Potential Novel Thioether-Amide or Guanidine-Linker Class of SARS-CoV-2 Virus RNA-Dependent RNA Polymerase Inhibitors Identified by High-Throughput Virtual Screening Coupled to Free-Energy Calculations

De Novo Potent Peptide Nucleic Acid Antisense Oligomer Inhibitors Targeting SARS-CoV-2 RNA-Dependent RNA Polymerase via Structure-Guided Drug Design

Global reports of novel SARS-CoV-2 variants and recurrence cases continue despite substantial vaccination campaigns, raising severe concerns about COVID-19. While repurposed drugs offer some treatment options for COVID-19, notably, nucleoside inhibitors like Remdesivir stand out as curative therapies for COVID-19 that are approved by the US Food and Drug Administration (FDA). The emergence of highly contagious SARS-CoV-2 variants underscores the imperative for antiviral drugs adaptable to evolving viral mutations. RNA-dependent RNA polymerase (RdRp) plays a key role in viral genome replication. Currently, inhibiting viral RdRp function remains a pivotal strategy to tackle the notorious virus. Peptide nucleic acid (PNA) therapy shows promise by effectively targeting specific genome regions, reducing viral replication, and inhibiting infection. In our study, we designed PNA antisense oligomers conjugated with cell-penetrating peptides (CPP) aiming to evaluate their antiviral effects against RdRp target using structure-guided drug design, which involves molecular docking simulations, drug likeliness and pharmacokinetic evaluations, molecular dynamics simulations, and computing binding free energy. The in silico analysis predicts that chemically modified PNAs might act as antisense molecules in order to disrupt ribosome assembly at RdRp's translation start site, and their chemically stable and neutral backbone might enhance sequence-specific RNA binding interaction. Notably, our findings demonstrate that PNA-peptide conjugates might be the most promising inhibitors of SARS-CoV-2 RdRp, with superior binding free energy compared to Remdesivir in the current COVID-19 medication. Specifically, PNA-CPP-1 could bind simultaneously to the active site residues of RdRp protein and sequence-specific RdRp-RNA target in order to control viral replication.

The Impact of Data on Structure-Based Binding Affinity Predictions Using Deep Neural Networks

Artificial intelligence (AI) has gained significant traction in the field of drug discovery, with deep learning (DL) algorithms playing a crucial role in predicting protein-ligand binding affinities. Despite advancements in neural network architectures, system representation, and training techniques, the performance of DL affinity prediction has reached a plateau, prompting the question of whether it is truly solved or if the current performance is overly optimistic and reliant on biased, easily predictable data. Like other DL-related problems, this issue seems to stem from the training and test sets used when building the models. In this work, we investigate the impact of several parameters related to the input data on the performance of neural network affinity prediction models. Notably, we identify the size of the binding pocket as a critical factor influencing the performance of our statistical models; furthermore, it is more important to train a model with as much data as possible than to restrict the training to only high-quality datasets. Finally, we also confirm the bias in the typically used current test sets. Therefore, several types of evaluation and benchmarking are required to understand models' decision-making processes and accurately compare the performance of models.

Autochthonous Peruvian Natural Plants as Potential SARS-CoV-2 Mpro Main Protease Inhibitors

Over 750 million cases of COVID-19, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), have been reported since the onset of the global outbreak. The need for effective treatments has spurred intensive research for therapeutic agents based on pharmaceutical repositioning or natural products. In light of prior studies asserting the bioactivity of natural compounds of the autochthonous Peruvian flora, the present study focuses on the identification SARS-CoV-2 M^pro main protease dimer inhibitors. To this end, a target-based virtual screening was performed over a representative set of Peruvian flora-derived natural compounds. The best poses obtained from the ensemble molecular docking process were selected. These structures were subjected to extensive molecular dynamics steps for the computation of binding free energies along the trajectory and evaluation of the stability of the complexes. The compounds exhibiting the best free energy behaviors were selected for in vitro testing, confirming the inhibitory activity of Hyperoside against M^pro, with a K_i value lower than 20 µM, presumably through allosteric modulation.

In silico evaluation of potential intervention against SARS-CoV-2 RNA-dependent RNA polymerase

Background

With few available effective interventions, emergence of novel mutants responding poorly to existing vaccines and ever swelling newer waves of infection, SARS-CoV-2 is posing difficult challenges to mankind. This mandates development of newer and effective therapeutics to prevent loss of life and contain the spread of this deadly virus. Nsp12 or RNA-dependent RNA polymerase (RdRp) is a suitable druggable target as it plays a central role in viral replication.

