The SARS CoV-2 pandemic has affected millions of people around the globe. Despite many efforts to find some effective medicines against SARS CoV-2, no established therapeutics are available yet. The ...use of phytochemicals as antiviral agents provides hope against the proliferation of SARS-CoV-2. Several natural compounds were analyzed by virtual screening against six SARS CoV-2 protein targets using molecular docking simulations in the present study. More than a hundred plant-derived secondary metabolites have been docked, including alkaloids, flavonoids, coumarins, and steroids. SARS CoV-2 protein targets include Main protease (MPro), Papain-like protease (PLpro), RNA-dependent RNA polymerase (RdRp), Spike glycoprotein (S), Helicase (Nsp13), and E-Channel protein. Phytochemicals were evaluated by molecular docking, and MD simulations were performed using the YASARA structure using a modified genetic algorithm and AMBER03 force field. Binding energies and dissociation constants allowed the identification of potentially active compounds. Ligand-protein interactions provide an insight into the mechanism and potential of identified compounds. Glycyrrhizin and its metabolite 18-β-glycyrrhetinic acid have shown a strong binding affinity for MPro, helicase, RdRp, spike, and E-channel proteins, while a flavonoid Baicalin also strongly binds against PLpro and RdRp. The use of identified phytochemicals may help to speed up the drug development and provide natural protection against SARS-CoV-2.
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•We propose a multiple grid arrangement method for protein–ligand docking software.•It covers arbitrarily sized binding sites and enables modeling of ligand binding to proteins with ...unknown binding sites.•The correct rate of re-docking with the Astex dataset improved from 27.1% to 34.1%.•The script for Schrödinger Glide is available at http://www.bi.cs.titech.ac.jp/mga_glide/.
The identification of comprehensive drug–target interactions is important in drug discovery. Although numerous computational methods have been developed over the years, a gold standard technique has not been established. Computational ligand docking and structure-based drug design allow researchers to predict the binding affinity between a compound and a target protein, and thus, they are often used to virtually screen compound libraries. In addition, docking techniques have also been applied to the virtual screening of target proteins (inverse docking) to predict target proteins of a drug candidate. Nevertheless, a more accurate docking method is currently required. In this study, we proposed a method in which a predicted ligand-binding site is covered by multiple grids, termed multiple grid arrangement. Notably, multiple grid arrangement facilitates the conformational search for a grid-based ligand docking software and can be applied to the state-of-the-art commercial docking software Glide (Schrödinger, LLC). We validated the proposed method by re-docking with the Astex diverse benchmark dataset and blind binding site situations, which improved the correct prediction rate of the top scoring docking pose from 27.1% to 34.1%; however, only a slight improvement in target prediction accuracy was observed with inverse docking scenarios. These findings highlight the limitations and challenges of current scoring functions and the need for more accurate docking methods. The proposed multiple grid arrangement method was implemented in Glide by modifying a cross-docking script for Glide, xglide.py. The script of our method is freely available online at http://www.bi.cs.titech.ac.jp/mga_glide/.
The utilization of inverse docking methods for target identification has been driven by an increasing demand for efficient tools for detecting potential drug side‐effects. Despite impressive ...achievements in the field of inverse docking, identifying true positives from a pool of potential targets still remains challenging. Notably, most of the developed techniques have low accuracies, limit the pool of possible targets that can be investigated or are not easy to use for non‐experts due to a lack of available scripts or webserver. Guided by our finding that the absolute docking score was a poor indication of a ligand's protein target, we developed a novel “combined Z‐score” method that used a weighted fraction of ligand and receptor‐based Z‐scores to identify the most likely binding target of a ligand. With our combined Z‐score method, an additional 14%, 3.6%, and 6.3% of all ligand–protein pairs of the Astex, DUD, and DUD‐E databases, respectively, were correctly predicted compared to a docking score‐based selection. The combined Z‐score had the highest area under the curve in a ROC curve analysis of all three datasets and the enrichment factor for the top 1% predictions using the combined Z‐score analysis was the highest for the Astex and DUD‐E datasets. Additionally, we developed a user‐friendly python script (compatible with both Python2 and Python3) that enables users to employ the combined Z‐score analysis for target identification using a user‐defined list of ligands and targets. We are providing this python script and a user tutorial as part of the supplemental information.
