Antiviral Peptides: Identification and Validation

MSCMamba: Prediction of Antimicrobial Peptide Activity Values by Fusing Multiscale Convolution with Mamba Module

Antimicrobial peptides (AMPs) have important developmental prospects as potential candidates for novel antibiotics. Although many studies have been devoted to the identification of AMPs and the qualitative prediction of their functional activities, few methods address the quantitative prediction of their activity values. In this paper, we propose a regression model called MSCMamba, which fuses multiscale convolutional neural network with Mamba module to accurately predict the activity values of AMPs. AMPs sequences are feature-extracted by multiple encoding methods and fed into a multiscale convolutional network and a Mamba module to capture local and long-range dependent features, respectively. The model fuses these two outputs and predicts the activity values of AMPs through a linear layer. Experimental results show that MSCMamba outperforms the current state-of-the-art methods in several performance metrics, especially with an increase in R2 from 0.422 to 0.467, representing a 10.66% improvement. Additionally, we did a series of ablation experiments to verify the validity of each part of the MSCMamba model and the performance enhancement of feature diversification.This study provides a new method for activity prediction of AMPs, which is expected to accelerate the development of novel antibiotics.

Computational structure-based design of antiviral peptides as potential protein-protein interaction inhibitors of rabies virus phosphoprotein and human LC8

Rabies is a serious zoonotic disease caused by the rabies virus (RABV). Despite the successful development of vaccines and efforts made in drug discovery, rabies is incurable. Therefore, development of novel drugs is of interest to the scientific community. Antiviral peptides can be designed based on the known structures of viral proteins and their biological targets. Cytoplasmic dynein light chain LC8, one of the first identified host partners of RABV phosphoprotein (RABV P), is an essential factor for RABV transcription and replication. As part of the search for new potential drugs against rabies, we used structure-based drug design using the in silico tools. The binding site of LC8 with RABV P was used for peptide design. Four potential peptide inhibitors (Pep1-4) were selected, modeled, and docked with RABV P. The highest binding affinity was observed for the RABV P-Pep2 complex. Molecular dynamics (MD) simulations were performed and the stability of the peptides and complexes was confirmed. Finally, Pep2 can be used as a potential candidate for peptide-based antiviral therapy against RABV. The identified small peptides may prevent RABV infection based on the results of the current investigation. Further in vitro and in vivo studies are needed to confirm these results.

Antimicrobial Peptides Against Arboviruses: Mechanisms, Challenges, and Future Directions

This review delves into the potential of antimicrobial peptides (AMPs) as promising candidates for combating arboviruses, focusing on their mechanisms of antiviral activity, challenges, and future directions. AMPs have shown promise in preventing arbovirus attachment to host cells, inducing interferon production, and targeting multiple viral stages, illustrating their multifaceted impact on arbovirus infections. Structural elucidation of AMP-viral complexes is explored to deepen the understanding of molecular determinants governing viral neutralization, paving the way for structure-guided design. Furthermore, this review highlights the potential of AMP-based combination therapies to create synergistic effects that enhance overall treatment outcomes while minimizing the likelihood of resistance development. Challenges such as susceptibility to proteases, toxicity, and scalable production are discussed alongside strategies to address these limitations. Additionally, the expanding applications of AMPs as vaccine adjuvants and antiviral delivery systems are emphasized, underscoring their versatility beyond direct antiviral functions.

Phosphorylation of S-S-S Motif in Nuclear Export Protein (NEP) Plays a Critical Role in Viral Ribonucleoprotein (vRNP) Nuclear Export of Influenza A and B Viruses

The phosphorylation of three highly conserved serine residues S23, S24, and S25 (S-S-S motif) has been previously identified in NEP of influenza virus. However, it remains obscure whether and how this motif regulates the vRNPs nuclear export. Here the influenza A H5N6 viruses harboring NEP S23C, S24L, or S25L is generated, allowing to impair the phosphorylation on these sites without mutating viral NS1 protein. These mutations significantly inhibited vRNPs nuclear export are founded, decreased viral infectivity and attenuated virulence in mice. In addition, inhibition or knockout of ATM or CK2, two predicated Ser/Thr protein kinases that phosphorylate the S-S-S motif, impedes vRNP nuclear export and virus replication in cells and reduces the virulence in vivo. Moreover, treatment of NEP peptide mimics containing the S-S-S motif to competitively block NEP binding to the kinases reduces influenza virus replication in cells and mice. However, neither the inhibitors above nor the NEP peptide mimics significantly inhibit the replication of H5N6-DDD mutant, indicating phosphorylation of S-S-S motif is required for the vRNP nuclear export. This studies contribute to a better understanding of the mechanism by which NEP regulates vRNP nuclear export and provides novel insights into antiviral targets against influenza A and B viruses.

Antiviral activity of cathelicidins against porcine epidemic diarrhea virus (PEDV): Mechanisms, and efficacy

Porcine epidemic diarrhea virus (PEDV) is a harmful coronavirus infecting pigs, which is resulting in substantial financial losses in the global pig industry. The lack of effective vaccines or treatments underscores the pressing need for new antiviral strategies. Antimicrobial peptides (AMPs), specifically cathelicidins such as LL-37, have demonstrated promising activity against a range of viruses. This study aims to elucidate the antiviral mechanisms of cathelicidins by examining their inhibitory capabilities against PEDV in vitro. Four pig-derived antimicrobial peptides (PMAP-36, PMAP-23, PR-39, and PG-1), together with chicken-derived CATH-B1 and human-derived LL-37 were analyzed for their anti-PEDV activity. Flow cytometry and fluorescent microscopy confirmed that LL-37 and CATH-B1 had strong inhibitory effects at non-toxic concentrations of 5 and 10 µM, significantly reducing GFP-PEDV infection of Vero cells both in co- and pre-incubation setups. In contrast, none of the porcine peptides exhibited any inhibitory effects, even at higher doses. Fluorogenic LL-37 was shown to enter VERO cells, indicative of a possible immunomodulatory antiviral mode of action. However, transmission electron microscopy clearly indicated that both LL-37 and CATH-B1 affected virus morphology and caused aggregation of viral particles, showing that peptide-virus interaction caused reduced virus infectivity. In conclusion, this analysis highlights the potential of LL-37 and CATH-B1 as inhibitors against PEDV, suggesting promising directions for innovative therapeutic antiviral strategies.

Detection strategies of infectious diseases via peptide-based electrochemical biosensors

Infectious diseases have threatened human life for as long as humankind has existed. One of the most crucial aspects of fighting against these infections is diagnosis to prevent disease spread. However, traditional diagnostic methods prove insufficient and time-consuming in the face of a pandemic. Therefore, studies focusing on detecting viruses causing these diseases have increased, with a particular emphasis on developing rapid, accurate, specific, user-friendly, and portable electrochemical biosensor systems. Peptides are used integral components in biosensor fabrication for several reasons, including various and adaptable synthesis protocols, long-term stability, and specificity. Here, we discuss peptide-based electrochemical biosensor systems that have been developed over the last decade for the detection of infectious diseases. In contrast to other reports on peptide-based biosensors, we have emphasized the following points i) the synthesis methods of peptides for biosensor applications, ii) biosensor fabrication approaches of peptide-based electrochemical biosensor systems, iii) the comparison of electrochemical biosensors with other peptide-based biosensor systems and the advantages and limitations of electrochemical biosensors, iv) the pros and cons of peptides compared to other biorecognition molecules in the detection of infectious diseases, v) different perspectives for future studies with the shortcomings of the systems developed in the past decade.

Antiviral and antibacterial peptides: Mechanisms of action

Antimicrobial peptides (AMPs) present promising alternatives for addressing bacterial and viral multidrug resistance due to their distinctive properties. Understanding the mechanisms of these compounds is essential for achieving this objective. Therefore, this comprehensive review aims to highlight primary natural sources of AMPs and elucidate various aspects of the modes of action of antiviral and antibacterial peptides (ABPs). It emphasizes that antiviral peptides (AVPs) can disrupt the replication cycle of both enveloped and non-enveloped viruses at several stages, including pre-fusion, fusion, and post-entry into the host cell. Additionally, the review discusses the inhibitory effects of ABPs on bacterial growth, outlining their extracellular actions as well as their intracellular activities following membrane translocation. Factors such as structure, size, electric charge, environmental factors, degrading enzymes, and microbial resistance against AMPs can affect the function of AMPs.

