Positive Modulators of Glycine Receptors with Analgesic Potential Identified by Virtual Screening
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Tuberculosis (TB) disease continues to remain one of the global threats for mankind. Till date many antibacterial compounds have been identified to target mycobacterium tuberculosis (MTB). However, the mutating nature of the mycobacteria has always posed a challenge for designing newer drugs which can target both the non-mutating and mutating forms of TB. In this process, Mycobacterial membrane protein Large 3 (MmpL3) transporter was identified as one of the key targets for inhibiting tuberculosis. Herein we have made an effort to find potential inhibitors against MmpL3 by using a pharmacophore-based virtual screening workflow, followed by molecular docking studies and molecular dynamic simulations. Based on a set of 220 compounds showing anti-tubercular activity proposed to target MmpL3 transporter with MIC values ranging from 0.003 to 737 μM, a 5-point pharmacophore ADHHR_2 model possessing one hydrogen acceptor, one hydrogen donor, two hydrophobic groups and an aromatic ring system was generated. The model validated by enrichment study was used to screen Asinex and DrugBank database to identify a potential lead compound such as DrugBank_6059 that was found to show better binding affinity (-11.36) and hydrophobic interactions with target protein in comparison to standard drug SQ109.Communicated by Ramaswamy H. Sarma.
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Protein kinases are one of the most targeted protein families in current drug discovery pipelines. They are implicated in many oncological, inflammatory, CNS-related and other clinical indications. Virtual screening is a computational technique with a diverse set of available tools that has been shown many times to provide novel starting points for kinase-directed drug discovery. This review starts with a concise overview of the function, structural features and inhibitory mechanisms of protein kinases. In addition to briefly reviewing the practical aspects of structure-based virtual screenings, we discuss several case studies to illustrate the state of the art in the virtual screening for type I, type II, allosteric (type III-V) and covalent (type VI) kinase inhibitors. With this review, we strive to provide a summary of the latest advances in the structure-based discovery of novel kinase inhibitors, as well as a practical tool to anyone who wishes to embark on such an endeavor.
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Objective: SARS-CoV-2 is a pandemic virus characterized by upper respiratory tract infection and can range from mild symptoms to severe complications. In this case, drug repurposing and computer-aided studies have become very important to find emergency solutions. In this study, drug-target interactions on three nonstructural protein structures of SARS-CoV-2 of 8820 drug candidates or drug molecules obtained from the DrugBank database were analyzed. Material and Method: Comprehensive virtual screening and molecular docking studies from 8820 drug molecules or candidates obtained from the DrugBank database were performed on the RNA binding protein, 2'-O-methyltransferase, and endoribonuclease of SARS-CoV-2; and potential drug candidates were determined for each target. Virtual screening studies have been done with High-Throughput Virtual Screening (HTVS), Standard Precision (SP), Extra Precision (XP), and Molecular Mechanics Generalized Born Surface Area (MM-GBSA). Also, information about the clinical findings, transmission, pathogenesis, and treatment of SARS-CoV-2 has been given. Result and Discussion: Drug-target interactions on three nonstructural protein structures of SARS-CoV-2 of 8820 drug candidates or drug molecules obtained from the DrugBank database were analyzed. Potential compound recommendations for each drug target were presented. Information was given about key amino acids where active sites of drug target proteins interact with ligands. This study is expected to be useful in target-based drug development studies on the proteins of SARS-CoV-2.
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A virtual screening campaign was conducted in order to discover new anticonvulsant drug candidates for the treatment of refractory epilepsy. To this purpose, a topological discriminant function to identify antiMES drugs and a sequential filtering methodology to discriminate P-glycoprotein substrates and nonsubstrates were jointly applied to ZINC 5 and DrugBank databases. The virtual filters combine an ensemble of 2D classifiers and docking simulations. In the light of the results, 10 structurally diverse compounds were acquired and tested in animal models of seizure and the rotorod test. All 10 candidates showed some level of protection against MES test.
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In the process of new drug discovery, the application of virtual screening can enrich active compounds, reduce the cost of drug screening, and increase the feasibility of drug screening. Therefore virtual screening technology has become an important approach for new drug discovery. As virtual screening and bioactivity screening possess different advantages, their combination can effectively promote new drug discovery. In the present paper, the application and the trend of removal of non-drug compounds, removal of false positive compounds, pharmacophore searching, molecular docking, and molecular similarity in the process of drug discovery are introduced in order to obtain more benefit from virtual screening strategy for new drug discovery.
