Nanobody peptide conjugate: a novel CD163 based broad neutralizing strategy against porcine reproductive and respiratory syndrome virus.

BACKGROUND: Porcine reproductive and respiratory syndrome virus (PRRSV) is a prevalent swine pathogen, which has caused adverse impact on the global swine industry for almost 30 years. However, due to the immune suppression caused by the virus and the genetic diversity in PRRSV, no virus-targeting broad neutralizing strategy has been successfully developed yet. Antiviral peptide and nanobody have attracted extensive attention with the ease in production and the efficacy in practice. In this study, four new fusion proteins named nanobody peptide conjugates (NPCs) were developed by combining PRRSV specific non-neutralizing nanobodies with CD163-derived peptides targeting the receptor binding domain (RBD) of PRRSV proteins. RESULTS: Four NPCs were successfully constructed using two nanobodies against PRRSV N and nsp9 individually, recombining with two antiviral peptides 4H7 or 8H2 from porcine CD163 respectively. All four NPCs demonstrated specific capability of binding to PRRSV and broad inhibitory effect against various lineages of PRRSV in a dose-dependent manner. NPCs interfere with the binding of the RBD of PRRSV proteins to CD163 in the PRRSV pre-attachment stage by CD163 epitope peptides in the assistance of Nb components. NPCs also suppress viral replication during the stage of post-attachment, and the inhibitory effects depend on the antiviral functions of Nb parts in NPCs, including the interference in long viral RNA synthesis, NF-κB and IFN-ß activation. Moreover, an interaction was predicted between aa K31 and T32 sites of neutralizing domain 4H7 of NPC-N/nsp9-4H7 and the motif 171NLRLTG176 of PRRSV GP2a. The motif 28SSS30 of neutralizing domain 8H2 of NPC-N/nsp9-8H2 could also form hydrogens to bind with the motif 152NAFLP156 of PRRSV GP3. The study provides valuable insights into the structural characteristics and potential functional implications of the RBD of PRRSV proteins. Finally, as indicated in a mouse model, NPC intranasally inoculated in vivo for 12-24 h sustains the significant neutralizing activity against PRRSV. These findings inspire the potential of NPC as a preventive measure to reduce the transmission risk in the host population against respiratory infectious agents like PRRSV. CONCLUSION: The aim of the current study was to develop a peptide based bioactive compound to neutralize various PRRSV strains. The new antiviral NPC (nanobody peptide conjugate) consists of a specific nanobody targeting the viral protein and a neutralizing CD163 epitope peptide for virus blocking and provides significant antiviral activity. The study will greatly promote the antiviral drug R&D against PRRSV and enlighten a new strategy against other viral diseases.

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/.

Deepstacked-AVPs: predicting antiviral peptides using tri-segment evolutionary profile and word embedding based multi-perspective features with deep stacking model.

BACKGROUND: Viral infections have been the main health issue in the last decade. Antiviral peptides (AVPs) are a subclass of antimicrobial peptides (AMPs) with substantial potential to protect the human body against various viral diseases. However, there has been significant production of antiviral vaccines and medications. Recently, the development of AVPs as an antiviral agent suggests an effective way to treat virus-affected cells. Recently, the involvement of intelligent machine learning techniques for developing peptide-based therapeutic agents is becoming an increasing interest due to its significant outcomes. The existing wet-laboratory-based drugs are expensive, time-consuming, and cannot effectively perform in screening and predicting the targeted motif of antiviral peptides. METHODS: In this paper, we proposed a novel computational model called Deepstacked-AVPs to discriminate AVPs accurately. The training sequences are numerically encoded using a novel Tri-segmentation-based position-specific scoring matrix (PSSM-TS) and word2vec-based semantic features. Composition/Transition/Distribution-Transition (CTDT) is also employed to represent the physiochemical properties based on structural features. Apart from these, the fused vector is formed using PSSM-TS features, semantic information, and CTDT descriptors to compensate for the limitations of single encoding methods. Information gain (IG) is applied to choose the optimal feature set. The selected features are trained using a stacked-ensemble classifier. RESULTS: The proposed Deepstacked-AVPs model achieved a predictive accuracy of 96.60%%, an area under the curve (AUC) of 0.98, and a precision-recall (PR) value of 0.97 using training samples. In the case of the independent samples, our model obtained an accuracy of 95.15%, an AUC of 0.97, and a PR value of 0.97. CONCLUSION: Our Deepstacked-AVPs model outperformed existing models with a ~ 4% and ~ 2% higher accuracy using training and independent samples, respectively. The reliability and efficacy of the proposed Deepstacked-AVPs model make it a valuable tool for scientists and may perform a beneficial role in pharmaceutical design and research academia.