Stack-AVP: A Stacked Ensemble Predictor Based on Multi-view Information for Fast and Accurate Discovery of Antiviral Peptides

AVPs, or antiviral peptides, are short chains of amino acids capable of inhibiting viral replication, preventing viral entry, or disrupting viral membranes. They represent a promising area of research for developing new antiviral therapies due to their potential to target a broad spectrum of viruses, incorporating those resistant to traditional antiviral drugs. However, traditional experimental methods for identifying AVPs are often costly and labour-intensive. Thus far, multiple computational methods have been introduced for the in silico identification of AVPs, but these methods still have certain shortcomings. In this study, we propose a novel stacked ensemble learning framework, termed Stack-AVP, for fast and accurate AVP identification. In Stack-AVP, we investigated heterogeneous prediction models, which were trained with 12 commonly used machine learning algorithms coupled with a wide range of multiple feature encoding schemes. Subsequently, these prediction models were adopted to generate multi-view features providing class information and probability information. Finally, we applied our feature selection method to determine the best feature subset for the construction of the final stacked model. Comparative assessments on the independent test dataset revealed that Stack-AVP surpassed the performance of current state-of-the-art methods, with an accuracy of 0.930, MCC of 0.860, and AUC of 0.975. Furthermore, it was found that our multi-view features exhibited a crucial mechanism to improve the prediction performance of AVPs. To facilitate experimental scientists in performing high-throughput identification of AVPs, the prediction sever Stack-AVP is publicly accessible at https://pmlabqsar.pythonanywhere.com/Stack-AVP.

Generative Adversarial Network-Based Augmentation With Noval 2-Step Authentication for Anti-Coronavirus Peptide Prediction

The virus poses a longstanding and enduring danger to various forms of life. Despite the ongoing endeavors to combat viral diseases, there exists a necessity to explore and develop novel therapeutic options. Antiviral peptides are bioactive molecules with a favorable toxicity profile, making them promising alternatives for viral infection treatment. Therefore, this article employed a generative adversarial network for antiviral peptide augmentation and a novel two-step authentication process for augmented synthetic peptides to enhance antiviral activity prediction. Additionally, five widely utilized deep learning models were employed for classification purposes. Initially, a GAN was used to augment the antiviral peptide. In a two-step authentication process, the NCBI-BLAST was utilized to identify the antiviral activity resemblance between the synthetic and real peptide. Subsequently, the hydrophobicity, hydrophilicity, hydroxylic nature, positive charge, and negative charge of synthetic and authentic antiviral peptides were compared before their utilization. Later, to examine the impact of authenticated peptide augmentation in the prediction of antiviral peptides, a comparison is conducted with the outcomes of non-peptide augmented prediction. The study demonstrates that the 1-D convolution neural network with augmented peptide exhibits superior performance compared to other employed classifiers and state-of-the-art models. The network attains a mean classification accuracy of 95.41%, an AUC value of 0.95, and an MCC value of 0.90 on the benchmark antiviral and anti-corona peptides dataset. Thus, the performance of the proposed model indicates its efficacy in predicting the antiviral activity of peptides.

In Vitro Profiling of the Antiviral Peptide TAT-I24

The synthetic peptide TAT-I24 (GRKKRRQRRRPPQCLAFYACFC) exerts antiviral activity against several double-stranded (ds) DNA viruses, including herpes simplex viruses, cytomegalovirus, some adenoviruses, vaccinia virus and SV40 polyomavirus. In the present study, in vitro profiling of this peptide was performed with the aim of characterizing and improving its properties for further development. As TAT-I24 contains three free cysteine residues, a potential disadvantageous feature, peptide variants with replacements or deletions of specific residues were generated and tested in various cell systems and by biochemical analyses. Some cysteine replacements had no impact on the antiviral activity, such as the deletion of cysteine 14, which also showed improved biochemical properties, while the cyclization of cysteines 14 and 20 had the most detrimental effect on antiviral activity. At concentrations below 20 µM, TAT-I24 and selected variants did not induce hemolysis in red blood cells (RBCs) nor modulated lipopolysaccharide (LPS)-induced release of cytokines, such as interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α), in human peripheral blood mononuclear cells (PBMCs). These data indicate that TAT-I24 or its peptide variants are not expected to cause unwanted effects on blood cells.

HybAVPnet: A Novel Hybrid Network Architecture for Antiviral Peptides Prediction

Viruses pose a great threat to human production and life, thus the research and development of antiviral drugs is urgently needed. Antiviral peptides play an important role in drug design and development. Compared with the time-consuming and laborious wet chemical experiment methods, it is critical to use computational methods to predict antiviral peptides accurately and rapidly. However, due to limited data, accurate prediction of antiviral peptides is still challenging and extracting effective feature representations from sequences is crucial for creating accurate models. This study introduces a novel two-step approach, named HybAVPnet, to predict antiviral peptides with a hybrid network architecture based on neural networks and traditional machine learning methods. We adopted a stacking-like structure to capture both the long-term dependencies and local evolution information to achieve a comprehensive and diverse prediction using the predicted labels and probabilities. Using an ensemble technique with the different kinds of features can reduce the variance without increasing the bias. The experimental result shows HybAVPnet can achieve better and more robust performance compared with the state-of-the-art methods, which makes it useful for the research and development of antiviral drugs. Meanwhile, it can also be extended to other peptide recognition problems because of its generalization ability.

Short-form thymic stromal lymphopoietin (sfTSLP) restricts herpes simplex virus infection of human primary keratinocytes

Eczema herpeticum (EH) is a disseminated severe herpes simplex virus type 1 (HSV-1) infection that mainly occurs in a subset of patients suffering from atopic dermatitis (AD). EH is complex and multifaceted, involving immunological changes, environmental influences, and genetic aberrations. Certain genetic variants of the thymic stromal lymphopoietin (TSLP) may predispose to develop severe HSV-1-induced eczema. Therefore, we investigated the impact of TSLP on HSV-1 infection. TSLP encodes for two distinct forms: a long-form (lfTSLP), primarily associated with type 2 immunity, and a short-form (sfTSLP) with anti-inflammatory and antimicrobial properties. While sfTSLP reduced HSV-1 infectibility in human primary keratinocytes (HPK), lfTSLP did not. In HPK treated with sfTSLP, HSV-1 gene expression, and replication decreased, while virion binding to cells and targeting of incoming capsids to the nucleus were not diminished compared to untreated cells. sfTSLP caused only minor changes in the expression of innate immunity cytokines, and its inhibition of HSV-1 infection did not require de novo protein synthesis. Time window experiments indicated a different antiviral mechanism than LL-37. sfTSLP showed the strongest antiviral effect when administered to HPK before or after inoculation with HSV-1, and outperformed the inhibitory potential of LL-37 under these conditions. Our data show that sfTSLP has antiviral functions and promotes repression of the HSV-1 infection in HPK.

Mechanistic insights into lanthipeptide modification by a distinct subclass of LanKC enzyme that forms dimers

Naturally occurring lanthipeptides, peptides post-translationally modified by various enzymes, hold significant promise as antibiotics. Despite extensive biochemical and structural studies, the events preceding peptide modification remain poorly understood. Here, we identify a distinct subclass of lanthionine synthetase KC (LanKC) enzymes with distinct structural and functional characteristics. We show that PneKC, a member of this subclass, forms a dimer and possesses GTPase activity. Through three cryo-EM structures of PneKC, we illustrate different stages of peptide PneA binding, from initial recognition to full binding. Our structures show the kinase domain complexed with the PneA core peptide and GTPγS, a phosphate-bound lyase domain, and an unconventional cyclase domain. The leader peptide of PneA interact with a gate loop, transitioning from an extended to a helical conformation. We identify a dimerization hot spot and propose a "negative cooperativity" mechanism toggling the enzyme between tense and relaxed conformation. Additionally, we identify an important salt bridge in the cyclase domain, differing from those in in conventional cyclase domains. These residues are highly conserved in the LanKC subclass and are part of two signature motifs. These results unveil potential differences in lanthipeptide modification enzymes assembly and deepen our understanding of allostery in these multifunctional enzymes.

Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides

Viruses constitute a constant threat to global health and have caused millions of human and animal deaths throughout human history. Despite advances in the discovery of antiviral compounds that help fight these pathogens, finding a solution to this problem continues to be a task that consumes time and financial resources. Currently, artificial intelligence (AI) has revolutionized many areas of the biological sciences, making it possible to decipher patterns in amino acid sequences that encode different functions and activities. Within the field of AI, machine learning, and deep learning algorithms have been used to discover antimicrobial peptides. Due to their effectiveness and specificity, antimicrobial peptides (AMPs) hold excellent promise for treating various infections caused by pathogens. Antiviral peptides (AVPs) are a specific type of AMPs that have activity against certain viruses. Unlike the research focused on the development of tools and methods for the prediction of antimicrobial peptides, those related to the prediction of AVPs are still scarce. Given the significance of AVPs as potential pharmaceutical options for human and animal health and the ongoing AI revolution, we have reviewed and summarized the current machine learning and deep learning-based tools and methods available for predicting these types of peptides.

A two-stage computational framework for identifying antiviral peptides and their functional types based on contrastive learning and multi-feature fusion strategy

Antiviral peptides (AVPs) have shown potential in inhibiting viral attachment, preventing viral fusion with host cells and disrupting viral replication due to their unique action mechanisms. They have now become a broad-spectrum, promising antiviral therapy. However, identifying effective AVPs is traditionally slow and costly. This study proposed a new two-stage computational framework for AVP identification. The first stage identifies AVPs from a wide range of peptides, and the second stage recognizes AVPs targeting specific families or viruses. This method integrates contrastive learning and multi-feature fusion strategy, focusing on sequence information and peptide characteristics, significantly enhancing predictive ability and interpretability. The evaluation results of the model show excellent performance, with accuracy of 0.9240 and Matthews correlation coefficient (MCC) score of 0.8482 on the non-AVP independent dataset, and accuracy of 0.9934 and MCC score of 0.9869 on the non-AMP independent dataset. Furthermore, our model can predict antiviral activities of AVPs against six key viral families (Coronaviridae, Retroviridae, Herpesviridae, Paramyxoviridae, Orthomyxoviridae, Flaviviridae) and eight viruses (FIV, HCV, HIV, HPIV3, HSV1, INFVA, RSV, SARS-CoV). Finally, to facilitate user accessibility, we built a user-friendly web interface deployed at https://awi.cuhk.edu.cn/∼dbAMP/AVP/.

Analyzing resistome in soil and Human gut: a study on the characterization and risk evaluation of antimicrobial peptide resistance

Objective

The limited existing knowledge regarding resistance to antimicrobial peptides (AMPs) is hindering their broad utilization. The aim of this study is to enhance the understanding of AMP resistance, a pivotal factor in the exploration of alternative drug development in response to the escalating challenge of antibiotic resistance.

Methods

We utilized metagenomic functional selection to analyze genes resistant to AMPs, with a specific focus on the microbiota in soil and the human gut. Through a combination of experimental methods and bioinformatics analyses, our investigation delved into the possibilities of the evolution of resistance to AMPs, as well as the transfer or interchange of resistance genes among the environment, the human body, and pathogens. Additionally, we examined the cross-resistance between AMPs and evaluated interactions among AMPs and conventional antibiotics.

Results

The presence of AMP resistance, including various resistance mechanisms, was observed in both soil and the human gut microbiota, as indicated by our findings. Significantly, the study underscored the facile evolution of AMP resistance and the potential for gene sharing or exchange among different environments. Notably, cross-resistance among AMPs was identified as a phenomenon, while cross-resistance between AMPs and antibiotics was found to be relatively infrequent.

Conclusion

The results of our study highlight the significance of taking a cautious stance when considering the extensive application of AMPs. It is imperative to thoroughly assess potential resistance risks, with a particular focus on the development of resistance to AMPs across diverse domains. A comprehensive grasp of these aspects is essential for making well-informed decisions and ensuring the responsible utilization of AMPs in the ongoing fight against antibiotic resistance.

Food peptidomic analysis of bovine milk fermented by Lacticaseibacillus casei LBC 237: In silico prediction of bioactive peptides and anticancer potential

Bioactive peptides, which exhibited diverse biological activities such as anti-cancer, anti-inflammatory, bactericidal, antiviral, and quorum sensing properties, were considered promising alternative therapeutic agents. Sourced from various raw materials, particularly foods, these peptides garnered significant interest. In this context, the study focused on exploring bioactive peptides derived from bovine whole milk fermentation by Lacticaseibacillus casei LBC 237. Comprehensive peptidomic analysis and in silico predictions, with a specific emphasis on anti-cancer properties, were conducted. The study categorized peptides into BP-LBC, originating from the metabolism of L. casei LBC 237 and not matching any sequence in the Bos taurus database, and BP-MILK, matching a sequence in the Bos taurus database. Among the 143 identified peptides with potential biological activity, 33.56% were attributed to BP-LBC, while 66.43% originated from BP-MILK, demonstrating the important contribution of proteins in bovine milk in the generation of bioactive peptides. Hydrophobic peptides, enriched in Leucine, Lysine, and Proline, dominated both fractions, significantly influencing their functional properties. Pearson correlation analysis revealed inverse relationships between bioactive peptides, molecular weight, and anti-tumor activity in BP-MILK. The DGKVWEESLK peptide exhibited in silico activity against 10 different cancer cell lines. Studying the bioactive properties of peptides from familiar sources enhances the connection between food science and human health. In addition, in silico studies have been crucial in deepening our understanding of the bioactive potential of these peptides and their mode of action.

Metallic Nanocarriers for Therapeutic Peptides: Emerging Solutions Addressing the Delivery Challenges in Brain Ailments

Peptides and proteins have recently emerged as efficient therapeutic alternatives to conventional therapies. Although they emerged a few decades back, extensive exploration of various ailments or disorders began recently. The drawbacks of current chemotherapies and irradiation treatments, such as drug resistance and damage to healthy tissues, have enabled the rise of peptides in the quest for better prospects. The chemical tunability and smaller size make them easy to design selectively for target tissues. Other remarkable properties include antifungal, antiviral, anti-inflammatory, protection from hemorrhage stroke, and as therapeutic agents for gastric disorders and Alzheimer and Parkinson diseases. Despite these unmatched properties, their practical applicability is often hindered due to their weak susceptibility to enzymatic digestion, serum degradation, liver metabolism, kidney clearance, and immunogenic reactions. Several methods are adapted to increase the half-life of peptides, such as chemical modifications, fusing with Fc fragment, change in amino acid composition, and carrier-based delivery. Among these, nanocarrier-mediated encapsulation not only increases the half-life of the peptides in vivo but also aids in the targeted delivery. Despite its structural complexity, they also efficiently deliver therapeutic molecules across the blood-brain barrier. Here, in this review, we tried to emphasize the possible potentiality of metallic nanoparticles to be used as an efficient peptide delivery system against brain tumors and neurodegenerative disorders. SIGNIFICANCE STATEMENT: In this review, we have emphasized the various therapeutic applications of peptides/proteins, including antimicrobial, anticancer, anti-inflammatory, and neurodegenerative diseases. We also focused on these peptides' challenges under physiological conditions after administration. We highlighted the importance and potentiality of metallic nanocarriers in the ability to cross the blood-brain barrier, increasing the stability and half-life of peptides, their efficiency in targeting the delivery, and their diagnostic applications.

