Due to the unavailability specific drugs or vaccines (FDA approved) that can cure COVID-19, the development of potent antiviral drug candidates/therapeutic molecules against COVID-19 is urgently required. This study was aimed at in silico screening and study of polyphenolic phytochemical compounds in a rational way by virtual screening, molecular docking and molecular dynamics studies against SARS-CoV-2 main protease (Mpro) and papain-like protease (PLpro) enzymes. The objective of the study was to identify plant-derived polyphenolic compounds and/or flavonoid molecules as possible antiviral agents with protease inhibitory potential against SARS-CoV-2. In this study, we report plant-derived polyphenolic compounds (including flavonoids) as novel protease inhibitors against SARS-CoV-2. From virtual docking and molecular docking study, 31 polyphenolic compounds were identified as active antiviral molecules possessing well-defined binding affinity with acceptable ADMET, toxicity and lead-like or drug-like properties. Six polyphenolic compounds, namely, enterodiol, taxifolin, eriodictyol, leucopelargonidin, morin and myricetin were found to exhibit remarkable binding affinities against the proteases with taxifolin and morin exhibiting the highest binding affinity toward Mpro and PLpro respectively. Molecular dynamics simulation studies of these compounds in complex with the proteases showed that the binding of the compounds is characterized by structural perturbations of the proteases suggesting their antiviral activities. These compounds can therefore be investigated further by in vivo and in vitro techniques to assess their potential efficacy against SARS-CoV-2 and thus serve as the starting point for the development of potent antiviral agents against the deadly COVID-19. Communicated by Ramaswamy H. Sarma

Molecular modelling is a vital tool in the discovery and characterisation of bioactive peptides, providing insights into their structural properties and interactions with biological targets. Many models predicting bioactive peptide function or structure rely on their intrinsic properties, including the influence of amino acid composition, sequence, and chain length, which impact stability, folding, aggregation, and target interaction. Homology modelling predicts peptide structures based on known templates. Peptide–protein interactions can be explored using molecular docking techniques, but there are challenges related to the inherent flexibility of peptides, which can be addressed by more computationally intensive approaches that consider their movement over time, called molecular dynamics (MD). Virtual screening of many peptides, usually against a single target, enables rapid identification of potential bioactive peptides from large libraries, typically using docking approaches. The integration of artificial intelligence (AI) has transformed peptide discovery by leveraging large amounts of data. AlphaFold is a general protein structure prediction tool based on deep learning that has greatly improved the predictions of peptide conformations and interactions, in addition to providing estimates of model accuracy at each residue which greatly guide interpretation. Peptide function and structure prediction are being further enhanced using Protein Language Models (PLMs), which are large deep-learning-derived statistical models that learn computer representations useful to identify fundamental patterns of proteins. Recent methodological developments are discussed in the context of canonical peptides, as well as those with modifications and cyclisations. In designing potential peptide therapeutics, the main outstanding challenge for these methods is the incorporation of diverse non-canonical amino acids and cyclisations.

As a member of the Orthomyxoviridae family of viruses, influenza viruses (IVs) are known causative agents of respiratory infection in vertebrates. They remain a major global threat responsible for the most virulent diseases and global pandemics in humans. The virulence of IVs and the consequential high morbidity and mortality of IV infections are primarily attributed to the high mutation rates in the IVs’ genome coupled with the numerous genomic segments, which give rise to antiviral resistant and vaccine evading strains. Current therapeutic options include vaccines and small molecule inhibitors, which therapeutically target various catalytic processes in IVs. However, the periodic emergence of new IV strains necessitates the continuous development of novel anti-influenza therapeutic options. The crux of this review highlights the recent studies on the biology of influenza viruses, focusing on the structure, function, and mechanism of action of the M2 channel and neuraminidase as therapeutic targets. We further provide an update on the development of new M2 channel and neuraminidase inhibitors as an alternative to existing anti-influenza therapy. We conclude by highlighting therapeutic strategies that could be explored further towards the design of novel anti-influenza inhibitors with the ability to inhibit resistant strains.

The coronavirus is a group of viruses found in animals as well as humans and have been detected since the 1960s. However, a newly identified form, SARS-CoV-2, has triggered a recent pandemic of respiratory disease now called COVID-19. There is currently no specific antiviral drug for the treatment of this pandemic, with most treatment strategies focused on symptomatic management and supportive therapy. As such, several drug discovery efforts are ongoing for potent treatment agents, with medicinal plants gradually gaining prominence. Approximately 80% of the South African population use traditional medicines to meet their primary health care needs. The current study aimed to identify potential COVID-19 therapeutic agents from a list of 29 bioactive compounds isolated from commonly used South African medicinal plants using molecular docking and molecular dynamics. Molecular docking identified arabic acid from Acacia senegal and L-canavanine found in Sutherlandia frutescens as a potential inhibitor of SARS-CoV-2 3C-like main protease. Similarly, hypoxoside isolated from Hypoxis hemerocallidea and uzarin from Xysmalobium undulatum, were identified as a potential inhibitor of SARS-CoV-2 receptor binding domain and SARS-CoV-2 RNA-dependent polymerase. These four bioactive compounds exhibited favourable binding orientations characterized by strong molecular interactions within respective inhibitors binding pockets of the target enzymes. Molecular dynamics simulations revealed that the binding of the identified inhibitors are characterized by structural perturbations which favour the inhibitory potency of these bioactive compounds. Additionally, in silico pharmacokinetic assessment of the compounds demonstrated favourable anti-SARS-CoV-2 properties. Although not conclusive, further experimental exploration of these compounds could serve as a starting point for the discovery of novel SARS-CoV-2 therapeutic.