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Objectives The skin commensal and opportunistic pathogen Staphylococcus epidermidis is one of the leading causes of nosocomial and biofilm-associated infections, which urgently requires discovery of new antibiotics. We decided to find new leads that target the S. epidermidis tryptophanyl-tRNA synthetase (SeWRS), which is essential for translation. Methods We applied an approach combining structure-based discovery in silico with biochemical and biological experiments in vitro to screen SeWRS inhibitors. Results Three compounds have an inhibitory effect on enzymatic activities of SeWRS, of which two show low inhibition of the human tryptophanyl-tRNA synthetase. Binding of these compounds to bacterially expressed SeWRS was demonstrated by surface plasmon resonance technology. These three compounds can also obviously inhibit growth of S. epidermidis in vitro and displayed low cytotoxicity to mammalian cells. Conclusions These compounds are good leads to develop new antibiotics. [ABSTRACT FROM AUTHOR]

Shikimate dehydrogenase (SDH) is the fourth enzyme involved in the shikimate pathway. It catalyzes the NADPH-dependent reduction of 3-dehydroshikimate to shikimate, and has been developed as a promising target for the discovery of antimicrobial agent. In this report, we identified a new aroE gene encoding SDH from Helicobacter pylori strain SS1. The recombinant H. pylori shikimate dehydrogenase (HpSDH) was cloned, expressed, and purified in Escherichia coli system. The enzymatic characterization of HpSDH demonstrates its activity with kcat of 7.7 s−1 and Km of 0.148 mm toward shikimate, kcat of 7.1 s−1 and Km of 0.182 mm toward NADP, kcat of 5.2 s−1 and Km of 2.9 mm toward NAD. The optimum pH of the enzyme activity is between 8.0 and 9.0, and the optimum temperature is around 60 °C. Using high throughput screening against our laboratory chemical library, five compounds, curcumin ( 1), 3-(2-naphthyloxy)-4-oxo-2-(trifluoromethyl)-4 H-chromen-7-yl 3-chlorobenzoate ( 2), butyl 2-{[3-(2-naphthyloxy)-4-oxo-2-(trifluoromethyl)-4 H-chromen-7-yl]oxy}propanoate ( 3), 2-({2-[(2-{[2-(2,3-dimethylanilino)-2-oxoethyl]sulfanyl}-1,3-benzothiazol-6-yl)amino]-2-oxoethyl}sulfanyl)- N-(2-naphthyl)acetamide ( 4), and maesaquinone diacetate ( 5) were discovered as HpSDH inhibitors with IC50 values of 15.4, 3.9, 13.4, 2.9, and 3.5 µm, respectively. Further investigation indicates that compounds 1, 2, 3, and 5 demonstrate noncompetitive inhibition pattern, and compound 4 displays competitive inhibition pattern with respect to shikimate. Compounds 1, 4, and 5 display noncompetitive inhibition mode, and compounds 2 and 3 show competitive inhibition mode with respect to NADP. Antibacterial assays demonstrate that compounds 1, 2, and 5 can inhibit the growth of H. pylori with MIC of 16, 16, and 32 µg·mL−1, respectively. This current work is expected to favor better understanding the features of SDH and provide useful information for the development of novel antibiotics to treat H. pylori-associated infection. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global crisis. There is no therapeutic treatment specific for COVID-19. It is highly desirable to identify potential antiviral agents against SARS-CoV-2 from existing drugs available for other diseases and thus repurpose them for treatment of COVID-19. In general, a drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a virtual screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. Here we report a virtual screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions and its use in identifying drugs targeting SARS-CoV-2 main protease (Mpro). The accurate FEP-ABFE predictions were based on the use of a restraint energy distribution (RED) function, making the practical FEP-ABFE-based virtual screening of the existing drug library possible. As a result, out of 25 drugs predicted, 15 were confirmed as potent inhibitors of SARS-CoV-2 Mpro. The most potent one is dipyridamole (inhibitory constant Ki = 0.04 µM)which has shown promising therapeutic effects in subsequently conducted clinical studies for treatment of patients with COVID-19. Additionally, hydroxychloroquine (Ki = 0.36 µM) and chloroquine (Ki = 0.56 µM) were also found to potently inhibit SARS-CoV-2 Mpro. We anticipate that the FEP-ABFE prediction-based virtual screening approach will be useful in many other drug repurposing or discovery efforts. [ABSTRACT FROM AUTHOR]

In recent decades, high-performance computing (HPC) technologies and supercomputers in China have significantly advanced, resulting in remarkable achievements. Computational drug discovery and design, which is based on HPC and combines pharmaceutical chemistry and computational biology, has become a critical approach in drug research and development and is financially supported by the Chinese government. This approach has yielded a series of new algorithms in drug design, as well as new software and databases. This review mainly focuses on the application of HPC to the fields of drug discovery and molecular simulation at the Chinese Academy of Sciences, including virtual drug screening, molecular dynamics simulation, and protein folding. In addition, the potential future application of HPC in precision medicine is briefly discussed. [ABSTRACT FROM AUTHOR]

Protein-protein interactions (PPIs) are central to most biological processes. Although efforts have been devoted to the development of methodology for predicting PPIs and protein interaction networks, the application of most existing methods is limited because they need information about protein homology or the interaction marks of the protein partners. In the present work, we propose a method for PPI prediction using only the information of protein sequences. This method was developed based on a learning algorithm-support vector machine combined with a kernel function and a conjoint triad feature for describing amino acids. More than 16,000 diverse PPI pairs were used to construct the universal model. The prediction ability of our approach is better than that of other sequence-based PPI prediction methods because it is able to predict PPI networks. Different types of PPI networks have been effectively mapped with our method, suggesting that, even with only sequence information, this method could be applied to the exploration of networks for any newly discovered protein with unknown biological relativity. In addition, such supplementary experimental information can enhance the prediction ability of the method. [ABSTRACT FROM AUTHOR]