Emerging Treatment Approaches for COVID-19 Infection: A Critical Review

In the present scenario, the SARS-CoV-2 virus has imposed enormous damage on human survival and the global financial system. It has been estimated that around 111 million people all around the world have been infected, and about 2.47 million people died due to this pandemic. The major symptoms were sneezing, coughing, cold, difficulty breathing, pneumonia, and multi-organ failure associated 1with SARS-CoV-2. Currently, two key problems, namely insufficient attempts to develop drugs against SARSCoV-2 and the lack of any biological regulating process, are mostly responsible for the havoc caused by this virus. Henceforth, developing a few novel drugs is urgently required to cure this pandemic. It has been noticed that the pathogenesis of COVID-19 is caused by two main events: infection and immune deficiency, that occur during the pathological process. Antiviral medication can treat both the virus and the host cells. Therefore, in the present review, the major approaches for the treatment have been divided into "target virus" and "target host" groups. These two mechanisms primarily rely on drug repositioning, novel approaches, and possible targets. Initially, we discussed the traditional drugs per the physicians' recommendations. Moreover, such therapeutics have no potential to fight against COVID-19. After that, detailed investigation and analysis were conducted to find some novel vaccines and monoclonal antibodies and conduct a few clinical trials to check their effectiveness against SARSCoV- 2 and mutant strains. Additionally, this study presents the most successful methods for its treatment, including combinatorial therapy. Nanotechnology was studied to build efficient nanocarriers to overcome the traditional constraints of antiviral and biological therapies.

Identification of oral therapeutics using an AI platform against the virus responsible for COVID-19, SARS-CoV-2

Purpose: This study introduces a sophisticated computational pipeline, eVir, designed for the discovery of antiviral drugs based on their interactions within the human protein network. There is a pressing need for cost-effective therapeutics for infectious diseases (e.g., COVID-19), particularly in resource-limited countries. Therefore, our team devised an Artificial Intelligence (AI) system to explore repurposing opportunities for currently used oral therapies. The eVir system operates by identifying pharmaceutical compounds that mirror the effects of antiviral peptides (AVPs)-fragments of human proteins known to interfere with fundamental phases of the viral life cycle: entry, fusion, and replication. eVir extrapolates the probable antiviral efficacy of a given compound by analyzing its established and predicted impacts on the human protein-protein interaction network. This innovative approach provides a promising platform for drug repurposing against SARS-CoV-2 or any virus for which peptide data is available. Methods: The eVir AI software pipeline processes drug-protein and protein-protein interaction networks generated from open-source datasets. eVir uses Node2Vec, a graph embedding technique, to understand the nuanced connections among drugs and proteins. The embeddings are input a Siamese Network (SNet) and MLPs, each tailored for the specific mechanisms of entry, fusion, and replication, to evaluate the similarity between drugs and AVPs. Scores generated from the SNet and MLPs undergo a Platt probability calibration and are combined into a unified score that gauges the potential antiviral efficacy of a drug. This integrated approach seeks to boost drug identification confidence, offering a potential solution for detecting therapeutic candidates with pronounced antiviral potency. Once identified a number of compounds were tested for efficacy and toxicity in lung carcinoma cells (Calu-3) infected with SARS-CoV-2. A lead compound was further identified to determine its efficacy and toxicity in K18-hACE2 mice infected with SARS-CoV-2. Computational Predictions: The SNet confidently differentiated between similar and dissimilar drug pairs with an accuracy of 97.28% and AUC of 99.47%. Key compounds identified through these networks included Zinc, Mebendazole, Levomenol, Gefitinib, Niclosamide, and Imatinib. Notably, Mebendazole and Zinc showcased the highest similarity scores, while Imatinib, Levemenol, and Gefitinib also ranked within the top 20, suggesting their significant pharmacological potentials. Further examination of protein binding analysis using explainable AI focused on reverse engineering the causality of the networks. Protein interaction scores for Mebendazole and Imatinib revealed their effects on notable proteins such as CDPK1, VEGF2, ABL1, and several tyrosine protein kinases. Laboratory Studies: This study determined that Mebendazole, Gefitinib, Topotecan and to some extent Carfilzomib showed conventional drug-response curves, with IC50 values near or below that of Remdesivir with excellent confidence all above R2>0.91, and no cytotoxicity at the IC50 concentration in Calu-3 cells. Cyclosporine A showed antiviral activity, but also unconventional drug-response curves and low R2 which are explained by the non-dose dependent toxicity of the compound. Additionally, Niclosamide demonstrated a conventional drug-response curve with high confidence; however, its inherent cytotoxicity may be a confounding element that misrepresents true antiviral efficacy, by reflecting cellular damage rather than a genuine antiviral action. Remdesivir was used as a control compound and was evaluated in parallel with the submitted test article and had conventional drug-response curves validating the overall results of the assay. Mebendazole was identified from the cell studies to have efficacy at non-toxic concentrations and were further evaluated in mice infected with SARS-CoV-2. Mebendazole administered to K18-hACE2 mice infected with SARS-CoV-2, resulted in a 44.2% reduction in lung viral load compared to non-treated placebo control respectively. There were no significant differences in body weight and all clinical chemistry determinations evaluated (i.e., kidney and liver enzymes) between the different treatment groups. Conclusion: This research underscores the potential of repurposing existing compounds for treating COVID-19. Our preliminary findings underscore the therapeutic promise of several compounds, notably Mebendazole, in both in vitro and in vivo settings against SARS-CoV-2. Several of the drugs explored, especially Mebendazole, are off-label medication; their cost-effectiveness position them as economical therapies against SARS-CoV-2.

iAVPs-ResBi: Identifying antiviral peptides by using deep residual network and bidirectional gated recurrent unit

Human history is also the history of the fight against viral diseases. From the eradication of viruses to coexistence, advances in biomedicine have led to a more objective understanding of viruses and a corresponding increase in the tools and methods to combat them. More recently, antiviral peptides (AVPs) have been discovered, which due to their superior advantages, have achieved great impact as antiviral drugs. Therefore, it is very necessary to develop a prediction model to accurately identify AVPs. In this paper, we develop the iAVPs-ResBi model using k-spaced amino acid pairs (KSAAP), encoding based on grouped weight (EBGW), enhanced grouped amino acid composition (EGAAC) based on the N5C5 sequence, composition, transition and distribution (CTD) based on physicochemical properties for multi-feature extraction. Then we adopt bidirectional long short-term memory (BiLSTM) to fuse features for obtaining the most differentiated information from multiple original feature sets. Finally, the deep model is built by combining improved residual network and bidirectional gated recurrent unit (BiGRU) to perform classification. The results obtained are better than those of the existing methods, and the accuracies are 95.07, 98.07, 94.29 and 97.50% on the four datasets, which show that iAVPs-ResBi can be used as an effective tool for the identification of antiviral peptides. The datasets and codes are freely available at https://github.com/yunyunliang88/iAVPs-ResBi.

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.

A systematical review on antimicrobial peptides and their food applications

Food safety issues are a major concern in food processing and packaging industries. Food spoilage is caused by microbial contamination, where antimicrobial peptides (APs) provide solutions by eliminating microorganisms. APs such as nisin have been successfully and commonly used in food processing and preservation. Here, we discuss all aspects of the functionalization of APs in food applications. We briefly review the natural sources of APs and their native functions. Recombinant expression of APs in microorganisms and their yields are described. The molecular mechanisms of AP antibacterial action are explained, and this knowledge can further benefit the design of functional APs. We highlight current utilities and challenges for the application of APs in the food industry, and address rational methods for AP design that may overcome current limitations.

YAP/TAZ axis was involved in the effects of metformin on breast cancer

Breast cancer is malignant tumours in women. A large amount of data analysis shows that Metformin has been shown to play a significance role in reducing the risk of breast cancer, but the mechanism remains unclear. The hippo signalling pathway can be involved in the formation, metastasis and recurrence of breast cancer. When YAP/TAZ is activated, cells can overcome contact inhibition and enter a state of uncontrolled proliferation. Therefore, YAP/TAZ is considered a potential therapeutic target for breast cancer. Eighty breast cancer patients, forty cases of triple-negative and forty cases of HER-2+, were included in this study. In vitro and in vivo experiments were used to confirm the YAP/TAZ axis was involved in the effects of metformin on breast cancer. EMT plays an important role in breast cancer, including chemoresistance and tumour metastasis. Our results confirmed that YAP could modulate the activity of EMT, which in turn altered tumour resistance. Therefore, MET can inhibit EMT by reducing the expression of YAP, and finally achieve the therapeutic effect of breast cancer. Our findings support metformin as a novel YAP inhibitor and potentially as a novel breast cancer drug.

