking large toxin peptides to ion channel homology models, as exemplified by the α-GID:α4β2 nAChR complex, and is extendable to other toxin peptides and ion channels. ToxDock is freely available atrosie.rosettacommons.org/tox_dock.
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Rahman, Noor, Zarrin Basharat, Muhammad Yousuf, Giuseppe Castaldo, Luca Rastrelli, and Haroon Khan. "Virtual Screening of Natural Products against Type II Transmembrane Serine Protease (TMPRSS2), the Priming Agent of Coronavirus 2 (SARS-CoV-2)." Molecules 25, no. 10 (May 12, 2020): 2271. http://dx.doi.org/10.3390/molecules25102271.
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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused about 2 million infections and is responsible for more than 100,000 deaths worldwide. To date, there is no specific drug registered to combat the disease it causes, named coronavirus disease 2019 (COVID-19). In the current study, we used an in silico approach to screen natural compounds to find potent inhibitors of the host enzyme transmembrane protease serine 2 (TMPRSS2). This enzyme facilitates viral particle entry into host cells, and its inhibition blocks virus fusion with angiotensin-converting enzyme 2 (ACE2). This, in turn, restricts SARS-CoV-2 pathogenesis. A three-dimensional structure of TMPRSS2 was built using SWISS-MODEL and validated by RAMPAGE. The natural compounds library Natural Product Activity and Species Source (NPASS), containing 30,927 compounds, was screened against the target protein. Two techniques were used in the Molecular Operating Environment (MOE) for this purpose, i.e., a ligand-based pharmacophore approach and a molecular docking-based screening. In total, 2140 compounds with pharmacophoric features were retained using the first approach. Using the second approach, 85 compounds with molecular docking comparable to or greater than that of the standard inhibitor (camostat mesylate) were identified. The top 12 compounds with the most favorable structural features were studied for physicochemical and ADMET (absorption, distribution, metabolism, excretion, toxicity) properties. The low-molecular-weight compound NPC306344 showed significant interaction with the active site residues of TMPRSS2, with a binding energy score of −14.69. Further in vitro and in vivo validation is needed to study and develop an anti-COVID-19 drug based on the structures of the most promising compounds identified in this study.
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Coghi, Paolo, Li Jun Yang, Jerome P. L. Ng, Richard K. Haynes, Maurizio Memo, Alessandra Gianoncelli, Vincent Kam Wai Wong, and Giovanni Ribaudo. "A Drug Repurposing Approach for Antimalarials Interfering with SARS-CoV-2 Spike Protein Receptor Binding Domain (RBD) and Human Angiotensin-Converting Enzyme 2 (ACE2)." Pharmaceuticals 14, no. 10 (September 23, 2021): 954. http://dx.doi.org/10.3390/ph14100954.
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Host cell invasion by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is mediated by the interaction of the viral spike protein (S) with human angiotensin-converting enzyme 2 (ACE2) through the receptor-binding domain (RBD). In this work, computational and experimental techniques were combined to screen antimalarial compounds from different chemical classes, with the aim of identifying small molecules interfering with the RBD-ACE2 interaction and, consequently, with cell invasion. Docking studies showed that the compounds interfere with the same region of the RBD, but different interaction patterns were noted for ACE2. Virtual screening indicated pyronaridine as the most promising RBD and ACE2 ligand, and molecular dynamics simulations confirmed the stability of the predicted complex with the RBD. Bio-layer interferometry showed that artemisone and methylene blue have a strong binding affinity for RBD (KD = 0.363 and 0.226 μM). Pyronaridine also binds RBD and ACE2 in vitro (KD = 56.8 and 51.3 μM). Overall, these three compounds inhibit the binding of RBD to ACE2 in the μM range, supporting the in silico data.
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Huo, Xiaoqian, Fang Lu, Liansheng Qiao, Gongyu Li, and Yanling Zhang. "A Component Formula of Chinese Medicine for Hypercholesterolemia Based on Virtual Screening and Biology Network." Evidence-Based Complementary and Alternative Medicine 2018 (June 28, 2018): 1–11. http://dx.doi.org/10.1155/2018/1854972.