Methodology

Catalytically important conserved amino acid residues of RdRp were delineated through a comprehensive literature search and multiple sequence alignments. PDB ID 7BV2 was used to create binding pockets using SeeSAR and to generate docked poses of the FDA approved drugs on the receptor and estimating their binding affinity and other properties.

Result

In silico approach used in this study assisted in prediction of several potential RdRp inhibitors; and re-validation of the already reported ones. Five molecules namely Inosine, Ribavirin, 2-Deoxy-2-Fluoro-D-glucose, Guaifenesin, and Lamivudine were shortlisted which exhibited reasonable binding affinities, with neither torsional nor intermolecular or intramolecular clashes.

Conclusion

This study aimed to widen the prospect of interventions against the SARS-CoV-2 RdRp. Our results also re-validate already reported molecules like 2-Deoxy-D-glucose as a similar molecule 2-deoxy-2-fluoro-D-glucose is picked up in this study. Additionally, ribavirin and lamivudine, already known antivirals with polymerase inhibition activity are also picked up as the top leads. Selected potent inhibitors of RdRp hold promise to cater for any future coronavirus-outbreak subject to in vitro and in vivo validations.

Antioxidant, Alpha-Glucosidase Inhibition Activities, In Silico Molecular Docking and Pharmacokinetics Study of Phenolic Compounds from Native Australian Fruits and Spices

Native Australian fruits and spices are enriched with beneficial phytochemicals, especially phenolic compounds, which are not fully elucidated. Therefore, this study aimed to analyze native Australian mountain-pepper berries (Tasmannia lanceolata), rosella (Hibiscus sabdariffa), lemon aspen (Acronychia acidula), and strawberry gum (Eucalyptus olida) for phenolic and non-phenolic metabolites and their antioxidant and alpha-glucosidase inhibition activities. Liquid chromatography-mass spectrometry-electrospray ionization coupled with quadrupole time of flight (LC-ESI-QTOF-MS/MS) was applied to elucidate the composition, identities, and quantities of bioactive phenolic metabolites in Australian native commercial fruits and spices. This study identified 143 phenolic compounds, including 31 phenolic acids, 70 flavonoids, 10 isoflavonoids, 7 tannins, 3 stilbenes, 7 lignans, 10 other compounds, and 5 limonoids. Strawberry gum was found to have the highest total phenolic content (TPC-36.57 ± 1.34 milligram gallic acid equivalent per gram (mg GAE/g), whereas lemon aspen contained the least TPC (4.40 ± 0.38 mg GAE/g). Moreover, strawberry gum and mountain pepper berries were found to have the highest antioxidant and anti-diabetic potential. In silico molecular docking and pharmacokinetics screening were also conducted to predict the potential of the most abundant phenolic compounds in these selected plants. A positive correlation was observed between phenolic contents and biological activities. This study will encourage further research to identify the nutraceutical and phytopharmaceutical potential of these native Australian fruits.

High-Throughput Screening for the Potential Inhibitors of SARS-CoV-2 with Essential Dynamic Behavior

Global health security has been challenged by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. Due to the lengthy process of generating vaccinations, it is vital to reposition currently available drugs in order to relieve anti-epidemic tensions and accelerate the development of therapies for Coronavirus Disease 2019 (COVID-19), the public threat caused by SARS-CoV-2. High throughput screening techniques have established their roles in the evaluation of already available medications and the search for novel potential agents with desirable chemical space and more cost-effectiveness. Here, we present the architectural aspects of highthroughput screening for SARS-CoV-2 inhibitors, especially three generations of virtual screening methodologies with structural dynamics: ligand-based screening, receptor-based screening, and machine learning (ML)-based scoring functions (SFs). By outlining the benefits and drawbacks, we hope that researchers will be motivated to adopt these methods in the development of novel anti- SARS-CoV-2 agents.