A critical review on antiviral peptides derived from viral glycoproteins and host receptors to decoy herpes simplex virus

The health of the human population has been continuously challenged by viral infections. Herpes simplex virus (HSV) is one of the common causes of illness and can lead to death in immunocompromised patients. Existing anti-HSV therapies are not completely successful in eliminating the infection due to anti-viral drug resistance, ineffectiveness against the latent virus and high toxicity over prolonged use. There is a need to update our knowledge of the current challenges faced in anti-HSV therapeutics and realize the necessity of developing alternative treatment approaches. Protein therapeutics are now being explored as a novel approach due to their high specificity and low toxicity. This review highlights the significance of HSV viral glycoproteins and host receptors in the pathogenesis of HSV infection. Proteins or peptides derived from HSV glycoproteins gC, gB, gD, gH and host cell receptors (HSPG, nectin and HVEM) that act as decoys to inhibit HSV attachment, entry, or fusion have been discussed. Few researchers have tried to improve the efficacy and stability of the identified peptides by modifying them using a peptidomimetic approach. With these efforts, we think developing an alternative treatment option for immunocompromised patients and drug-resistant organisms is not far off.

FFMAVP: a new classifier based on feature fusion and multitask learning for identifying antiviral peptides and their subclasses

Antiviral peptides (AVPs) are widely found in animals and plants, with high specificity and strong sensitivity to drug-resistant viruses. However, due to the great heterogeneity of different viruses, most of the AVPs have specific antiviral activities. Therefore, it is necessary to identify the specific activities of AVPs on virus types. Most existing studies only identify AVPs, with only a few studies identifying subclasses by training multiple binary classifiers. We develop a two-stage prediction tool named FFMAVP that can simultaneously predict AVPs and their subclasses. In the first stage, we identify whether a peptide is AVP or not. In the second stage, we predict the six virus families and eight species specifically targeted by AVPs based on two multiclass tasks. Specifically, the feature extraction module in the two-stage task of FFMAVP adopts the same neural network structure, in which one branch extracts features based on amino acid feature descriptors and the other branch extracts sequence features. Then, the two types of features are fused for the following task. Considering the correlation between the two tasks of the second stage, a multitask learning model is constructed to improve the effectiveness of the two multiclass tasks. In addition, to improve the effectiveness of the second stage, the network parameters trained through the first-stage data are used to initialize the network parameters in the second stage. As a demonstration, the cross-validation results, independent test results and visualization results show that FFMAVP achieves great advantages in both stages.

SARS-CoV-2 inhibitory activity of a short peptide derived from internal fusion peptide of S2 subunit of spike glycoprotein

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has posed a great concern in human population. To fight coronavirus emergence, we have dissected the conserved amino acid region of the internal fusion peptide in the S2 subunit of Spike glycoprotein of SARS-CoV-2 to design new inhibitory peptides. Among the 11 overlapping peptides (9-23-mer), PN19, a 19-mer peptide, exhibited a powerful inhibitory activity against different SARS-CoV-2 clinical isolate variants in absence of cytotoxicity. The PN19 inhibitory activity was found to be dependent on conservation of the central Phe and C-terminal Tyr residues in the peptide sequence. Circular dichroism spectra of the active peptide exhibited an alpha-helix propensity, confirmed by secondary structure prediction analysis. The PN19 inhibitory activity, exerted in the first step of virus infection, was reduced after peptide adsorption treatment with virus-cell substrate during fusion interaction. Additionally, PN19 inhibitory activity was reduced by adding S2 membrane-proximal region derived peptides. PN19 showed binding ability to the S2 membrane proximal region derived peptides, confirmed by molecular modelling, playing a role in the mechanism of action. Collectively, these results confirm that the internal fusion peptide region is a good candidate on which develop peptidomimetic anti SARS-CoV-2 antivirals.

AMPFinder: A computational model to identify antimicrobial peptides and their functions based on sequence-derived information

Antimicrobial peptides (AMPs) called host defense peptides have existed among all classes of life with 5-100 amino acids generally and can kill mycobacteria, envelop viruses, bacteria, fungi, cancerous cells and so on. Owing to the non-drug resistance of AMP, it has been a wonderful agent to find novel therapies. Therefore, it is urgent to identify AMPs and predict their function in a high-throughput way. In this paper, we propose a cascaded computational model to identify AMPs and their functional type based on sequence-derived and life language embedding, called AMPFinder. Compared with other state-of-the-art methods, AMPFinder obtains higher performance both on AMP identification and AMP function prediction. AMPFinder shows better performance with improvement of F1-score (1.45%-6.13%), MCC (2.92%-12.86%) and AUC (5.13%-8.56%) and AP (9.20%-21.07%) on an independent test dataset. And AMPFinder achieve lower bias of R² on a public dataset by 10-fold cross-validation with an improvement of (18.82%-19.46%). The comparison with other state-of-the-art methods shows that AMP can accurately identify AMP and its function types. The datasets, source code and user-friendly application are available at https://github.com/abcair/AMPFinder.

The Anti-Dengue Virus Peptide DV2 Inhibits Zika Virus Both In Vitro and In Vivo

The C-terminal portion of the E protein, known as stem, is conserved among flaviviruses and is an important target to peptide-based antiviral strategies. Since the dengue (DENV) and Zika (ZIKV) viruses share sequences in the stem region, in this study we evaluated the cross-inhibition of ZIKV by the stem-based DV2 peptide (419–447), which was previously described to inhibit all DENV serotypes. Thus, the anti-ZIKV effects induced by treatments with the DV2 peptide were tested in both in vitro and in vivo conditions. Molecular modeling approaches have demonstrated that the DV2 peptide interacts with amino acid residues exposed on the surface of pre- and postfusion forms of the ZIKA envelope (E) protein. The peptide did not have any significant cytotoxic effects on eukaryotic cells but efficiently inhibited ZIKV infectivity in cultivated Vero cells. In addition, the DV2 peptide reduced morbidity and mortality in mice subjected to lethal challenges with a ZIKV strain isolated in Brazil. Taken together, the present results support the therapeutic potential of the DV2 peptide against ZIKV infections and open perspectives for the development and clinical testing of anti-flavivirus treatments based on synthetic stem-based peptides.

DRAVP: A Comprehensive Database of Antiviral Peptides and Proteins

Viruses with rapid replication and easy mutation can become resistant to antiviral drug treatment. With novel viral infections emerging, such as the recent COVID-19 pandemic, novel antiviral therapies are urgently needed. Antiviral proteins, such as interferon, have been used for treating chronic hepatitis C infections for decades. Natural-origin antimicrobial peptides, such as defensins, have also been identified as possessing antiviral activities, including direct antiviral effects and the ability to induce indirect immune responses to viruses. To promote the development of antiviral drugs, we constructed a data repository of antiviral peptides and proteins (DRAVP). The database provides general information, antiviral activity, structure information, physicochemical information, and literature information for peptides and proteins. Because most of the proteins and peptides lack experimentally determined structures, AlphaFold was used to predict each antiviral peptide's structure. A free website for users (http://dravp.cpu-bioinfor.org/, accessed on 30 August 2022) was constructed to facilitate data retrieval and sequence analysis. Additionally, all the data can be accessed from the web interface. The DRAVP database aims to be a useful resource for developing antiviral drugs.

