Potential "biopeptidal" therapeutics for severe respiratory syndrome coronaviruses: a review of antiviral peptides, viral mechanisms, and prospective needs

TP-LMMSG: a peptide prediction graph neural network incorporating flexible amino acid property representation

Bioactive peptide therapeutics has been a long-standing research topic. Notably, the antimicrobial peptides (AMPs) have been extensively studied for its therapeutic potential. Meanwhile, the demand for annotating other therapeutic peptides, such as antiviral peptides (AVPs) and anticancer peptides (ACPs), also witnessed an increase in recent years. However, we conceive that the structure of peptide chains and the intrinsic information between the amino acids is not fully investigated among the existing protocols. Therefore, we develop a new graph deep learning model, namely TP-LMMSG, which offers lightweight and easy-to-deploy advantages while improving the annotation performance in a generalizable manner. The results indicate that our model can accurately predict the properties of different peptides. The model surpasses the other state-of-the-art models on AMP, AVP and ACP prediction across multiple experimental validated datasets. Moreover, TP-LMMSG also addresses the challenges of time-consuming pre-processing in graph neural network frameworks. With its flexibility in integrating heterogeneous peptide features, our model can provide substantial impacts on the screening and discovery of therapeutic peptides. The source code is available at https://github.com/NanjunChen37/TP_LMMSG.

Physicochemical and sequence determinants of antiviral peptides

Antiviral peptides (AVPs) open new possibilities as an effective antiviral therapeutic in the current scenario of evolving drug-resistant viruses. Knowledge about the sequence and structure activity relationship in AVPs is still largely unknown. AVPs and antimicrobial peptides (AMPs) share several common features but as they target different life forms (living organisms and viruses), exploring the differential sequence features may facilitate in designing specific AVPs. The current work developed accurate prediction models for discriminating (a) AVPs from AMPs, (b) Coronaviridae AVPs from other virus family specific AVPs and (c) highly active AVPs (HAA) from lowly active AVPs (LAA). Further explainable machine learning methods (using model agnostic global interpretable methods) are utilized for exploring and interpreting the physicochemical spaces of AVPs, Coronaviridae AVPs and highly active AVPs. To further understand the association of physicochemical space distribution with pIC50 values, regression models were developed and analyzed using accumulated local effects and interaction strength analysis. An independent sample t-test is used to filter out the significant compositional differences between the smaller length HAA and longer length HAA groups. AVPs prefer lower charge/length ratio and basic residues in comparison with AMPs. Coronaviridae family-specific AVPs have lower propensities for basic amino acids, charge and preference for aspartic acid. Further there is prevalence for basic residues in lowly active AVPs as compared to highly active AVPs. Sequence order effects captured in terms of average amino acid pair distances proved to be more constructive in deciphering the sequences of AVPs.

Protegrin-2, a potential inhibitor for targeting SARS-CoV-2 main protease Mpro

Background

SARS-CoV-2 variants continue to spread throughout the world and cause waves of COVID-19 infections. It is important to find effective antiviral drugs to combat SARS-CoV-2 and its variants. The main protease (Mpro) of SARS-CoV-2 is a promising therapeutic target due to its crucial role in viral replication and its conservation in all the variants. Therefore, the aim of this work was to identify an effective inhibitor of Mpro.

Methods

We studied around 200 antimicrobial peptides using in silico methods including molecular docking and allergenicity and toxicity prediction. One selected antiviral peptide was studied experimentally using a Bioluminescence Resonance Energy Transfer (BRET)-based Mpro biosensor, which reports Mpro activity through a decrease in energy transfer.

Results

Molecular docking identified one natural antimicrobial peptide, Protegrin-2, with high binding affinity and stable interactions with Mpro allosteric residues. Furthermore, free energy calculations and molecular dynamics simulation illustrated a high affinity interaction between the two. We also determined the impact of the binding of Protegrin-2 to Mpro using a BRET-based assay, showing that it inhibits the proteolytic cleavage activity of Mpro.

Conclusions

Our in silico and experimental studies identified Protegrin-2 as a potent inhibitor of Mpro that could be pursued further towards drug development against COVID-19 infection.

Exploring bioactive peptides as potential therapeutic and biotechnology treasures: A contemporary perspective

In preceding years, bioactive peptides (BAPs) have piqued escalating attention owing to their multitudinous biological features. To date, many potential BAPs exhibiting anti-cancer activities have been documented; yet, obstacles such as their safety profiles and consumer acceptance continue to exist. Moreover, BAPs have been discovered to facilitate the suppression of Coronavirus Disease 2019 (CoVID-19) and maybe ideal for treating the CoVID-19 infection, as stated by published experimental findings, but their widespread knowledge is scarce. Likewise, there is a cornucopia of BAPs possessing neuroprotective effects that mend neurodegenerative diseases (NDs) by regulating gut microbiota, but they remain a subject of research interest. Additionally, a plethora of researchers have attempted next-generation approaches based on BAPs, but they need scientific attention. The text format of this critical review is organized around an overview of BAPs' versatility and diverse bio functionalities with emphasis on recent developments and novelties. The review is alienated into independent sections, which are related to either BAPs based disease management strategies or next-generation BAPs based approaches. BAPs based anti-cancer, anti-CoVID-19, and neuroprotective strategies have been explored, which may offer insights that could help the researchers and industries to find an alternate regimen against the three aforementioned fatal diseases. To the best of our knowledge, this is the first review that has systematically discussed the next-generation approaches in BAP research. Furthermore, it can be concluded that the BAPs may be optimal for the management of cancer, CoVID-19, and NDs; nevertheless, experimental and preclinical studies are crucial to validate their therapeutic benefits.

Theta-Defensins to Counter COVID-19 as Furin Inhibitors: In Silico Efficiency Prediction and Novel Compound Design

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was characterized as a pandemic by the World Health Organization (WHO) in Dec. 2019. SARS-CoV-2 binds to the cell membrane through spike proteins on its surface and infects the cell. Furin, a host-cell enzyme, possesses a binding site for the spike protein. Thus, molecules that block furin could potentially be a therapeutic solution. Defensins are antimicrobial peptides that can hypothetically inhibit furin because of their arginine-rich structure. Theta-defensins, a subclass of defensins, have attracted attention as drug candidates due to their small size, unique structure, and involvement in several defense mechanisms. Theta-defensins could be a potential treatment for COVID-19 through furin inhibition and an anti-inflammatory mechanism. Note that inflammatory events are a significant and deadly condition that could happen at the later stages of COVID-19 infection. Here, the potential of theta-defensins against SARS-CoV-2 infection was investigated through in silico approaches. Based on docking analysis results, theta-defensins can function as furin inhibitors. Additionally, a novel candidate peptide against COVID-19 with optimal properties regarding antigenicity, stability, electrostatic potential, and binding strength was proposed. Further in vitro/in vivo investigations could verify the efficiency of the designed novel peptide.