Guided by our finding that the absolute docking score was a poor indication of a ligand's protein target, we developed a novel “combined Z‐score” method that successfully improved the identification of the most likely binding target of a ligand. We are also providing a user‐friendly python script that enables non‐expert users to employ the combined Z‐score analysis for target identification using a user‐defined list of ligands and targets.
Binding MOAD (Mother Of All Databases) Hu, Liegi; Benson, Mark L.; Smith, Richard D. ...
Proteins, structure, function, and bioinformatics,
15 August 2005, Volume:
60, Issue:
3
Journal Article
Based on compelling experimental and clinical evidence,
L. exerts a beneficial effect in ameliorating mild to moderate dementia in patients with Alzheimer's disease (AD) and other neurological ...disorders, although the pharmacological mechanisms remain unknown. In the present study, compounds, their putative target proteins identified using an inverse docking approach, and clinically tested AD-related target proteins were systematically integrated together with applicable bioinformatics methods in
. The results suggested that the beneficial effects of
on AD may be contributed by the regulation of hormone sensitivity, improvements in endocrine homeostasis, maintenance of endothelial microvascular integrity, and proteolysis of tau proteins, particularly prior to amyloid β-protein (Aβ) plaque formation. Moreover, we identified six putative protein targets that are significantly related to AD, but have not been researched or have had only preliminary studies conducted on the anti-AD effects of
. These mechanisms and protein targets are very significant for future scientific research. In addition, the existing mechanisms were also verified, such as the reduction of oxidative stress, anti-apoptotic effects, and protective effects against amyloidogenesis and Aβ aggregation. The discoveries summarized here may provide a macroscopic perspective that will improve our understanding of the molecular mechanism of medicinal plants or dietary supplements, as well as new clues for the future development of therapeutic strategies for AD.
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•A series of flavonol derivatives of fisetin were synthesized, characterized, and evaluated as potential anti-skin-cancer agents.•Several of these compounds showed micromolar range in ...vitro anti-proliferative activity in melanoma (A375, SK-MEL-28) and non-melanoma (A431 and UWBCC-1) skin cancer cells.•Molecular docking and in-vitro enzymatic kinase assays identified CDK2, c-KIT, and mTOR inhibitions, with potencies as single, dual or multi-target inhibitors.•Analogs, F20, F9 and F17 significantly induced apoptosis and inhibited clonogenicity, wound healing and downregulated cancer-related molecular targets CDK2, c-Kit and effectors including mTOR, S6K, p90RSK, Stat3, and MAPK (ERK1/2).
Due to hurdles, including resistance, adverse effects, and poor bioavailability, among others linked with existing therapies, there is an urgent unmet need to devise new, safe, and more effective treatment modalities for skin cancers. Herein, a series of flavonol-based derivatives of fisetin, a plant-based flavonoid identified as an anti-tumorigenic agent targeting the mammalian targets of rapamycin (mTOR)-regulated pathways, were synthesized and fully characterized. New potential inhibitors of receptor tyrosine kinases (c-KITs), cyclin-dependent kinase-2 (CDK2), and mTOR, representing attractive therapeutic targets for melanoma and non-melanoma skin cancers (NMSCs) treatment, were identified using inverse-docking, in vitro kinase activity and various cell-based anticancer screening assays. Eleven compounds exhibited significant inhibitory activities greater than the parent molecule against four human skin cancer cell lines, including melanoma (A375 and SK-Mel-28) and NMSCs (A431 and UWBCC1), with IC50 values ranging from 0.12 to < 15 μM. Seven compounds were identified as potentially potent single, dual or multi-kinase c-KITs, CDK2, and mTOR kinase inhibitors after inverse-docking and screening against twelve known cancer targets, followed by kinase activity profiling. Moreover, the potent compound F20, and the multi-kinase F9 and F17 targeted compounds, markedly decreased scratch wound closure, colony formation, and heightened expression levels of key cancer-promoting pathway molecular targets c-Kit, CDK2, and mTOR. In addition, these compounds downregulated Bcl-2 levels and upregulated Bax and cleaved caspase-3/7/8 and PARP levels, thus inducing apoptosis of A375 and A431 cells in a dose-dependent manner. Overall, compounds F20, F9 and F17, were identified as promising c-Kit, CDK2 and mTOR inhibitors, worthy of further investigation as therapeutics, or as adjuvants to standard therapies for the control of melanoma and NMSCs.