Peptides with Diverse Functions from Scorpion Venom: A Great Opportunity for the Treatment of a Wide Variety of Diseases

Toxicology Research Center, Medical Basic Sciences Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

The venom glands are a rich source of biologically important peptides with pharmaceutical properties. Scorpion venoms have been identified as a reservoir for components that might be considered as great candidates for drug development. Pharmacological properties of the venom compounds have been confirmed in the treatment of different disorders. Ion channel blockers and AMPs are the main groups of scorpion venom components. Despite the existence of several studies about scorpion peptides, there are still valuable components to be discovered. Additionally, owing to the improvement of proteomics and transcriptomics, the number of peptide drugs is steadily increasing, which reflects the importance of these medications. This review evaluates available literatures on some important scorpion venom peptides with pharmaceutical activities. Given that the last three years have been dominated by the COVID-19 from the medical/pharmaceutical perspective, scorpion compounds with the potential against the coronavirus 2 (SARS-CoV-2) are discussed in this review.

Antiviral Peptides in Antimicrobial Surface Coatings—From Current Techniques to Potential Applications

The transmission of pathogens through contact with contaminated surfaces is an important route for the spread of infections. The recent outbreak of COVID-19 highlights the necessity to attenuate surface-mediated transmission. Currently, the disinfection and sanitization of surfaces are commonly performed in this regard. However, there are some disadvantages associated with these practices, including the development of antibiotic resistance, viral mutation, etc.; hence, a better strategy is necessary. In recent years, peptides have been studied to be utilized as a potential alternative. They are part of the host immune defense and have many potential in vivo applications in drug delivery, diagnostics, immunomodulation, etc. Additionally, the ability of peptides to interact with different molecules and membrane surfaces of microorganisms has made it possible to exploit them in ex vivo applications such as antimicrobial (antibacterial and antiviral) coatings. Although antibacterial peptide coatings have been studied extensively and proven to be effective, antiviral coatings are a more recent development. Therefore, this study aims to highlight antiviral coating strategies and the current practices and application of antiviral coating materials in personal protective equipment, healthcare devices, and textiles and surfaces in public settings. Here, we have presented a review on potential techniques to incorporate peptides in current surface coating strategies that will serve as a guide for developing cost-effective, sustainable and coherent antiviral surface coatings. We further our discussion to highlight some challenges of using peptides as a surface coating material and to examine future perspectives.

Are Herbal-peptides Effective as Adjunctive Therapy in Coronavirus Disease COVID-19?

BACKGROUND

Plant antiviral peptides (AVP) are macromolecules that can inhibit the pathogenesis of viruses by affecting their pathogenic mechanism, but most of these peptides can bind to cell membranes, inhibit viral receptors, and prevent viruses. Recently, due to the coronavirus pandemic, the availability of appropriate drugs with low side effects is needed. In this article, the importance of plant peptides in viral inhibition, especially viral inhibition of the coronavirus family, will be discussed.

METHODS

By searching the databases of PubMed, Scopus, Web of Science, the latest articles on plant peptides effective on the COVID-19 virus were collected and reviewed.

RESULTS

Some proteins can act against the COVID-19 virus by blocking sensitive receptors in COVID-19, such as angiotensin-converting enzyme 2 (ACE2). The 23bp sequence of the ACE2 alpha receptor chain can be considered as a target for therapeutic peptides. Protease and RNAP inhibitors and other important receptors that are active against COVID-19 should also be considered.

CONCLUSION

Herbal medicines with AVP, especially those with a long history of antiviral effects, might be a good choice in complement therapy against the COVID-19 virus.

Structural insights on the selective interaction of the histidine-rich piscidin antimicrobial peptide Of-Pis1 with membranes

Of-Pis1 is a potent piscidin antimicrobial peptide (AMP), recently isolated from rock bream (Oplegnathus fasciatus). This rich in histidines and glycines 24-amino acid peptide displays high and broad antimicrobial activity and no significant hemolytic toxicity against human erythrocytes, suggesting low toxicity. To better understand the mechanism of action of Of-Pis1 and its potential selectivity, using NMR and CD spectroscopies, we studied the interaction with eukaryotic and procaryotic membranes and membrane models. Anionic sodium dodecyl sulfate (SDS) and lipopolysaccharide (LPS) micelles were used to mimic procaryotic membranes, while zwitterionic dodecyl phosphocholine (DPC) was used as eukaryotic membrane surrogate. In an aqueous environment, Of-Pis1 adopts a flexible random coil conformation. In DPC and SDS instead, the N-terminal region of Of-Pis1 forms an amphipathic α-helix with the non-polar face in close contact with the micelles. Slower solvent exchange and higher pK_as of the histidine residues in SDS than in DPC suggest that Of-Pis1 interacts more tightly with SDS. Of-Pis1 also binds tightly and structurally perturbs LPS micelles. Of-Pis1 interacts with both Escherichia coli and mammalian cell membranes, but only in the presence of Escherichia coli membranes it populates the helical conformation. Furthermore, ligand-based NMR experiments support a tighter and more specific interaction with bacterial than with eukaryotic membranes. Overall, these data clearly show the selective interaction of this broadly active AMP with bacterial over eukaryotic membranes. The conformational information is discussed in terms of Of-Pis1 amino acid sequence and composition to provide insights useful to design more potent and selective AMPs.

A Review: The Antiviral Activity of Cyclic Peptides

In the design and development of therapeutic agents, macromolecules with restricted structures have stronger competitive edges than linear biological entities since cyclization can overcome the limitations of linear structures. The common issues of linear peptides include susceptibility to degradation of the peptidase enzyme, off-target effects, and necessity of routine dosing, leading to instability and ineffectiveness. The unique conformational constraint of cyclic peptides provides a larger surface area to interact with the target at the same time, improving the membrane permeability and in vivo stability compared to their linear counterparts. Currently, cyclic peptides have been reported to possess various activities, such as antifungal, antiviral and antimicrobial activities. To date, there is emerging interest in cyclic peptide therapeutics, and increasing numbers of clinically approved cyclic peptide drugs are available on the market. In this review, the medical significance of cyclic peptides in the defence against viral infections will be highlighted. Except for chikungunya virus, which lacks specific antiviral treatment, all the viral diseases targeted in this review are those with effective treatments yet with certain limitations to date. Thus, strategies and approaches to optimise the antiviral effect of cyclic peptides will be discussed along with their respective outcomes. Apart from isolated naturally occurring cyclic peptides, chemically synthesized or modified cyclic peptides with antiviral activities targeting coronavirus, herpes simplex viruses, human immunodeficiency virus, Ebola virus, influenza virus, dengue virus, five main hepatitis viruses, termed as type A, B, C, D and E and chikungunya virus will be reviewed herein.

Graphical Abstract

Drug and Anti-Viral Peptide Design to Inhibit the Monkeypox Virus by Restricting A36R Protein

Most recently, monkeypox virus (MPXV) has emanated as a global public health threat. Unavailability of effective medicament against MPXV escalates demand for new therapeutic agent. In this study, in silico strategies were conducted to identify novel drug against the A36R protein of MPXV. The A36R protein of MPXV is responsible for the viral migration, adhesion, and vesicle trafficking to the host cell. To block the A36R protein, 4893 potential antiviral peptides (AVPs) were retrieved from DRAMP and SATPdb databases. Finally, 57 sequences were screened based on peptide filtering criteria, which were then modeled. Likewise, 31 monkeypox virus A36R protein sequences were collected from NCBI protein database to find consensus sequence and to predict 3D protein model. The refined and validated models of the A36R protein and AVP peptides were used to predict receptor-ligand interactions using DINC 2 server. Three peptides that showed best interactions were SATPdb10193, SATPdb21850, and SATPdb26811 with binding energies -6.10, -6.10, and -6.30 kcal/mol, respectively. Small molecules from drug databases were also used to perform virtual screening against the A36R protein. Among databases, Enamine-HTSC showed strong affinity with docking scores ranging from -8.8 to 9.8 kcal/mol. Interaction of target protein A36R with the top 3 peptides and the most probable drug (Z55287118) examined by molecular dynamic (MD) simulation. Trajectory analyses (RMSD, RMSF, SASA, and Rg) confirmed the stable nature of protein-ligand and protein-peptide complexes. This work suggests that identified top AVPs and small molecules might interfere with the function of the A36R protein of MPXV.