Virtual Screening and Quantum Chemistry Analysis for SARS-CoV-2 RNA-Dependent RNA Polymerase Using the ChEMBL Database: Reproduction of the Remdesivir-RTP and Favipiravir-RTP Binding Modes Obtained from Cryo-EM Experiments with High Binding Affinity

The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified as the pathogenic cause of coronavirus disease 2019 (COVID-19). The RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 is a potential target for the treatment of COVID-19. An RdRp complex:dsRNA structure suitable for docking simulations was prepared using a cryo-electron microscopy (cryo-EM) structure (PDB ID: 7AAP; resolution, 2.60 Å) that was reported recently. Structural refinement was performed using energy calculations. Structure-based virtual screening was performed using the ChEMBL database. Through 1,838,257 screenings, 249 drugs (37 approved, 93 clinical, and 119 preclinical drugs) were predicted to exhibit a high binding affinity for the RdRp complex:dsRNA. Nine nucleoside triphosphate analogs with anti-viral activity were included among these hit drugs, and among them, remdesivir-ribonucleoside triphosphate and favipiravir-ribonucleoside triphosphate adopted a similar docking mode as that observed in the cryo-EM structure. Additional docking simulations for the predicted compounds with high binding affinity for the RdRp complex:dsRNA suggested that 184 bioactive compounds could be anti-SARS-CoV-2 drug candidates. The hit bioactive compounds mainly consisted of a typical noncovalent major groove binder for dsRNA. Three-layer ONIOM (MP2/6-31G:AM1:AMBER) geometry optimization calculations and frequency analyses (MP2/6-31G:AMBER) were performed to estimate the binding free energy of a representative bioactive compound obtained from the docking simulation, and the fragment molecular orbital calculation at the MP2/6-31G level of theory was subsequently performed for analyzing the detailed interactions. The procedure used in this study represents a possible strategy for discovering anti-SARS-CoV-2 drugs from drug libraries that could significantly shorten the clinical development period for drug repositioning.

Therapeutic Targeting of Innate Immune Receptors Against SARS-CoV-2 Infection

The innate immune system is the first line of host's defense against invading pathogens. Multiple cellular sensors that detect viral components can induce innate antiviral immune responses. As a result, interferons and pro-inflammatory cytokines are produced which help in the elimination of invading viruses. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) belongs to Coronaviridae family, and has a single-stranded, positive-sense RNA genome. It can infect multiple hosts; in humans, it is responsible for the novel coronavirus disease 2019 (COVID-19). Successful, timely, and appropriate detection of SARS-CoV-2 can be very important for the early generation of the immune response. Several drugs that target the innate immune receptors as well as other signaling molecules generated during the innate immune response are currently being investigated in clinical trials. In this review, we summarized the current knowledge of the mechanisms underlying host sensing and innate immune responses against SARS-CoV-2 infection, as well as the role of innate immune receptors in terms of their therapeutic potential against SARS-CoV-2. Moreover, we discussed the drugs undergoing clinical trials and the FDA approved drugs against SARS-CoV-2. This review will help in understanding the interactions between SARS-CoV-2 and innate immune receptors and thus will point towards new dimensions for the development of new therapeutics, which can be beneficial in the current pandemic.

Commercial SARS-CoV-2 Targeted, Protease Inhibitor Focused and Protein-Protein Interaction Inhibitor Focused Molecular Libraries for Virtual Screening and Drug Design

Since December 2019, the new SARS-CoV-2-related COVID-19 disease has caused a global pandemic and shut down the public life worldwide. Several proteins have emerged as potential therapeutic targets for drug development, and we sought out to review the commercially available and marketed SARS-CoV-2-targeted libraries ready for high-throughput virtual screening (HTVS). We evaluated the SARS-CoV-2-targeted, protease-inhibitor-focused and protein-protein-interaction-inhibitor-focused libraries to gain a better understanding of how these libraries were designed. The most common were ligand- and structure-based approaches, along with various filtering steps, using molecular descriptors. Often, these methods were combined to obtain the final library. We recognized the abundance of targeted libraries offered and complimented by the inclusion of analytical data; however, serious concerns had to be raised. Namely, vendors lack the information on the library design and the references to the primary literature. Few references to active compounds were also provided when using the ligand-based design and usually only protein classes or a general panel of targets were listed, along with a general reference to the methods, such as molecular docking for the structure-based design. No receptor data, docking protocols or even references to the applied molecular docking software (or other HTVS software), and no pharmacophore or filter design details were given. No detailed functional group or chemical space analyses were reported, and no specific orientation of the libraries toward the design of covalent or noncovalent inhibitors could be observed. All libraries contained pan-assay interference compounds (PAINS), rapid elimination of swill compounds (REOS) and aggregators, as well as focused on the drug-like model, with the majority of compounds possessing their molecular mass around 500 g/mol. These facts do not bode well for the use of the reviewed libraries in drug design and lend themselves to commercial drug companies to focus on and improve.