The Discovery of Novel Agents against Staphylococcus aureus by Targeting Sortase A: A Combination of Virtual Screening and Experimental Validation
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Staphylococcus aureus (S. aureus), commonly known as “superbugs”, is a highly pathogenic bacterium that poses a serious threat to human health. There is an urgent need to replace traditional antibiotics with novel drugs to combat S. aureus. Sortase A (SrtA) is a crucial transpeptidase involved in the adhesion process of S. aureus. The reduction in virulence and prevention of S. aureus infections have made it a significant target for antimicrobial drugs. In this study, we combined virtual screening with experimental validation to identify potential drug candidates from a drug library. Three hits, referred to as Naldemedine, Telmisartan, and Azilsartan, were identified based on docking binding energy and the ratio of occupied functional sites of SrtA. The stability analysis manifests that Naldemedine and Telmisartan have a higher binding affinity to the hydrophobic pockets. Specifically, Telmisartan forms stable hydrogen bonds with SrtA, resulting in the highest binding energy. Our experiments prove that the efficiency of adhesion and invasion by S. aureus can be decreased without significantly affecting bacterial growth. Our work identifies Telmisartan as the most promising candidate for inhibiting SrtA, which can help combat S. aureus infection.Keywords:
Sortase A
Telmisartan
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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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Ligand docking is a widely used approach in virtual screening. In recent years a large number of publications have appeared in which docking tools are compared and evaluated for their effectiveness in virtual screening against a wide variety of protein targets. These studies have shown that the effectiveness of docking in virtual screening is highly variable due to a large number of possible confounding factors. Another class of method that has shown promise in virtual screening is the shape-based, ligand-centric approach. Several direct comparisons of docking with the shape-based tool ROCS have been conducted using data sets from some of these recent docking publications. The results show that a shape-based, ligand-centric approach is more consistent than, and often superior to, the protein-centric approach taken by docking.
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Due to the time and cost problems with traditional drug discovery, new methods must be found to increase the declining efficiency of traditional approaches. Virtual Screening (VS) is one possible solution to solve this problem. VS of databases has become an attractive method for pharmaceutical research. It plays a crucial role in the early stage of the drug discovery and development process. It aims to reduce the enormous search space of chemical compounds. As the number of ligands in the databases is increasing rapidly, this step should be both fast and effective in order to distinguish between active and inactive ligands. Deep learning algorithms can be used for screening big databases of molecules and classifying the ligands as drug-like and non-drug-like against a particular protein target and therefore speed up the VS process. In this paper, we propose a fast compound classification method based on a deep neural network for Virtual Screening called (DNN-VS) using the Spark-H2O platform in order to label small molecules from huge databases. Experimental results have shown that the proposed approach outperforms state-of-the-art machine learning techniques with an overall accuracy more than 99%.
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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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As a new method or technology for drug discovery, virtual screening has been appreciated by research institutions and big pharmaceutical companies. Moreover, virtual screening has been involved into the pipeline of drug discovery and development as a practical tool complemented with high throughput empirical screening. In this review, we will introduce recent advances in virtual screening and its function in new drug discovery. In particular, we will discuss the research situation of virtual screening in our country.
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Recent advances in the field of computational drug design and docking approaches has made “Virtual Screening” a major thrust area for bioinformaticians or the one who are aiming at computational drug designing. Virtual screening is defined as the process of reducing a library of enormous number of compounds to a manageable number of the targets of interest. Here in the study, the target is the cardiovascular disease against which the screening was performed. Virtual screening relies in the basic methodology of receptor modeling to library generation, flexible docking and ligand scoring. Virtual screening is a widely accepted method in lead discovery because it is advantageous in the elimination of undesired molecules from the compound libraries and the reduction of cost and time in drug discovery projects. The docking of a huge molecule database against a specific target yield new candidates for further lead development which can meet very heterogeneous demands. Docking can help to correlate the experimentally determined biological activities, ligand poses, and predicted binding affinities by the docking program, to evaluate the scoring functions and to identify a good score of the target protein. The screening of the dataset available from NCI produced 16 lead candidates after docking the compounds with the angiotensin converting enzyme using the DOCK 6.2 software by generating the spheres, calculating the grid and finally performing the docking.
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