Probing the surface of proteins to predict the binding site and binding affinity for a given small molecule is a critical but challenging task in drug discovery. Blind docking addresses this issue by ...performing docking on binding regions randomly sampled from the entire protein surface. However, compared with local docking, blind docking is less accurate and reliable because the docking space is too largetly sampled. Cavity detection-guided blind docking methods improved the accuracy by using cavity detection (also known as binding site detection) tools to guide the docking procedure. However, it is worth noting that the performance of these methods heavily relies on the quality of the cavity detection tool. This constraint, namely the dependence on a single cavity detection tool, significantly impacts the overall performance of cavity detection-guided methods. To overcome this limitation, we proposed
Co
nsensus
B
lind
Dock
(CoBDock), a novel blind, parallel docking method that uses machine learning algorithms to integrate docking and cavity detection results to improve not only binding site identification but also pose prediction accuracy. Our experiments on several datasets, including PDBBind 2020, ADS, MTi, DUD-E, and CASF-2016, showed that CoBDock has better binding site and binding mode performance than other state-of-the-art cavity detector tools and blind docking methods.
Toxoplasmosis is a worldwide parasitosis that is generally benign. The infestation may pose a risk to immunocompromized patients and to fetuses when pregnant women have recently seroconverted. ...Current treatments have numerous side effects and chemoresistance is emerging, hence the need to find new anti-Toxoplasma gondii substances. This study focuses on the antiparasitic potential of lupane-type pentacyclic triterpenes isolated from the bark of black alder (Alnus glutinosa), as well as the hypothesis of their macromolecular target by an original method of reverse docking. Among the isolated triterpenes, betulone was the most active compound with an IC
of 2.7 ± 1.2 μM, a CC
greater than 80 μM, and a selectivity index of over 29.6. An additional study of the anti-T. gondii potential of commercially available compounds (betulonic acid methyl ester and betulonic acid) showed the important role of the C3 ketone function and the C28 oxidation level on the lupane-type triterpene in the antiparasitic activity since their IC
and CC
were similar to that of betulone. Finally, the most active compounds were subjected to the AMIDE reverse docking workflow. A dataset of 87 T. gondii proteins from the Protein Data Bank was created. It identified calcium-dependent protein kinase CDPK3 as the most likely target of betulin derivatives.
Target prediction is a crucial step in modern drug discovery. However, existing experimental approaches to target prediction are time-consuming and costly. Here, we introduce LigTMap, an online ...server with a fully automated workflow that can identify protein targets of chemical compounds among 17 classes of therapeutic proteins extracted from the PDBbind database. It combines ligand similarity search with docking and binding similarity analysis to predict putative targets. In the validation experiment of 1251 compounds, targets were successfully predicted for more than 70% of the compounds within the top-10 list. The performance of LigTMap is comparable to the current best servers SwissTargetPrediction and SEA. When testing with our newly compiled compounds from recent literature, we get improved top 10 success rate (66% ours vs. 60% SwissTargetPrediction and 64% SEA) and similar top 1 success rate (45% ours vs. 51% SwissTargetPrediction and 41% SEA). LigTMap directly provides ligand docking structures in PDB format, so that the results are ready for further structural studies in computer-aided drug design and drug repurposing projects. The LigTMap web server is freely accessible at
https://cbbio.online/LigTMap
. The source code is released on GitHub (
https://github.com/ShirleyWISiu/LigTMap
) under the BSD 3-Clause License to encourage re-use and further developments.
Nature was and still is a prolific source of inspiration in pharmacy, cosmetics, and agro-food industries for the discovery of bioactive products. Informatics is now present in most human activities. ...Research in natural products is no exception. In silico tools may help in numerous cases when studying natural substances: in pharmacognosy, to store and structure the large and increasing number of data, and to facilitate or accelerate the analysis of natural products in regards to traditional uses of natural resources; in drug discovery, to rationally design libraries for screening natural compound mimetics and identification of biological activities for natural products. Here we review different aspects of in silico approaches applied to the research and development of bioactive substances and give examples of using nature-inspiring power and ultimately valorize biodiversity.
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