Virtual Screening of Peptide Libraries: The Search for Peptide-Based Therapeutics Using Computational Tools.

Over the last few decades, we have witnessed growing interest from both academic and industrial laboratories in peptides as possible therapeutics. Bioactive peptides have a high potential to treat various diseases with specificity and biological safety. Compared to small molecules, peptides represent better candidates as inhibitors (or general modulators) of key protein-protein interactions. In fact, undruggable proteins containing large and smooth surfaces can be more easily targeted with the conformational plasticity of peptides. The discovery of bioactive peptides, working against disease-relevant protein targets, generally requires the high-throughput screening of large libraries, and in silico approaches are highly exploited for their low-cost incidence and efficiency. The present review reports on the potential challenges linked to the employment of peptides as therapeutics and describes computational approaches, mainly structure-based virtual screening (SBVS), to support the identification of novel peptides for therapeutic implementations. Cutting-edge SBVS strategies are reviewed along with examples of applications focused on diverse classes of bioactive peptides (i.e., anticancer, antimicrobial/antiviral peptides, peptides blocking amyloid fiber formation).

Chemically modified antiviral peptides against SARS-CoV-2.

To date, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) COVID-19 pandemic continues to be a potentially lethal disease. Although both vaccines and specific antiviral drugs have been approved, the search for more specific therapeutic approaches is still ongoing. The infection mechanism of SARS-CoV-2 consists of several stages, and each one can be selectively blocked to disrupt viral infection. Peptides are a promising class of antiviral compounds, which may be suitably modified to be more stable, more effective, and more selective towards a specific viral replication step. The latter two goals might be obtained by increasing the specificity and/or the affinity of the interaction with a specific target and often imply the stabilization of the secondary structure of the active peptide. This review is focused on modified antiviral peptides against SARS-CoV-2 acting at different stages of virus replication, including ACE2-RBD interaction, membrane fusion mechanism, and the proteolytic cleavage by different viral proteases. Therefore, the landscape presented herein provides a useful springboard for the design of new and powerful antiviral therapeutics.

Antiviral peptides inhibiting the main protease of SARS-CoV-2 investigated by computational screening and in vitro protease assay.

The main protease (Mpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) plays an important role in viral replication and transcription and received great attention as a vital target for drug/peptide development. Therapeutic agents such as small-molecule drugs or peptides that interact with the Cys-His present in the catalytic site of Mpro are an efficient way to inhibit the protease. Although several emergency-approved vaccines showed good efficacy and drastically dropped the infection rate, evolving variants are still infecting and killing millions of people globally. While a small-molecule drug (Paxlovid) received emergency approval, small-molecule drugs have low target specificity and higher toxicity. Besides small-molecule drugs, peptide therapeutics are thus gaining increasing popularity as they are easy to synthesize and highly selective and have limited side effects. In this study, we investigated the therapeutic value of 67 peptides targeting Mpro using molecular docking. Subsequently, molecular dynamics (MD) simulations were implemented on eight protein-peptide complexes to obtain molecular-level information on the interaction between these peptides and the Mpro active site, which revealed that temporin L, indolicidin, and lymphocytic choriomeningitis virus (LCMV) GP1 are the best candidates in terms of stability, interaction, and structural compactness. These peptides were synthesized using the solid-phase peptide synthesis protocol, purified by reversed-phase high-performance liquid chromatography (RP-HPLC), and authenticated by mass spectrometry (MS). The in vitro fluorometric Mpro activity assay was used to validate the computational results, where temporin L and indolicidin were observed to be very active against SARS-CoV-2 Mpro with IC50 values of 38.80 and 87.23 µM, respectively. A liquid chromatography-MS (LC-MS) assay was developed, and the IC50 value of temporin L was measured at 23.8 µM. The solution-state nuclear magnetic resonance (NMR) structure of temporin L was determined in the absence of sodium dodecyl sulfate (SDS) micelles and was compared to previous temporin structures. This combined investigation provides critical insights and assists us to further develop peptide inhibitors of SARS-CoV-2 Mpro through structural guided investigation.