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Hypercholesterolemia is a risk factor to atherosclerosis and coronary heart disease II. The abnormal rise of cholesterol in plasma is the main symptom. Cholesterol synthesis pathway is an important pathway of the origin of cholesterol, which is an essential pathway for the therapy of hypercholesterolemia. The 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMG-CoA reductase), squalene synthase (SQS), and sterol regulatory element binding protein-2 (SREBP-2) are closely connected with the synthesis of cholesterol. The inhibition of these targets can reduce the cholesterol in plasma. This study aimed to build a component formula including three Traditional Chinese Medicines (TCM) components with the inhibition activity of these targets by using virtual screening and biological network. Structure-based pharmacophore models of HMG-CoA reductase and SQS and ligand-based pharmacophore model of SREBP-2 were constructed to screen the Traditional Chinese Medicine Database (TCMD). Molecular docking was used for further screening of components of HMG-CoA reductase and SQS. Then, metabolic network was constructed to elucidate the comprehensive interaction of three targets for lipid metabolism. Finally, three potential active compounds were obtained, which are poncimarin, hexahydrocurcumin, and forsythoside C. The source plants of the compounds were also taken into account, which should have known action of lowering hyperlipidemia. The lipid-lowering effect of hexahydrocurcumin was verified by experiment in vitro. The components that originated from TCMs with lipid-lowering efficacy made up a formula with a synergistic effect through the computer aid drug design methods. The research provides a fast and efficient method to build TCM component formula and it may inspire the study of the explanation of TCM formula mechanism.
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Wahrmund, Rebecca D., Kristiana A. Avad, Stephanie M. Reeve, Jesse A. Jones, Thao La, Richard E. Lee, and Kirk E. Hevener. "1233. Identification of Novel Inhibitors of Clostridioides difficile Enoyl-Reductase II (FabK) by High-Throughput Virtual and Experimental Screening." Open Forum Infectious Diseases 7, Supplement_1 (October 1, 2020): S636. http://dx.doi.org/10.1093/ofid/ofaa439.1418.
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Abstract Background Clostridioides difficile is one of only three bacteria categorized as an ‘urgent’ drug-resistant threat by the CDC. This pathogen is responsible for nearly 500,000 hospital-acquired infections and 29,000 deaths per year at a cost of nearly $4.8 billion. There is a critical need for the identification of novel, anti-difficile agents with narrow-spectrum activity that can spare the human microbiome. We previously reported the essentiality of the FAS-II enzyme, enoyl-ACP reductase (FabK) in C. difficile, and the narrow-spectrum activity of a series of phenylimidazole inhibitors. We present here experimental and virtual compound screening studies that identified novel FabK inhibitors with sub-micromolar activity and follow-up SAR and structural studies. Methods Using a novel luminescence assay, 20K diverse compounds from the St. Jude drug-like and lead-like libraries were screened. In parallel, a ligand-based virtual screen was performed against 2.4 million lead- and drug-like compounds from commercial libraries. Hit compounds were confirmed using an orthogonal fluorescence-intensity assay and SAR studies were performed by testing commercially available hit analogs. The most potent inhibitor was advanced into co-crystallography structural studies. Results The compound screening campaigns resulted in the identification of several confirmed hit compounds with low to sub-micromolar activity and novel scaffolds relative to the known phenylimidazole inhibitors. Importantly, the first confirmed nanomolar inhibitor of C. difficile FabK was identified and validated with an IC50 of 0.35 μM. SAR studies of the hits along with a high-resolution co-crystal structure have allowed new insights into the key binding determinants and structural requirements for activity as well as the structural requirements for species specificity. Conclusion The C. difficile FabK enzyme offers a promising narrow-spectrum drug target that can potentially spare the human microbiome. The known activity of the phenylimidazole inhibitor series supports the druggability of this target. The newly discovered inhibitor class presented here further demonstrates the potential of FabK inhibition and will facilitate the development of clinically relevant, narrow-spectrum anti-difficile agents. Disclosures All Authors: No reported disclosures
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Muhseen, Ziyad Tariq, and Guanglin Li. "Promising Terpenes as Natural Antagonists of Cancer: An In-Silico Approach." Molecules 25, no. 1 (December 30, 2019): 155. http://dx.doi.org/10.3390/molecules25010155.
