Molecular characteristics, immune evasion, and impact of SARS-CoV-2 variants

The development of inhibitors that target the papain‐like protease (PLpro) has the potential to counteract the spread of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), the agent causing coronavirus disease 2019 (COVID‐19). Based on a consideration of its several downstream effects, interfering with PLpro would both revert immune suppression exerted by the virus and inhibit viral replication. By following a repurposing strategy, the current study evaluates the potential of antimalarial drugs as PLpro inhibitors, and thereby the possibility of their use for treatment of SARS‐CoV‐2 infection. Computational tools were employed for structural analysis, molecular docking, and molecular dynamics simulations to screen antimalarial drugs against PLpro, and in silico data were validated by in vitro experiments. Virtual screening highlighted amodiaquine and methylene blue as the best candidates, and these findings were complemented by the in vitro results that indicated amodiaquine as a μM PLpro deubiquitinase inhibitor. The results of this study demonstrate that the computational workflow adopted here can correctly identify active compounds. Thus, the highlighted antimalarial drugs represent a starting point for the development of new PLpro inhibitors through structural optimization.

Combining computational and experimental evidence on the activity of antimalarial drugs on papain‐like protease of SARS‐CoV‐2: A repurposing study

Abstract Due to the ongoing global warming, maize production worldwide is expected to be heavily inflicted by droughts. The grain yield of maize hybrids is an important factor in evaluating their suitability and stability. In this study, we utilized the AMMI model and GGE biplot to analyze grain yield of 20 hybrids from the three tested environments in Inner Mongolia in 2018 and 2019, aiming at selecting drought-tolerant maize hybrids. AMMI variance analysis revealed highly significant difference on main effects for genotype, environment, and their interaction. Furthermore, G11 (DK159) and G15 (JKY3308) exhibited favorable productivity and stability across all three test environments. Moreover, G10 (LH1) emerged as the most stable hybrid according to the AMMI analysis and the GGE biplot. Bayannur demonstrated the highest identification ability among the three tested sites. Our study provides accurate identification for drought-resilient maize hybrids in different rain-fed regions. These findings can contribute to the selection of appropriate hybrids that exhibit productivity, stability, and adaptability in drought-prone conditions. 

AMMI an GGE biplot analysis of grain yield for drought-tolerant maize hybrid selection in Inner Mongolia

/pdf/antiviral-activity-of-curcumin-and-its-analogs-selected-by-1x53gyp1.pdf

Antiviral activity of curcumin and its analogs selected by an artificial intelligence-supported activity prediction system in SARS-CoV-2-infected VeroE6 cells

Curcumin has been reported to exert its anti-SARS-CoV-2 activity by inhibiting the binding of spike receptor-binding domain (RBD) to angiotensin-converting enzyme-2 (ACE2). To identify more potent compounds, we evaluated the antiviral activities of curcumin and its analogs in SARS-CoV-2-infected cells. An artificial intelligence-supported activity prediction system was used to select the compounds, and 116 of the 334 curcumin analogs were proposed to have spike RBD-ACE2 binding inhibitory activity. These compounds were narrowed down to eight compounds for confirmatory studies. Six out of the eight compounds showed antiviral activity with EC50 values of less than 30 µM and binding inhibitory activity with IC20 values of less than 30 µM. Structure-activity relationship analyses revealed that the double bonds in the carbon chain connecting the two phenolic groups were essential for both activities. X-ray co-crystallography studies are needed to clarify the true binding pose and design more potent derivatives.

Antiviral activity of curcumin and its analogs selected by an artificial intelligence-supported activity prediction system in SARS-CoV-2-infected VeroE6 cells.

/pdf/exploring-sars-cov-2-delta-variant-spike-protein-receptor-2dcu4gpr.pdf

Exploring SARS-CoV-2 Delta variant spike protein receptor-binding domain (RBD) as a target for tanshinones and antimalarials

Genotype by environment interaction for grain yield in foxtail millet (Setarai italica) using AMMI model and GGE Biplot

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Exploring SARS-CoV-2 Delta variant spike protein receptor-binding domain (RBD) as a target for tanshinones and antimalarials

Genotype by environment interaction for grain yield in foxtail millet (Setarai italica) using AMMI model and GGE Biplot