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.

The major obstacle to successful treatment of gastric cancer is chemotherapy resistance. Our study was designed to investigate the role of phosphoinositide 3-kinase (PI3K)/Akt pathway in the development of chemoresistance in gastric cancer. In the present study, elevated Akt expression and Akt phosphorylation (Ser 473), as well as decreased PTEN expression were observed in 28 cases of gastric cancer tissues.

We present the results for CAPRI Round 46, the third joint CASP‐CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo‐oligomers and 6 heterocomplexes. Eight of the homo‐oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher‐order assemblies. These were more difficult to model, as their prediction mainly involved “ab‐initio” docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance “gap” was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template‐based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements.

The new coronavirus COVID-19, also known as SARS-CoV-2, has infected more than 300,000 patients and become a global health emergency due to the very high risk of spread and impact of COVID-19. There are no specific drugs or vaccines against COVID-19, thus effective antiviral agents are still urgently needed to combat this virus. Herein, the FEP (free energy perturbation)-based screening strategy is newly derived as a rapid protocol to accurately reposition potential agents against COVID-19 by targeting viral proteinase Mpro. Restrain energy distribution (RED) function was derived to optimize the alchemical pathway of FEP, which greatly accelerated the calculations and first made FEP possible in the virtual screening of the FDA-approved drugs database. As a result, fifteen out of twenty-five drugs validated in vitro exhibited considerable inhibitory potencies towards Mpro. Among them, the most potent Mpro inhibitor dipyridamole potentially inhibited NF-B signaling pathway and inflammatory responses, and has just finished the first round clinical trials. Our result demonstrated that the FEP-based screening showed remarkable advantages in prompting drug repositioning against COVID-19.

In this study, monodisperse palladium (Pd) nanoparticles on reduced graphene oxide (RGO) surfaces were successfully prepared by a "wet" and "clean" method in aqueous solution. Without any surface treatment, Pd nanoparticles are firmly attached to the RGO sheets. These RGO/Pd nanocomposites exhibited catalytic activity in hydrogen generation from the hydrolysis of ammonia borane (AB). Their hydrolysis completion time and activation energy were 12.5 min and 51 ± 1 kJ mol(-1), respectively, which were comparable to the best Pd-based catalyst reported. The TOF values (mol of H(2)× (mol of catalyst × min)(-1)) of RGO/Pd is 6.25, which appears to be one of the best catalysts reported so far. We also obtained a (11)B NMR spectrum to investigate the mechanism of this catalytic hydrolysis process. This simple and straightforward method is of significance for the facile preparation of metal nanocatalysts with high catalytic activity on proper supporting materials.

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Two Mn(II) coordination complexes with considerable difference in photochromism and chirality have been constructed from the photoactive 1-(4-carboxybenzyl)-4,4'-bipyridinium ligand upon adjusting the reaction solvents.

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