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Overexpression of murine double minute 2 (MDM2) results in the inactivation of p53 and causes cancer which is a leading cause of death in recent era. In recent decades, much attention has been paid to discover potential inhibitors against MDM2 in order to cure cancer. Outcomes from studies proposes that the MDM2 is a hot target to screen potent antagonists. Thus, this study aims at discovering natural compounds using several computational approaches to inhibit the MDM2 and to eliminate p53-MDM2 interaction, which would result in the reactivation of p53 activity. A library of 500 terpenes was prepared and several virtual screening approaches were employed to find out the best hits which could serve as p53-MDM2 antagonists. On the basis of the designed protocol, three terpenes were selected. In the present study, for the stability and validation of selected three protein-ligand complexes 20 ns molecular dynamics simulations and principal component analyses (PCA) were performed. Results found that the selected terpenes hits (3-trans-p-coumaroyl maslinic acid, Silvestrol and Betulonic acid) are potential inhibitors of p53–MDM2 interaction and could serve as potent antagonists.
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Durai, Prasannavenkatesh, Young-Joon Ko, Jin-Chul Kim, Cheol-Ho Pan, and Keunwan Park. "Identification of Tyrosinase Inhibitors and Their Structure-Activity Relationships via Evolutionary Chemical Binding Similarity and Structure-Based Methods." Molecules 26, no. 3 (January 22, 2021): 566. http://dx.doi.org/10.3390/molecules26030566.
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Tyrosinase is an enzyme that plays a crucial role in the melanogenesis of humans and the browning of food products. Thus, tyrosinase inhibitors that are useful to the cosmetic and food industries are required. In this study, we have used evolutionary chemical binding similarity (ECBS) to screen a virtual chemical database for human tyrosinase, which resulted in seven potential tyrosinase inhibitors confirmed through the tyrosinase inhibition assay. The tyrosinase inhibition percentage for three of the new actives was over 90% compared to 61.9% of kojic acid. From the structural analysis through pharmacophore modeling and molecular docking with the human tyrosinase model, the pi–pi interaction of tyrosinase inhibitors with conserved His367 and the polar interactions with Asn364, Glu345, and Glu203 were found to be essential for tyrosinase–ligand interactions. The pharmacophore features and the docking models showed high consistency, revealing the possible essential binding interactions of inhibitors to human tyrosinase. We have also presented the activity cliff analysis that successfully revealed the chemical features related to substantial activity changes found in the new tyrosinase inhibitors. The newly identified inhibitors and their structure–activity relationships presented here will help to identify or design new human tyrosinase inhibitors.
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Kwofie, Samuel K., Emmanuel Broni, Bismark Dankwa, Kweku S. Enninful, Gabriel B. Kwarko, Louis Darko, Ravi Durvasula, et al. "Outwitting an Old Neglected Nemesis: A Review on Leveraging Integrated Data-Driven Approaches to Aid in Unraveling of Leishmanicides of Therapeutic Potential." Current Topics in Medicinal Chemistry 20, no. 5 (March 27, 2020): 349–66. http://dx.doi.org/10.2174/1568026620666200128160454.
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The global prevalence of leishmaniasis has increased with skyrocketed mortality in the past decade. The causative agent of leishmaniasis is Leishmania species, which infects populations in almost all the continents. Prevailing treatment regimens are consistently inefficient with reported side effects, toxicity and drug resistance. This review complements existing ones by discussing the current state of treatment options, therapeutic bottlenecks including chemoresistance and toxicity, as well as drug targets. It further highlights innovative applications of nanotherapeutics-based formulations, inhibitory potential of leishmanicides, anti-microbial peptides and organometallic compounds on leishmanial species. Moreover, it provides essential insights into recent machine learning-based models that have been used to predict novel leishmanicides and also discusses other new models that could be adopted to develop fast, efficient, robust and novel algorithms to aid in unraveling the next generation of anti-leishmanial drugs. A plethora of enriched functional genomic, proteomic, structural biology, high throughput bioassay and drug-related datasets are currently warehoused in both general and leishmania-specific databases. The warehoused datasets are essential inputs for training and testing algorithms to augment the prediction of biotherapeutic entities. In addition, we demonstrate how pharmacoinformatics techniques including ligand-, structure- and pharmacophore-based virtual screening approaches have been utilized to screen ligand libraries against both modeled and experimentally solved 3D structures of essential drug targets. In the era of data-driven decision-making, we believe that highlighting intricately linked topical issues relevant to leishmanial drug discovery offers a one-stop-shop opportunity to decipher critical literature with the potential to unlock implicit breakthroughs.
