Inhibitors of the proteasome have been extensively studied for their applications in the treatment of human diseases such as hematologic malignancies, autoimmune disorders, and viral infections. Many of the proteasome inhibitors reported in the literature target the non-primed site of proteasome's substrate binding pocket. In this study, we designed, synthesized and characterized a series of novel α-keto phenylamide derivatives aimed at both the primed and non-primed sites of the proteasome. In these derivatives, different substituted phenyl groups at the head group targeting the primed site were incorporated in order to investigate their structure-activity relationship and optimize the potency of α-keto phenylamides. In addition, the biological effects of modifications at the cap moiety, P1、P2 and P3 side chain positions were explored. Many derivatives displayed highly potent biological activities in proteasome inhibition and anticancer activity against a panel of six cancer cell lines, which were further rationalized by molecular modeling analyses. Furthermore, a representative α-ketoamide derivative was tested and found to be active in inhibiting the cellular infection of SARS-CoV-2 which causes the COVID-19 pandemic. These results demonstrate that this new class of α-ketoamide derivatives are potent anticancer agents and provide experimental evidence of the anti-SARS-CoV-2 effect by one of them, thus suggesting a possible new lead to develop antiviral therapeutics for COVID-19.

G protein-coupled receptors (GPCRs) comprise the most important superfamily of protein targets in current ligand discovery and drug development. GPCRs are integral membrane proteins that play key roles in various cellular signaling processes. Therefore, GPCR signaling pathways are closely associated with numerous diseases, including cancer and several neurological, immunological, and hematological disorders. Computer-aided drug design (CADD) can expedite the process of GPCR drug discovery and potentially reduce the actual cost of research and development. Increasing knowledge of biological structures, as well as improvements on computer power and algorithms, have led to unprecedented use of CADD for the discovery of novel GPCR modulators. Similarly, machine learning approaches are now widely applied in various fields of drug target research. This review briefly summarizes the application of rising CADD methodologies, as well as novel machine learning techniques, in GPCR structural studies and bioligand discovery in the past few years. Recent novel computational strategies and feasible workflows are updated, and representative cases addressing challenging issues on olfactory receptors, biased agonism, and drug-induced cardiotoxic effects are highlighted to provide insights into future GPCR drug discovery.

Zika virus (ZIKV), one of the flaviviruses, has attracted worldwide attention since its large epidemics around Brazil. Association of ZIKV infection with microcephaly and neurological problems such as Guillain–Barré syndrome has prompted intensive pathological investigations. However, there is still a long way to go on the discovery of effective anti-ZIKV therapeutics. In this study, an in silico screening of the National Cancer Institute (NCI) diversity set based on ZIKV NS3 helicase was performed using a molecular docking approach. Selected compounds with drug-like properties were subjected to cell-based antiviral assays resulting in the identification of two novel lead compounds (named Compounds 1 and 2). They inhibited ZIKV infection with IC50 values at the micro-molar level (8.5 μM and 15.2 μM, respectively). Binding mode analysis, absolute binding free energy calculation, and structure–activity relationship studies of these two compounds revealed their possible interactions with ZIKV NS3 helicase, suggesting a mechanistic basis for further optimization. These two novel small molecules may represent new leads for the development of inhibitory drugs against ZIKV.

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