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Phenols are widely distributed in various plants and plant-derived foods. Currently, there is an increasing interest in their application as food supplements. In this study we performed a virtual screening to identify potential molecular targets of phenolic compounds derived from medicinal plants known for their antioxidant and anticancer effects. A dataset of 75 phenols, reported in the literature and a virtual library of 7770 unique drug compounds, extracted from the DrugBank database (https://www.drugbank.ca/) were used. Multi-conformer structure databases were created using OpenEye OMEGA, shape- and chemical-based overlays of the conformers were performed in OpenEye ROCS (https://www.eyesopen.com/). As a result of the virtual screening, followed by data filtration and analysis, two bacterial enzymes, responsible for DNA replication, were suggested as potential novel targets of a plant-derived hydroxyanthraquinone. This research allows outlining the potential receptor-mediated pharmacological mechanisms of phenolic compounds and aims to be a first step in the development of in silico protocol for their prioritisation as healthy dietary supplements.
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Drug discovery is a long process with a low rate of successful new therapeutic discovery regardless of the advances in information technologies. Identification of candidate proteins is an essential step for the drug discovery and it usually requires considerable time and efforts in the drug discovery. The drug discovery is not a logical, but a fortuitous process. Nevertheless, considerable amount of information on drugs are accumulated in UniProt, NCBI, or DrugBank. As a result, it has become possible to try to devise new computational methods classifying drug target candidates extracting the common features of known drug target proteins. In this paper, we devise a method for drug target protein classification by using weighted feature summation and Support Vector Machine. According to our evaluation, the method is revealed to show moderate accuracy 85~90%. This indicates that if the devised method is used appropriately, it can contribute in reducing the time and cost of the drug discovery process, particularly in identifying new drug target proteins.
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Target prediction and virtual screening are two powerful tools of computer-aided drug design. Target identification is of great significance for hit discovery, lead optimization, drug repurposing and elucidation of the mechanism. Virtual screening can improve the hit rate of drug screening to shorten the cycle of drug discovery and development. Therefore, target prediction and virtual screening are of great importance for developing highly effective drugs against COVID-19. Here we present D3AI-CoV, a platform for target prediction and virtual screening for the discovery of anti-COVID-19 drugs. The platform is composed of three newly developed deep learning-based models i.e., MultiDTI, MPNNs-CNN and MPNNs-CNN-R models. To compare the predictive performance of D3AI-CoV with other methods, an external test set, named Test-78, was prepared, which consists of 39 newly published independent active compounds and 39 inactive compounds from DrugBank. For target prediction, the areas under the receiver operating characteristic curves (AUCs) of MultiDTI and MPNNs-CNN models are 0.93 and 0.91, respectively, whereas the AUCs of the other reported approaches range from 0.51 to 0.74. For virtual screening, the hit rate of D3AI-CoV is also better than other methods. D3AI-CoV is available for free as a web application at http://www.d3pharma.com/D3Targets-2019-nCoV/D3AI-CoV/index.php, which can serve as a rapid online tool for predicting potential targets for active compounds and for identifying active molecules against a specific target protein for COVID-19 treatment.
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Virtual ligand screening is an integral part of the modern drug discovery process. Traditional ligand-based, virtual screening approaches are fast but require a set of structurally diverse ligands known to bind to the target. Traditional structure-based approaches require high-resolution target protein structures and are computationally demanding. In contrast, the recently developed threading/structure-based FINDSITE-based approaches have the advantage that they are as fast as traditional ligand-based approaches and yet overcome the limitations of traditional ligand- or structure-based approaches. These new methods can use predicted low-resolution structures and infer the likelihood of a ligand binding to a target by utilizing ligand information excised from the target's remote or close homologous proteins and/or libraries of ligand binding databases. Here, we develop an improved version of FINDSITE, FINDSITEfilt, that filters out false positive ligands in threading identified templates by a better binding site detection procedure that includes information about the binding site amino acid similarity. We then combine FINDSITEfilt with FINDSITEX that uses publicly available binding databases ChEMBL and DrugBank for virtual ligand screening. The combined approach, FINDSITEcomb, is compared to two traditional docking methods, AUTODOCK Vina and DOCK 6, on the DUD benchmark set. It is shown to be significantly better in terms of enrichment factor, dependence on target structure quality, and speed. FINDSITEcomb is then tested for virtual ligand screening on a large set of 3576 generic targets from the DrugBank database as well as a set of 168 Human GPCRs. Excluding close homologues, FINDSITEcomb gives an average enrichment factor of 52.1 for generic targets and 22.3 for GPCRs within the top 1% of the screened compound library. Around 65% of the targets have better than random enrichment factors. The performance is insensitive to target structure quality, as long as it has a TM-score ≥ 0.4 to native. Thus, FINDSITEcomb makes the screening of millions of compounds across entire proteomes feasible. The FINDSITEcomb web service is freely available for academic users at http://cssb.biology.gatech.edu/skolnick/webservice/FINDSITE-COMB/index.html
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