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Chen, Kuan-Chung, Hsin-Yi Chen, and Calvin Yu-Chian Chen. "Potential Protein Phosphatase 2A Agents from Traditional Chinese Medicine against Cancer." Evidence-Based Complementary and Alternative Medicine 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/436863.
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Protein phosphatase 2A (PP2A) is an important phosphatase which regulates various cellular processes, such as protein synthesis, cell growth, cellular signaling, apoptosis, metabolism, and stress responses. It is a holoenzyme composed of the structural A and catalytic C subunits and a regulatory B subunit. As an environmental toxin, okadaic acid, is a tumor promoter and binds to PP2A catalytic C subunit and the cancer-associated mutations in PP2A structural A subunit in human tumor tissue; PP2A may have tumor-suppressing function. It is a potential drug target in the treatment of cancer. In this study, we screen the TCM compounds in TCM Database@Taiwan to investigate the potent lead compounds as PP2A agent. The results of docking simulation are optimized under dynamic conditions by MD simulations after virtual screening to validate the stability of H-bonds between PP2A-αprotein and each ligand. The top TCM candidates, trichosanatine and squamosamide, have potential binding affinities and interactions with key residues Arg89 and Arg214 in the docking simulation. In addition, these interactions were stable under dynamic conditions. Hence, we propose the TCM compounds, trichosanatine and squamosamide, as potential candidates as lead compounds for further study in drug development process with the PP2A-αprotein.
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Hsiao, Ya-Li, Pei-Chun Chang, Hung-Jin Huang, Chia-Chen Kuo, and Calvin Yu-Chian Chen. "Treatment of Acute Lymphoblastic Leukemia from Traditional Chinese Medicine." Evidence-Based Complementary and Alternative Medicine 2014 (2014): 1–21. http://dx.doi.org/10.1155/2014/601064.
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Acute lymphoblastic leukemia (ALL) is a cancer that immature white blood cells continuously overproduce in the bone marrow. These cells crowd out normal cells in the bone marrow bringing damage and death. Methotrexate (MTX) is a drug used in the treatment of various cancer and autoimmune diseases. In particular, for the treatment of childhood acute lymphoblastic leukemia, it had significant effect. MTX competitively inhibits dihydrofolate reductase (DHFR), an enzyme that participates in the tetrahydrofolate synthesis so as to inhibit purine synthesis. In addition, its downstream metabolite methotrexate polyglutamates (MTX-PGs) inhibit the thymidylate synthase (TS). Therefore, MTX can inhibit the synthesis of DNA. However, MTX has cytotoxicity and neurotoxin may cause multiple organ injury and is potentially lethal. Thus, the lower toxicity drugs are necessary to be developed. Recently, diseases treatments with Traditional Chinese Medicine (TCM) as complements are getting more and more attention. In this study, we attempted to discover the compounds with drug-like potential for ALL treatment from the components in TCM. We applied virtual screen and QSAR models based on structure-based and ligand-based studies to identify the potential TCM component compounds. Our results show that the TCM compounds adenosine triphosphate, manninotriose, raffinose, and stachyose could have potential to improve the side effects of MTX for ALL treatment.
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Kritsi, Eftichia, Minos-Timotheos Matsoukas, Constantinos Potamitis, Anastasia Detsi, Marija Ivanov, Marina Sokovic, and Panagiotis Zoumpoulakis. "Novel Hit Compounds as Putative Antifungals: The Case of Aspergillus fumigatus." Molecules 24, no. 21 (October 25, 2019): 3853. http://dx.doi.org/10.3390/molecules24213853.