Venomous gland transcriptome and venom proteomic analysis of the scorpion Androctonus amoreuxi reveal new peptides with anti-SARS-CoV-2 activity.

The recent COVID-19 pandemic shows the critical need for novel broad spectrum antiviral agents. Scorpion venoms are known to contain highly bioactive peptides, several of which have demonstrated strong antiviral activity against a range of viruses. We have generated the first annotated reference transcriptome for the Androctonus amoreuxi venom gland and used high performance liquid chromatography, transcriptome mining, circular dichroism and mass spectrometric analysis to purify and characterize twelve previously undescribed venom peptides. Selected peptides were tested for binding to the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein and inhibition of the spike RBD - human angiotensin-converting enzyme 2 (hACE2) interaction using surface plasmon resonance-based assays. Seven peptides showed dose-dependent inhibitory effects, albeit with IC50 in the high micromolar range (117-1202 µM). The most active peptide was synthesized using solid phase peptide synthesis and tested for its antiviral activity against SARS-CoV-2 (Lineage B.1.1.7). On exposure to the synthetic peptide of a human lung cell line infected with replication-competent SARS-CoV-2, we observed an IC50 of 200 nM, which was nearly 600-fold lower than that observed in the RBD - hACE2 binding inhibition assay. Our results show that scorpion venom peptides can inhibit the SARS-CoV-2 replication although unlikely through inhibition of spike RBD - hACE2 interaction as the primary mode of action. Scorpion venom peptides represent excellent scaffolds for design of novel anti-SARS-CoV-2 constrained peptides. Future studies should fully explore their antiviral mode of action as well as the structural dynamics of inhibition of target virus-host interactions.

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.

Antiviral Peptides Derived from Plants: Their Designs and Functions.

In recent years, plant-derived bioactive compounds have been developed as antiviral agents. Plants synthesize a variety of compounds, especially peptides, which possess antimicrobial activity. Current studies have shown that some antimicrobial peptides have antiviral activity against a wide range of human DNA and RNA viruses and play an effective role in the treatment of human viral diseases. These peptides act through different mechanisms. They can integrate into the envelope of the target virus or cell membrane of the host, resulting in an unstable membrane. For instance, some peptides prevent the attachment of viral spike proteins to host cells. On the other hand, some peptides may alter the cellular pathways, including DNA replication or protein synthesis, leading to the suppression of viral infection. However, the antiviral activity of peptides can be affected by their chemical and structural properties. In several studies, the properties of antimicrobial (antiviral) peptides were altered by minor modifications, but these changes require tools to predict. Recently, computational approaches have been introduced to analyze the effects of structural modifications on the physicochemical properties, mechanism of action, stability, and activity of peptides. In this mini-review, we will describe the design and function of antiviral peptides derived from plants.

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.

Identification and molecular docking of novel chikungunya virus NSP4 inhibitory peptides from camel milk proteins.

The chikungunya (CHIK) virus is an arbovirus belonging to the alphavirus (Togaviridae family). Around 85% of infected individuals suffer from symptoms such as high fever and severe joint pain; about 30 to 40% will develop a chronic joint illness. The Nsp4 protease is the most conserved protein in the alphavirus family and serves as an RNA-dependent RNA polymerase (RdRp). Targeting this enzyme might inhibit the CHIKV replication cycle. This work aims to in silico study the CHIKV RdRp inhibitory effect of peptides derived from camel milk protein as antiviral peptides. Various bioinformatics tools were recruited to identify, screen, predict and assess peptides obtained from camel milk as antiviral peptides (AVPs). During this study, CHIKV Nsp4 (polymerase) was used as a target to be inhibited by interaction with peptides derived from camel milk protein. Among 91 putative bioactive peptides, the best predicted 5 were further evaluated. Molecular docking showed that the top 5 AVPs generated better docking scores and interacted well with active sites of Nsp4 by the formation of different hydrogen bonds as well as other bonds. AVP63 and AVP20 showed the best Molecular docking and MD simulation results. The residue 315ASP of the GDD motif (catalytic core) exhibited a favorable interaction with the AVPs. The findings of this study suggest that the AVP20 derived from camel milk protein can be a potential novel CHIKV polymerase inhibitor.Communicated by Ramaswamy H. Sarma.

