The present study represents a formulation of nanocurcumin based hybrid virosomes (NC-virosome) to deliver drugs at targeted sites. Curcumin is a bioactive component derived from Curcuma longa and well-known for its medicinal property, but it exhibits poor solubility and rapid metabolism, which led to low bioavailability and hence limits its applications. Nanocurcumin was prepared to increase the aqueous solubility and to overcome all the limitations associated with curcumin. Influenza virosomes were prepared by solubilization of the viral membrane with 1,2-distearoyl-sn-glycerol-3-phosphocholine (DSPC). During membrane reconstitution, the hydrophilic nanocurcumin was added to the solvent system, followed by overnight dialysis to obtain NC-virosomes. The same was characterized using a transmission electron microscope (TEM) and scanning electron microscope (SEM), MTT assay was used to evaluate it's in vitro-cytotoxicity using MDA-MB231 and Mesenchyme stem cells (MSCs). The results showed NC-virosomes has spherical morphology with size ranging between 60 and 90 nm. It showed 82.6% drug encapsulation efficiency. The viability of MDA-MB231 cells was significantly inhibited by NC-virosome in a concentration-dependent manner at a specific time. The IC50 for nanocurcumin and NC-virosome was 79.49 and 54.23 µg/ml, respectively. The site-specific drug-targeting, high efficacy and non- toxicity of NC-virosomes proves its future potential as drug delivery vehicles.

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Preparation and characterization of nanocurcumin based hybrid virosomes as a drug delivery vehicle with enhanced anticancerous activity and reduced toxicity.

Berberine regulates PADI4-related macrophage function to prevent lung cancer

Milk provides energy as well as the basic nutrients required by the body. In particular, milk is beneficial for bone growth and development in children. Based on scientific evidence, cattle milk is an excellent and highly nutritious dietary component that is abundant in vitamins, calcium, potassium, and protein, among other minerals. However, the commercial productivity of cattle milk is markedly affected by mastitis. Mastitis is an economically important disease that is characterized by inflammation of the mammary gland. This disease is frequently caused by microorganisms and is detected as abnormalities in the udder and milk. Streptococcus agalactiae is a prominent cause of mastitis. Antibiotics are rarely used to treat this infection, and other available treatments take a long time to exhibit a therapeutic effect. Vaccination is recommended to protect cattle from mastitis. Accordingly, the present study sought to design a multi-epitope vaccine using immunoinformatics.The vaccine was designed to be antigenic, immunogenic, non-toxic, and non-allergic, and had a binding affinity with Toll-like receptor 2 (TLR2) and TLR4 based on structural modeling, docking, and molecular dynamics simulation studies. Besides, the designed vaccine was successfully expressed in E. coli. expression vector (pET28a) depicts its easy purification for production on a larger scale, which was determined through in silico cloning. Further, immune simulation analysis revealed the effectiveness of the vaccine with an increase in the population of B and T cells in response to vaccination.This multi-epitope vaccine is expected to be effective at generating an immune response, thereby paving the way for further experimental studies to combat mastitis. 

https://bmcvetres.biomedcentral.com/counter/pdf/10.1186/s12917-022-03432-z

Designing multi-epitope-based vaccine targeting surface immunogenic protein of Streptococcus agalactiae using immunoinformatics to control mastitis in dairy cattle

Abstract Mucormycosis is a deadly fungal disease mainly caused by Rhizopus oryzae (strain 99–880), also known as Rhizopus delemar. Previously, mucormycosis occurs in immunocompromised patients of diabetes mellitus, cancer, organ transplant, etc. But there was a drastic increase in mucormycosis cases in the ongoing COVID-19 pandemic. Despite several available therapies and antifungal treatments, the mortality rate of mucormycosis is about more than 50%. Currently, there is no vaccine available in the market for mucormycosis that urgently needs to develop a potential vaccine against mucormycosis with high efficacy. In the present study, we have screened 4 genome-derived predicted antigens (GDPA) through sequential filtration of the whole proteome of R. delemar using different benchmarked bioinformatics tools. These 4 GDPA along with 4 randomly selected experimentally reported antigens (ERA) were sourced for prediction of B- and T- cell epitopes and utilized in designing of two potential multi-epitope vaccine candidates which can induce both innate and adaptive immunity against R. delemar. Besides these, comparative immune simulation studies and in silico cloning were performed using L. lactis as an expression system for their possible uses as oral vaccines. This is the first multi-epitope vaccine designed against R. delemar through systematic pipelined reverse vaccinology and immunoinformatic approaches. Although the wet-lab based experimental validation of designed vaccines is required before testing in the preclinical model, the current study will significantly help in reducing the cost of experimentation as well as improving the efficacy of vaccine therapy against mucormycosis and other pathogenic diseases. Communicated by Ramaswamy H. Sarma

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Screening of potential antigens from whole proteome and development of multi-epitope vaccine against Rhizopus delemar using immunoinformatics approaches

The COVID-19 crisis, incited by the zoonotic SARS-CoV-2 virus, has quickly escalated into a catastrophic public health issue and a grave threat to humankind owing to the advent of mutant viruses. Multiple pharmaceutical therapies or biologics envision stopping the virus from spreading further; however, WHO has voiced concerns about the variants of concern (VoCs) inability to respond. Nanobodies are a new class of antibody mimics with binding affinity and specificity similar to classical mAbs, as well as the privileges of a small molecular weight, ease of entry into solid tissues, and binding cryptic epitopes of the antigen. Herein, we investigated multiple putative anti-SARS-CoV-2 nanobodies targeting the Receptor binding domain of the WHO-listed SARS-CoV-2 variants of concern using a comprehensive computational immunoinformatics methodology. With affinity maturation via alanine scanning mutagenesis, we remodeled an ultrapotent nanobody with substantial breadth and potency, exhibiting pico-molar binding affinities against all the VoCs. An antiviral peptide with specificity for ACE-2 receptors was affixed to make it multispecific and discourage viral entry. Collectively, we constructed a broad-spectrum therapeutic biparatopic nanobody-peptide conjugate (NPC) extending coverage to SARS-CoV-2 VoCs RBDs. We PEGylated the biparatopic construct with 20kD maleimide-terminated PEG (MAL-(PEG)n-OMe) to improve its clinical efficacy limiting rapid renal clearance, and performed in silico cloning to facilitate future experimental studies. Our findings suggest that combining biparatopic nanobody conjugate with standard treatment may be a promising bivariate tool for combating viral entry during COVID-19 illness. 