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The prevalence of invasive fungal infections has been dramatically increased as the size of the immunocompromised population worldwide has grown. Aspergillus fumigatus is characterized as one of the most widespread and ubiquitous fungal pathogens. Among antifungal drugs, azoles have been the most widely used category for the treatment of fungal infections. However, increasingly, azole-resistant strains constitute a major problem to be faced. Towards this direction, our study focused on the identification of compounds bearing novel structural motifs which may evolve as a new class of antifungals. To fulfil this scope, a combination of in silico techniques and in vitro assays were implemented. Specifically, a ligand-based pharmacophore model was created and served as a 3D search query to screen the ZINC chemical database. Additionally, molecular docking and molecular dynamics simulations were used to improve the reliability and accuracy of virtual screening results. In total, eight compounds, bearing completely different chemical scaffolds from the commercially available azoles, were proposed and their antifungal activity was evaluated using in vitro assays. Results indicated that all tested compounds exhibit antifungal activity, especially compounds 1, 2, and 4, which presented the most promising minimum inhibitory concentration (MIC) and minimum fungicidal concentration (MFC) values and, therefore, could be subjected to further hit to lead optimization.
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Liu, Juan, Xinjie Lian, Feng Liu, Xueling Yan, Chunyan Cheng, Lijia Cheng, Xiaolin Sun, and Zheng Shi. "Identification of Novel Key Targets and Candidate Drugs in Oral Squamous Cell Carcinoma." Current Bioinformatics 15, no. 4 (June 11, 2020): 328–37. http://dx.doi.org/10.2174/1574893614666191127101836.
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Background: Oral Squamous Cell Carcinoma (OSCC) is the most common malignant epithelial neoplasm. It is located within the top 10 ranking incidence of cancers with a poor prognosis and low survival rates. New breakthroughs of therapeutic strategies are therefore needed to improve the survival rate of OSCC harboring patients. Objective: Since targeted therapy is considered as the most promising therapeutic strategies in cancer, it is of great significance to identify novel targets and drugs for the treatment of OSCC. Methods: A series of bioinformatics approaches were launched to identify the hub proteins and their potential agents. Microarray analysis and several online functional activity network analysis were firstly utilized to recognize drug targets in OSCC. Subsequently, molecular docking was used to screen their potential drugs from the specs chemistry database. At the same time, the assessment of ligand-based virtual screening model was also evaluated. Results: In this study, two microarray data (GSE31056, GSE23558) were firstly selected and analyzed to get consensus candidate genes including 681 candidate genes. Additionally, we selected 33 candidate genes based on whether they belong to the kinases and transcription factors and further clustered candidate hub targets based on functions and signaling pathways with significant enrichment analysis by using DAVID and STRING online databases. Then, core PPI network was then identified and we manually selected GRB2 and IGF1 as the key drug targets according to the network analysis and previous references. Lastly, virtual screening was performed to identify potential small molecules which could target these two targets, and such small molecules can serve as the promising candidate agents for future drug development. Conclusion: In summary, our study might provide novel insights for understanding of the underlying molecular events of OSCC, and our discovered candidate targets and candidate agents could be used as the promising therapeutic strategies for the treatment of OSCC.
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Kuthuru, Srikanth, Adam T. Szafran, Fabio Stossi, Michael A. Mancini, and Arvind Rao. "Leveraging Image-Derived Phenotypic Measurements for Drug-Target Interaction Predictions." Cancer Informatics 18 (January 2019): 117693511985659. http://dx.doi.org/10.1177/1176935119856595.
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In recent years, protein kinases have become some of the most significant drug targets in cancer patients. Kinases are known to regulate the activity of many human proteins, and consequently their inhibition has been used to control cancer proliferation. A significant challenge in drug discovery is the rapid and efficient identification of new small molecules. In this study, we propose a novel in silico drug discovery approach to identify kinase targets that impinge on nuclear receptor signaling with data generated using high-content analysis (HCA). A high-throughput imaging dataset was generated from an siRNA human kinome screen on engineered cells that allow direct visualization of effects on estrogen receptor-α or a chimeric progesterone receptor B binding to specific DNA. Two types of kinase descriptors are extracted from these imaging data: first, a population-median-based descriptor and second a bag-of-words (BoW) descriptor that can capture heterogeneity information in the imaging data. Using these descriptors, we provide prediction results of drug-kinase-target interactions based on single-task learning, multi-task learning, and collaborative filtering methods. The best performing model in target-based drug discovery gives an area under the receiver operating characteristic curve (AUC) of 0.86, whereas the best model in ligand-based discovery gives an AUC of 0.79. These promising results suggest that imaging-based information can be used as an additional source of information to existing virtual screening methods, thereby making the drug discovery process more time and cost efficient.