Development of Hemagglutinin-Neuraminidase Homologous Peptides as Novel Promising Therapeutic Agents Against Peste des Petits Ruminants Virus.

The lack of specific antiviral therapy and complications associated with the existing peste des petits ruminants (PPR) vaccines accentuates the search of novel antiviral blocking agents in order to curtail the PPR infection at initial level. The synthetic hemagglutinin-neuraminidase (HN) homologous peptides may compete with the natural HN protein of PPR virus for binding to signaling lymphocytic activation molecule (SLAM) receptor, consequently, may disrupt peste des petits ruminants virus (PPRV) at entry level. Therefore, insilico analysis, synthesis, purification and subsequent characterization of HN homologous peptides were conducted in this study. The HN homologous peptides were synthesized by means of solid phase chemistry and were purified by reversed-phase-high performance liquid chromatography. The mass as well as sequence of HN homologous peptides were assessed by mass spectroscopy while its secondary structure was elucidated by circular dichroism spectroscopy. The binding (interaction) efficacy of HN homologous peptides with PPRV antibodies was assessed via indirect enzyme linked immunosorbent assay, visual detection test (red wine to purple), bathochromic shift under UV-Vis spectrophotometry and lateral flow immunochromatographic strip test. The antiviral properties and cytotoxicity of these peptides were also assessed in B95a cell line with changes in cytopathic effect and titer of PPRV (Sungri/96). The presence of green fluorescein isothiocyanate over the B95a cell surface pointed towards the binding of HN homologous peptides with surface SLAM receptor. Moreover, the intact beta sheet configuration in water and lower cytotoxicity [cytotoxic concentration 50 (CC50) > 1000 µg/ml] of these peptides signifies its in vivo use. Among HN homologous peptides, the binding efficacy and antiviral properties of pep A was relatively high in comparison to pep B and Pep ppr peptides. The prerequisite concentration of HN homologous peptides (pep A = 12.5 µg/ml; pep B = 25 µg/ml; pep ppr = 25 µg/ml) to exemplify its antiviral effect was much lower than its CC50 level. Hence, this study signifies the therapeutic potential of synthetic HN homologous peptides.

Antiviral polysaccharide and antiviral peptide delivering nanomaterials for prevention and treatment of SARS-CoV-2 caused COVID-19 and other viral diseases.

Antiviral peptides and antiviral polysaccharides can play a major role in the prevention and treatment of emerging viral health problems. These antiviral compounds are biocompatible, environmentally friendly, non-toxic, and cost-effective, yet are poorly water soluble and vulnerable to enzymatic (protease) degradation within the aggressive intercellular microenvironment. Therefore, they should be properly protected and delivered to viruses and host cells by the well-designed nanocarriers that mimic viruses in terms of size, morphology, and smart function. This literature review is meant to introduce the latest advances (mainly within the past five years) in antiviral nano-assemblies comprising antiviral peptides or antiviral polysaccharides. To the best of our knowledge, there is no similar study in the literature that has solely and sufficiently investigated such antiviral nanomaterials partially or totally derived from nature. The rational classification of microorganism-, plant-, and animal-derived antiviral polysaccharide and antiviral peptide delivering nanomaterials and exploration of their relevant applications will clarify the promising capacity of these state-of-the-art materials for a number of technologies developed to inactivate viruses.

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.

Unraveling the Role of Antimicrobial Peptides in Insects.

Antimicrobial peptides (AMPs) are short, mainly positively charged, amphipathic molecules. AMPs are important effectors of the immune response in insects with a broad spectrum of antibacterial, antifungal, and antiparasitic activity. In addition to these well-known roles, AMPs exhibit many other, often unobvious, functions in the host. They support insects in the elimination of viral infections. AMPs participate in the regulation of brain-controlled processes, e.g., sleep and non-associative learning. By influencing neuronal health, communication, and activity, they can affect the functioning of the insect nervous system. Expansion of the AMP repertoire and loss of their specificity is connected with the aging process and lifespan of insects. Moreover, AMPs take part in maintaining gut homeostasis, regulating the number of endosymbionts as well as reducing the number of foreign microbiota. In turn, the presence of AMPs in insect venom prevents the spread of infection in social insects, where the prey may be a source of pathogens.

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.