/pdf/nanobody-peptide-conjugate-npc-for-passive-immunotherapy-12v2rzco.pdf

Nanobody-peptide-conjugate (NPC) for passive immunotherapy against SARS-CoV-2 variants of concern (VoC): a prospective pan-coronavirus therapeutics

Immunoinformatics Approach to Design Multi-Epitope- Subunit Vaccine against Bovine Ephemeral Fever Disease

Reverse vaccinology (RV) defines a mechanism of vaccine discovery with the identification of prominent antigenic peptides or epitopes in a reverse manner. It is an approach where the process starts from the roots of the organism, which are genomic and proteomic datasets of a pathogen, and involves the screening of all possible peptides to construct an efficient vaccine. RV comprises many in silico tools that can reduce the time and cost of vaccine development. These tools can analyze the immunogenicity, stability, heterologous population coverage, and the total efficacy of vaccine candidates. RV is utilized to design peptide or multiepitope vaccine as well as DNA vaccine, which exclude all the useless part of an antigen and only select the highly specific and targeted antigen that can generate a strong immune response in the host against a particular pathogen. Various mathematical simulations are utilized to understand the actual host-pathogen interactions and to enhance vaccine production. This chapter discusses the immunological mechanism of vaccines, the importance of different epitopes, methods, and tools, and the advantages of the peptide vaccines with the possible future perspectives. 

Immunoinformatics and reverse vaccinology methods to design peptide-based vaccines

Anti-IL-10 Antibody Humanization by SDR Grafting with Enhanced Affinity to Neutralize the Adverse Response of Interleukin-10

The overexpression of interleukin-13 (IL-13) leads to autoimmune and inflammatory diseases. These adverse responses can be neutralized by using lebrikizumab as a therapeutic monoclonal antibody (mAb). Herein, we have attempted to modulate the lebrikizumab mAb to enhance its binding affinity towards IL-13. The interface residues of the lebrikizumab-IL-13 complex were determined by the PyMOL and verified by the artificial neural network-based B-cell epitope prediction server (ABCpred server) and the Paratome web server. The Cologne University Protein Stability Analysis Tool (CUPSAT) web server based mutational approach was used to identify the stable and favorable interface mutations in the lebrikizumab. Only 40 mutations were selected to generate a single mutant library, and their binding affinity for IL-13 was analyzed by using the Z-Dock server. Based on high Z-score, mutants having a better affinity with IL-13 were selected to create a multi-mutant library. The multi-mutant library was again subjected to the Z-Dock server, and their binding affinity was determined. The highest-scoring ten mAb mutants were validated by using PatchDock and ClusPro servers. The best two potential mAb mutants were identified and subjected to molecular dynamics (MD) simulations to ensure its structural stability at the microscopic level. The changes in the different bonds as the effect of mutation were assessed by LigPlot + v2.1. The AllerTOP and ToxinPred web servers were used to analyze the non-allergic and nontoxic nature of the selected mutants. Therefore, these redesigned mAb could be used for potential treatment against IL-13 associated diseased conditions.Communicated by Ramaswamy H. Sarma.

Immunoglobulin interface redesigning to enhance lebrikizumab mediated immunomodulation of IL-13 hyper-response.

SARS-CoV-2 and its variants cause serious health concerns throughout the world. The alarming increase in the daily number of cases has become a nightmare in many low-income countries; although some vaccines are available, their high cost and low vaccine production make them unreachable to ordinary people in developing countries. Other treatment strategies are required for novel therapeutic options. The peptide-based drug is one of the alternatives with low toxicity, more specificity, and ease of synthesis. Herein, we have applied structure-based virtual screening to identify potential peptides targeting the critical proteins of SARS-CoV-2. Non-toxic natural antiviral peptides were selected from the enormous number of peptides. Comparative modeling was applied to prepare a 3D structure of selected peptides. 3D models of the peptides were docked using the ClusPro docking server to determine their binding affinity and peptide-protein interaction. The high-scoring peptides were docked with other crucial proteins to analyze multiple targeting peptides. The two best peptides were subjected to MD simulations to validate the structure stability and evaluated RMSD, RMSF, Rg, SASA, and H-bonding from the trajectory analysis of 100 ns. The proposed lead peptides can be used as a broad-spectrum drug and potentially develop as a therapeutic to combat SARS-CoV-2, positively impacting the current pandemic.The online version contains supplementary material available at 10.1007/s11224-022-02113-9. 

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Vinita Sharma

Papers

Immunoinformatics Approach to Design Multi-Epitope- Subunit Vaccine against Bovine Ephemeral Fever Disease

Immunoinformatics and reverse vaccinology methods to design peptide-based vaccines

Anti-IL-10 Antibody Humanization by SDR Grafting with Enhanced Affinity to Neutralize the Adverse Response of Interleukin-10

Immunoglobulin interface redesigning to enhance lebrikizumab mediated immunomodulation of IL-13 hyper-response.

Identification of multi-targeting natural antiviral peptides to impede SARS-CoV-2 infection