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Sharda, Saphy, Ravina Khandelwal, Ritu Adhikary, Diksha Sharma, Manisha Majhi, Tajamul Hussain, Anuraj Nayarisseri, and Sanjeev Kumar Singh. "A Computer - Aided Drug Designing for Pharmacological Inhibition of Mutant ALK for the Treatment of Non-small Cell Lung Cancer." Current Topics in Medicinal Chemistry 19, no. 13 (August 27, 2019): 1129–44. http://dx.doi.org/10.2174/1568026619666190521084941.
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Background: Lung cancer is the most common among all the types of cancer worldwide with 1.8 million people diagnosed every year, leading to 1.6 million deaths every year according to the American cancer society. The involvement of mutated Anaplasic Lymphoma Kinase (ALK) positive fusion protein in the progression of NSCLC has made a propitious target to inhibit and treat NSCLC. In the present study, the main motif is to screen the most effective inhibitor against ALK protein with the potential pharmacological profile. The ligands selected were docked with Molegro Virtual Docker (MVD) and CEP-37440 (PubChem CID- 71721648) was the best docked pre-established compound with a permissible pharmacological profile. Methods: The selected ligands were docked with Molegro Virtual Docker (MVD). With reference to the obtained compound with the lowest re-rank score, PubChem database was virtually screened to retrieve a large set of similar compounds which were docked to find the compound with higher affinity. Further comparative studies and in silico prediction included pharmacophore studies, proximity energy parameters, ADMET and BOILED-egg plot analysis. Results: CEP-37440 (PubChem CID- 71721648) was the best docked pre-established compound with preferable pharmacological profile and PubChem compound CID-123449015 came out as the most efficient virtually screened inhibitor. Interestingly, the contours of the virtual screened compound PubChem CID- 123449015 fall within our desired high volume cavity of protein having admirable property to control the ALK regulation to prevent carcinogenesis in NSCLC. BOILED-Egg plot analysis depicts that both the compounds have analogous characteristics in the divergent aspects. Moreover, in the evaluations of Blood Brain Barrier, Human Intestinal Absorption, AMES toxicity, and LD50, the virtually screened compound (PubChem CID-123449015) was found within high optimization. Conclusion: These investigations denote that the virtually screened compound (PubChem CID- 123449015) is more efficient to be a better prospective candidate for NSCLC treatment having good pharmacological profile than the pre-established compound CEP-37440 (PubChem CID- 71721648) with low re-rank score. The identified virtually screened compound has high potential to act as an ALK inhibitor and can show promising results in the research of non-small cell lung cancer (NSCLC).
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Schurdak, Mark E., Martin J. Voorbach, Lan Gao, Xueheng Cheng, Kenneth M. Comess, Susan M. Rottinghaus, Usha Warrior, Hoa N. Truong, David J. Burns, and Bruce A. Beutel. "Complex Gel Permeation Assays for Screening Combinatorial Libraries." Journal of Biomolecular Screening 6, no. 5 (October 2001): 313–23. http://dx.doi.org/10.1177/108705710100600505.
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Gel permeation methods have been commonly used to screen combinatorial libraries synthesized on a solid support. We report here three screens of combinatorial libraries using gel permeation assays. These include a simple enzymatic assay to identify inhibitors of the influenza enzyme neuraminidase, and two more complex assays designed to screen for inhibitors of the interleukin-8 (IL-8)-IL-8 receptor and the urokinase-urokinase receptor interactions, respectively. The IL-8 ligand-receptor assay makes use of IL-8 receptor-expressing cells attached to a membrane, thus enabling washing steps as part of the assay. The urokinase ligand-receptor assay employs an enzyme-linked immunosorbent assay-type format, previously thought to be amenable only to well-based assays. The results of these three screens are reported here, including the discovery of a novel series of acyclic inhibitors of neuraminidase. The development of complex assays in a gel permeation format allows for the routine screening of combinatorially as well as noncombinatorially made compound collections against virtually any kind of target, and is being widely used in our high throughput screening operations.