Virtual Screening-Based Peptides Targeting Spike Protein to Inhibit Porcine Epidemic Diarrhea Virus (PEDV) Infection.

Due to the rapid mutation of porcine epidemic diarrhea virus (PEDV), existing vaccines cannot provide sufficient immune protection for pigs. Therefore, it is urgent to design the affinity peptides for the prevention and control of this disease. In this study, we made use of a molecular docking technology for virtual screening of affinity peptides that specifically recognized the PEDV S1 C-terminal domain (CTD) protein for the first time. Experimentally, the affinity, cross-reactivity and sensitivity of the peptides were identified by an enzyme-linked immunosorbent assay (ELISA) and a surface plasmon resonance (SPR) test, separately. Subsequently, Cell Counting Kit-8 (CCK-8), quantitative real-time PCR (qRT-PCR), Western blot and indirect immunofluorescence were used to further study the antiviral effect of different concentrations of peptide 110766 in PEDV. Our results showed that the P/N value of peptide 110766 at 450 nm reached 167, with a KD value of 216 nM. The cytotoxic test indicated that peptide 110766 was not toxic to vero cells. Results of the absolute quantitative PCR revealed that different concentrations (3.125 µM, 6.25 µM, 12.5 µM, 25 µM, 50 µM, 100 µM, 200 µM) of peptide 110766 could significantly reduce the viral load of PEDV compared with the virus group (p < 0.0001). Similarly, results of Western blot and indirect immunofluorescence also suggested that the antiviral effect of peptide 110766 at 3.125 is still significant. Based on the above research, high-affinity peptide 110766 binding to the PEDV S1-CTD protein was attained by a molecular docking technology. Therefore, designing, screening, and identifying affinity peptides can provide a new method for the development of antiviral drugs for PEDV.

Bioprospecting the American Alligator Peptidome for antiviral peptides against Venezuelan equine encephalitis virus.

The innate immune protection provided by cationic antimicrobial peptides (CAMPs) has been shown to extend to antiviral activity, with putative mechanisms of action including direct interaction with host cells or pathogen membranes. The lack of therapeutics available for the treatment of viruses such as Venezuelan equine encephalitis virus (VEEV) underscores the urgency of novel strategies for antiviral discovery. American alligator plasma has been shown to exhibit strong in vitro antibacterial activity, and functionalized hydrogel particles have been successfully employed for the identification of specific CAMPs from alligator plasma. Here, a novel bait strategy in which particles were encapsulated in membranes from either healthy or VEEV-infected cells was implemented to identify peptides preferentially targeting infected cells for subsequent evaluation of antiviral activity. Statistical analysis of peptide identification results was used to select five candidate peptides for testing, of which one exhibited a dose-dependent inhibition of VEEV and also significantly inhibited infectious titers. Results suggest our bioprospecting strategy provides a versatile platform that may be adapted for antiviral peptide identification from complex biological samples.

Potential antiviral peptides against the nucleoprotein of SARS-CoV-2.

Nucleoprotein is a conserved structural protein of SARS-CoV-2, which is involved in several functions, including replication, packaging, and transcription. In this research, 21 antiviral peptides that are known to have inhibitory function against nucleoprotein in several other viruses, were screened computationally against the nucleoprotein of SARS-CoV-2. The complexes of five best performing peptides (AVP1142, AVP1145, AVP1148, AVP1150, AVP1155) with nucleoprotein were selected for subsequent screening via 5 ns molecular dynamics (MD) simulation. Two peptides, namely AVP1145 and AVP1155, came out as promising candidates and hence were selected for 200 ns MD simulation for further validation, incorporating a DMPC-based membrane environment. In the long MD simulation, both AVP1155 and AVP1145 utilized multiple residues-mainly aromatic, acidic, and nonpolar residues-as interacting points to remain in contact with the nucleoprotein and formed predominantly hydrogen bonds along with hydrophobic and electrostatic interactions. However, AVP1155 proved to be superior to AVP1145 when its complex with nucleoprotein was analyzed in terms of root-mean-square deviation, root-mean-square fluctuation, radius of gyration, solvent accessible surface area and free energy landscape. In a nutshell, the findings of this research may guide future studies in the development of selective peptide inhibitors of SARS-CoV-2 nucleoprotein. Supplementary Information: The online version contains supplementary material available at 10.1007/s11696-022-02514-4.