HBD-2 variants and SARS-CoV-2: New insights into inter-individual susceptibility

Background

A deep understanding of the causes of liability to SARS-CoV-2 is essential to develop new diagnostic tests and therapeutics against this serious virus in order to overcome this pandemic completely. In the light of the discovered role of antimicrobial peptides [such as human b-defensin-2 (hBD-2) and cathelicidin LL-37] in the defense against SARS-CoV-2, it became important to identify the damaging missense mutations in the genes of these molecules and study their role in the pathogenesis of COVID-19.

Methods

We conducted a comprehensive analysis with multiple in silico approaches to identify the damaging missense SNPs for hBD-2 and LL-37; moreover, we applied docking methods and molecular dynamics analysis to study the impact of the filtered mutations.

Results

The comprehensive analysis reveals the presence of three damaging SNPs in hBD-2; these SNPs were predicted to decrease the stability of hBD-2 with a damaging impact on hBD-2 structure as well. G51D and C53G mutations were located in highly conserved positions and were associated with differences in the secondary structures of hBD-2. Docking-coupled molecular dynamics simulation analysis revealed compromised binding affinity for hBD-2 SNPs towards the SARS-CoV-2 spike domain. Different protein-protein binding profiles for hBD-2 SNPs, in relation to their native form, were guided through residue-wise levels and differential adopted conformation/orientation.

Conclusions

The presented model paves the way for identifying patients prone to COVID-19 in a way that would guide the personalization of both the diagnostic and management protocols for this serious disease.

Antiviral peptides against SARS-CoV-2: therapeutic targets, mechanistic antiviral activity, and efficient delivery

The global pandemic of COVID-19 is a serious public health concern. Over 625 million confirmed cases and more than 6 million deaths have been recorded worldwide. Although several vaccines and antiviral medications have been developed, their efficacy is limited by the emerging new SARS-CoV-2 strains. Peptide-based therapeutics is a fast-growing class of new drugs and have unique advantages over large proteins and small molecules. Antiviral peptides (AVPs) are short polycationic antivirals with broad-spectrum effects, which have been shown to exert both prophylactic and therapeutic actions against reported coronaviruses. The potential therapeutic targets of AVPs are located either on the virus (e.g., E-protein and S-protein) to prohibit viral binding or host cells, particularly, those present on the cell surface (e.g., ACE2 and TMPRSS2). Despite AVPs having promising antiviral effects, their efficacy is limited by low bioavailability. Thus, nanoformulation is a prerequisite for prolonged bioavailability and efficient delivery. This review aimed to present an insight into the therapeutic AVP targets on both virus and host cells by discussing their antiviral activities and associated molecular mechanisms. Besides, it described the technique for discovering and developing possible AVPs based on their targets, as well as the significance of using nanotechnology for their efficient delivery against SARS-CoV-2.

Recent Patents and FDA-Approved Drugs Based on Antiviral Peptides and Other Peptide-Related Antivirals

In spite of existing cases of severe viral infections with a high mortality rate, there are not enough antiviral drugs and vaccines available for the prevention and treatment of such diseases. In addition, the increasing reports of the emergence of viral epidemics highlight, the need for novel molecules with antiviral potential. Antimicrobial peptides (AMPs) with antiviral activity or antiviral peptides (AVPs) have turned into a research hotspot and already show tremendous potential to become pharmaceutically available antiviral medicines. AMPs, a diverse group of bioactive peptides act as a part of our first line of defense against pathogen inactivation. Although most of the currently reported AMPs are either antibacterial or antifungal peptides, the number of antiviral peptides is gradually increasing. Some of the AMPs that are shown as effective antivirals have been deployed against viruses such as influenza A virus, severe acute respiratory syndrome coronavirus (SARS-CoV), HIV, HSV, West Nile Virus (WNV), and other viruses. This review offers an overview of AVPs that have been approved within the past few years and will set out a few of the most essential patents and their usage within the context mentioned above during 2000-2020. Moreover, the present study will explain some of the progress in antiviral drugs based on peptides and peptide-related antivirals.

AI4AVP: an antiviral peptides predictor in deep learning approach with generative adversarial network data augmentation

Motivation

Antiviral peptides (AVPs) from various sources suggest the possibility of developing peptide drugs for treating viral diseases. Because of the increasing number of identified AVPs and the advances in deep learning theory, it is reasonable to experiment with peptide drug design using in silico methods.

Results

We collected the most up-to-date AVPs and used deep learning to construct a sequence-based binary classifier. A generative adversarial network was employed to augment the number of AVPs in the positive training dataset and enable our deep learning convolutional neural network (CNN) model to learn from the negative dataset. Our classifier outperformed other state-of-the-art classifiers when using the testing dataset. We have placed the trained classifiers on a user-friendly web server, AI4AVP, for the research community.

Availability and implementation

AI4AVP is freely accessible at http://axp.iis.sinica.edu.tw/AI4AVP/; codes and datasets for the peptide GAN and the AVP predictor CNN are available at https://github.com/lsbnb/amp_gan and https://github.com/LinTzuTang/AI4AVP_predictor.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Cereals as a Source of Bioactive Compounds with Anti-Hypertensive Activity and Their Intake in Times of COVID-19

Cereals have phytochemical compounds that can diminish the incidence of chronic diseases such as hypertension. The angiotensin-converting enzyme 2 (ACE2) participates in the modulation of blood pressure and is the principal receptor of the virus SARS-CoV-2. The inhibitors of the angiotensin-converting enzyme (ACE) and the block receptors of angiotensin II regulate the expression of ACE2; thus, they could be useful in the treatment of patients infected with SARS-CoV-2. The inferior peptides from 1 to 3 kDa and the hydrophobic amino acids are the best candidates to inhibit ACE, and these compounds are present in rice, corn, wheat, oats, sorghum, and barley. In addition, the vitamins C and E, phenolic acids, and flavonoids present in cereals show a reduction in the oxidative stress involved in the pathogenesis of hypertension. The influence of ACE on hypertension and COVID-19 has turned into a primary point of control and treatment from the nutritional perspective. The objective of this work was to describe the inhibitory effect of the angiotensin-converting enzyme that the bioactive compounds present in cereals possess in order to lower blood pressure and how their consumption could be associated with reducing the virulence of COVID-19.

Antiviral Peptides as Anti-Influenza Agents

Influenza viruses represent a leading cause of high morbidity and mortality worldwide. Approaches for fighting flu are seasonal vaccines and some antiviral drugs. The development of the seasonal flu vaccine requires a great deal of effort, as careful studies are needed to select the strains to be included in each year's vaccine. Antiviral drugs available against Influenza virus infections have certain limitations due to the increased resistance rate and negative side effects. The highly mutative nature of these viruses leads to the emergence of new antigenic variants, against which the urgent development of new approaches for antiviral therapy is needed. Among these approaches, one of the emerging new fields of "peptide-based therapies" against Influenza viruses is being explored and looks promising. This review describes the recent findings on the antiviral activity, mechanism of action and therapeutic capability of antiviral peptides that bind HA, NA, PB1, and M2 as a means of countering Influenza virus infection.

Absolute binding free energies of mucroporin and its analog mucroporin-M1 with the heptad repeat 1 domain and RNA-dependent RNA polymerase of SARS-CoV-2

The peptide Mucroporin and its analog Mucroporin-M1 were studied using the molecular docking and molecular dynamics simulation of their complexation with two protein targets, the Heptad Repeat 1 (HR1) domain and RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2. The molecular docking of the peptide-protein complexes was performed using the glowworm swarm optimization algorithm. The lowest energy poses were submitted to molecular dynamics simulation. Then, the binding free energies of Mucroporin and its analog Mucroporin-M1 with these two protein targets were calculated using the Multistate Bennett Acceptance Ratio (MBAR) method. It was verified that the peptides/HR1 domain complex showed stability in the interaction site determined by molecular docking. It was also found that Mucroporin-M1 has a much higher affinity than Mucroporin to the HR1 protein target. The peptides showed similar stability and affinity at the NTP binding site in the RdRp protein. Additional experimental studies are needed to confirm the antiviral activity of Mucroporin-M1 and a possible mechanism of action against SARS-CoV-2. However, here we indicate that Mucroporin-M1 may have potential antiviral activity against the HR1 domain with the possibility for further peptide optimization.Communicated by Ramaswamy H. Sarma.