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Naisam, Shahanas, Vidhya V. S., Suvanish Kumar, and Nidhin Sreekumar. "Virtual Screening of Phyto Chemicals Against SARS-CoV-2 Targets." International Journal of Quantitative Structure-Property Relationships 6, no. 3 (July 2021): 61–76. http://dx.doi.org/10.4018/ijqspr.2021070105.
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The COVID-19 pandemic wave has recommenced and is spreading like wildfire across the globe. The well-reported antiviral potency of phyto compounds could offer potential drug molecules for the current predicament. The present study analyses the molecular interaction of selected phyto compounds and SARS-CoV-2 molecular target proteins, namely spike protein, RNA-dependent RNA polymerase, 3C-like proteases, and papain-like protease. Ten newly modeled ligands were also considered for the study. Molecular docking analysis was carried out independently using MOE, AutoDock Vina, Schrodinger-Glide, and the stability of protein-ligand interaction was validated through molecular dynamics simulation. Petunidin interacts with spike protein resulting in a good Gscore, binding energy, and H-bond interaction. Also, alions, letestuianin-A, (+)-pinitol show better interaction with RdRp, 3CL-protease, and papain-like protease, respectively. The presented work screens through 2314 ligands to yield top-ranked molecules which could be taken up to develop potential lead molecules via in-vivo analysis.
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Tang, Min, Yang Fu, Ying Fan, Ming-Shui Fu, Zhi Zheng, and Xun Xu. "In-silico design of novel myocilin inhibitors for glaucoma therapy." Tropical Journal of Pharmaceutical Research 16, no. 10 (November 15, 2017): 2527–33. http://dx.doi.org/10.4314/tjpr.v16i10.29.
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Purpose: To explore newer computational approaches in the design of novel myocilin inhibitors for the treatment of glaucoma.Methods: An in-silico virtual screening technique based on simulation of molecular docking was utilised to design a novel myocilin inhibitors for the treatment of glaucoma. The designed novel molecules were theoretically evaluated to predict their pharmacokinetic properties and toxic effects. Lead molecules were screened out in virtual screening technique on the basis of low binding energies obtained in AutoDock based molecular docking simulation.Results: Out of ten top lead compounds screened, ZINC01729523 and ZINC04692015 were promising, having shown potent inhibition of myocilin, good pharmacokinetic properties and absence of any toxic effects.Conclusion: In-silico virtual screening of molecular libraries containing a large number of ligands is very useful for short-listing of potential lead molecules for further structure-based discovery of antiglaucoma-drugs.Keywords: Glaucoma, Myocilin, Docking, Virtual-screening, Autodock, Ligand, Drug design
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Vincenzi, Marian, Katarzyna Bednarska, and Zbigniew Leśnikowski. "Comparative Study of Carborane- and Phenyl-Modified Adenosine Derivatives as Ligands for the A2A and A3 Adenosine Receptors Based on a Rigid in Silico Docking and Radioligand Replacement Assay." Molecules 23, no. 8 (July 25, 2018): 1846. http://dx.doi.org/10.3390/molecules23081846.
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Adenosine receptors are involved in many physiological processes and pathological conditions and are therefore attractive therapeutic targets. To identify new types of effective ligands for these receptors, a library of adenosine derivatives bearing a boron cluster or phenyl group in the same position was designed. The ligands were screened in silico to determine their calculated affinities for the A2A and A3 adenosine receptors. An virtual screening protocol based on the PatchDock web server was developed. In the first screening phase, the effects of the functional group (organic or inorganic modulator) on the adenosine ligand affinity for the receptors were determined. Then, the lead compounds were identified for each receptor in the second virtual screening phase. Two pairs of the most promising ligands, compounds 3 and 4, and two ligands with lower affinity scores (compounds 11 and 12, one with a boron cluster and one with a phenyl group) were synthesized and tested in a radioligand replacement assay for affinity to the A2A and A3 receptors. A reasonable correlation of in silico and biological assay results was observed. In addition, the effects of a phenyl group and boron cluster, which is new adenosine modifiers, on the adenosine ligand binding were compared.