In vitro evaluation of antiviral activity of Shouchella clausii probiotic strain and bacterial supernatant against herpes simplex virus type 1

Herpes simplex virus-1 (HSV-1) is an important human neurotropic virus infecting 70% of the world population. Due to the emergence of viral resistance via mutations in HSV-1 genes and some of the adverse effects of antiviral compounds, there is a growing need for safe, novel, and effective therapeutic and preventive strategies. The aim of the present study was to investigate for the first time the potential antiviral activity of Shouchella clausii probiotic strain and bacterial supernatant against HSV-1. The MTT assay was used to determine the possible cytotoxicity of the S. clausii and bacterial supernatant. Vero cells were treated by S. clausii, bacterial supernatant, and HSV-1 under pre-treatment (incubation of Vero cells with S. clausii then HSV-1 inoculation), pre-incubation (mixture of co-incubated HSV-1/S. clausii added to Vero cell), competition (adding HSV-1 and S. clausii into Vero cells simultaneously) and post-treatment (Vero cells inoculated with HSV-1 then incubated with S. clausii) assays. Viral titer reduction (TCID₅₀) and viral DNA relative quantification by real-time PCR were measured in each experimental condition. The results indicated that S. clausii and its supernatant had the greatest inhibitory activity toward HSV-1 in pre-treatment assay. The HSV-1 titer treated with S. clausii, and bacterial supernatant was 3.6 and 2.2 Log₁₀TCID₅₀/mL lower compared to the control (7.66 Log₁₀TCID₅₀/mL). Results showed an antiviral effect of S. clausii and its supernatant. S. clausii could be considered as a novel inhibitor for HSV-1 infection.

Antimicrobial peptides and their potential application in antiviral coating agents

Coronavirus pandemic has evidenced the importance of creating bioactive materials to mitigate viral infections, especially in healthcare settings and public places. Advances in antiviral coatings have led to materials with impressive antiviral performance; however, their application may face health and environmental challenges. Bio-inspired antimicrobial peptides (AMPs) are suitable building blocks for antimicrobial coatings due to their versatile design, scalability, and environmentally friendly features. This review presents the advances and opportunities on the AMPs to create virucidal coatings. The review first describes the fundamental characteristics of peptide structure and synthesis, highlighting the recent findings on AMPs and the role of peptide structure (α-helix, β-sheet, random, and cyclic peptides) on the virucidal mechanism. The following section presents the advances in AMPs coating on medical devices with a detailed description of the materials coated and the targeted pathogens. The use of peptides in vaccine formulations is also reported, emphasizing the molecular interaction of peptides with different viruses and the current clinical stage of each formulation. The role of several materials (metallic particles, inorganic materials, and synthetic polymers) in the design of antiviral coatings is also presented, discussing the advantages and the drawbacks of each material. The final section offers future directions and opportunities for using AMPs on antiviral coatings to prevent viral outbreaks.

Anurans against SARS-CoV-2: A review of the potential antiviral action of anurans cutaneous peptides

At the end of 2019, in China, clinical signs and symptoms of unknown etiology have been reported in several patients whose sample sequencing revealed pneumonia caused by the SARS-CoV-2 virus. COVID-19 is a disease triggered by this virus, and in 2020, the World Health Organization declared it a pandemic. Since then, efforts have been made to find effective therapeutic agents against this disease. Identifying novel natural antiviral drugs can be an alternative to treatment. For this reason, antimicrobial peptides secreted by anurans' skin have gained attention for showing a promissory antiviral effect. Hence, this review aimed to elucidate how and which peptides secreted by anurans' skin can be considered therapeutic agents to treat or prevent human viral infectious diseases. Through a literature review, we attempted to identify potential antiviral frogs' peptides to combat COVID-19. As a result, the Magainin-1 and -2 peptides, from the Magainin family, the Dermaseptin-S9, from the Dermaseptin family, and Caerin 1.6 and 1.10, from the Caerin family, are molecules that already showed antiviral effects against SARS-CoV-2 in silico. In addition to these peptides, this review suggests that future studies should use other families that already have antiviral action against other viruses, such as Brevinins, Maculatins, Esculentins, Temporins, and Urumins. To apply these peptides as therapeutic agents, experimental studies with peptides already tested in silico and new studies with other families not tested yet should be considered.

PD1 CD44 antiviral peptide as an inhibitor of the protein-protein interaction in dengue virus invasion

Dengue virus (DENV) infection is mediated by the interaction between the virus envelope protein and cellular receptors of the host cells. In this study, we designed peptides to inhibit protein-protein interaction between dengue virus and CD44 receptor, which is one of the receptors used by DENV for entry. In silico model complexes were designed between domain III of the viral envelope protein of dengue virus 2 and the domain of human CD44 receptor using ClusPro 2.0, (https://cluspro.bu.edu/login.php), and inhibition peptides were designed with Rosetta Online-Server(http://rosie.rosettacommons.org/peptiderive). We identified one linear antiviral peptide of 18 amino acids derived from the human CD44 receptor, PD1 CD44. It did not show hemolysis or toxicity in HepG2 or BHK cell lines, nor did it stimulate the release of IL-1β, IL-6, TNF-α, and IFN-γ, below 100 µM. It had an IC50 of 13.8 µM and maximum effective dose of 54.9 µM evaluated in BHK cells. The decrease in plaque-forming units/mL for DENV1, DENV2, DENV3, and DENV4 was 99.60%, 99.40%, 97.80%, and 70.50%, respectively, and similar results were obtained by RT-qPCR. Non-structural protein 1 release was decreased in pre- and co-treatment but not in post-treatment. Competition assays between the DN59 peptide, envelope protein, and the fragment of domain III "MDKLQLKGMSYSMCTGKF" of the viral envelope of DENV2 and PD1 CD44 showed that our peptide lost its antiviral activity. We demonstrated that our peptide decreased endosome formation, and we propose that it binds to the envelope protein of DENV, inhibiting viral invasion/fusion.

Selective Inhibition of Murine Cytomegalovirus Viral Gene Expression by the Antiviral Peptide TAT-I24

The effect of the antiviral peptide TAT-I24 on viral gene expression in cells infected with murine cytomegalovirus (MCMV) was investigated. The expression of immediate-early, early and late genes was highly induced upon infection with MCMV. In the presence of the peptide, the expression of all tested genes was sustainably reduced to a similar extent, independent of whether they were immediate-early, early or late genes. In contrast, the expression of host genes, such as NF-κB inhibitor alpha (Nfkbia), interferon-induced protein with tetratricopeptide repeats 1 (Ifit1), chemokine (C-X-C motif) ligand 10 (Cxcl10), chemokine (C-C motif) ligand 7 (Ccl7) and chemokine (C-C motif) ligand 5 (Ccl5), which are induced early upon virus infection, was only transiently suppressed in peptide-treated cells. The expression of other host genes which are affected by MCMV infection and play a role in endoplasmic reticulum stress or DNA-damage repair was not inhibited by the peptide. A combination of TAT-I24 with the nucleoside analogue cidofovir showed enhancement of the antiviral effect, demonstrating that viral replication can be more efficiently inhibited with a combination of drugs acting at different stages of the viral life-cycle.

Synthetic antiviral peptides: a new way to develop targeted antiviral drugs

The global concern over emerging and re-emerging viral infections has spurred the search for novel antiviral agents. Peptides with antiviral activity stand out, by overcoming limitations of the current drugs utilized, due to their biocompatibility, specificity and effectiveness. Synthetic peptides have been shown to be viable alternatives to natural peptides due to several difficulties of using of the latter in clinical trials. Various platforms have been utilized by researchers to predict the most effective peptide sequences against HIV, influenza, dengue, MERS and SARS. Synthetic peptides are already employed in the treatment of HIV infection. The novelty of this study is to discuss, for the first time, the potential of synthetic peptides as antiviral molecules. We conclude that synthetic peptides can act as new weapons against viral threats to humans.