An antigen display system of GEM nanoparticles based on affinity peptide ligands

Proof of concept in utilizing the peptidoglycan skeleton of pathogenic bacteria as antigen delivery platform for enhanced immune response

Subunit vaccines are becoming increasingly important because of their safety and effectiveness. However, subunit vaccines often exhibit limited immunogenicity, necessitating the use of suitable adjuvants to elicit robust immune responses. In this study, we demonstrated for the first time that pathogenic bacteria can be prepared into a purified peptidoglycan skeleton without nucleic acids and proteins, presenting bacterium-like particles (pBLP). Our results showed that the peptidoglycan skeletons screened from four pathogens could activate Toll-like receptor1/2 receptors better than bacterium-like particles from Lactococcus lactis in macrophages. We observed that pBLP was safe in mouse models of multiple ages. Furthermore, pBLP improved the performance of two commercial vaccines in vivo. We confirmed that pBLP successfully loaded antigens onto the surface and proved to be an effective antigen delivery platform with enhanced antibody titers, antibody avidity, balanced subclass distribution, and mucosal immunity. These results indicate that the peptidoglycan skeleton of pathogenic bacteria represents a new strategy for developing subunit vaccine delivery systems.

A New Advanced Approach: Design and Screening of Affinity Peptide Ligands Using Computer Simulation Techniques

Peptides acquire target affinity based on the combination of residues in their sequences and the conformation formed by their flexible folding, an ability that makes them very attractive biomaterials in therapeutic, diagnostic, and assay fields. With the development of computer technology, computer-aided design and screening of affinity peptides has become a more efficient and faster method. This review summarizes successful cases of computer-aided design and screening of affinity peptide ligands in recent years and lists the computer programs and online servers used in the process. In particular, the characteristics of different design and screening methods are summarized and categorized to help researchers choose between different methods. In addition, experimentally validated sequences are listed, and their applications are described, providing directions for the future development and application of computational peptide screening and design.

Use of tree-based machine learning methods to screen affinitive peptides based on docking data

Screening peptides with good affinity is an important step in peptide-drug discovery. Recent advancement in computer and data science have made machine learning a useful tool in accurately affinitive-peptide screening. In current study, four different tree-based algorithms, including Classification and regression trees (CART), C5.0 decision tree (C50), Bagged CART (BAG) and Random Forest (RF), were employed to explore the relationship between experimental peptide affinities and virtual docking data, and the performance of each model was also compared in parallel. All four algorithms showed better performances on dataset pre-scaled, -centered and -PCA than other pre-processed dataset. After model re-built and hyperparameter optimization, the optimal C50 model (C50O) showed the best performances in terms of Accuracy, Kappa, Sensitivity, Specificity, F1, MCC and AUC when validated on test data and an unknown PEDV datasets evaluation (Accuracy=80.4 %). BAG and RFO (the optimal RF), as two best models during training process, did not performed as expecting during in testing and unknown dataset validations. Furthermore, the high correlation of the predictions of RFO and BAG to C50O implied the high stability and robustness of their prediction. Whereas although the good performance on unknown dataset, the poor performance in test data validation and correlation analysis indicated CARTO could not be used for future data prediction. To accurately evaluate the peptide affinity, the current study firstly gave a tree-model competition on affinitive peptide prediction by using virtual docking data, which would expand the application of machine learning algorithms in studying PepPIs and benefit the development of peptide therapeutics.

Bacterium-like particles derived from probiotics: progress, challenges and prospects

Bacterium-like particles (BLPs) are hollow peptidoglycan particles obtained from food-grade Lactococcus lactis inactivated by hot acid. With the advantage of easy preparation, high safety, great stability, high loading capacity, and high mucosal delivery efficiency, BLPs can load and display proteins on the surface with the help of protein anchor (PA), making BLPs a proper delivery system. Owning to these features, BLPs are widely used in the development of adjuvants, vaccine carriers, virus/antigens purification, and enzyme immobilization. This review has attempted to gather a full understanding of the technical composition, characteristics, applications. The mechanism by which BLPs induces superior adaptive immune responses is also discussed. Besides, this review tracked the latest developments in the field of BLPs, including Lactobacillus-derived BLPs and novel anchors. Finally, the main limitations and proposed breakthrough points to further enhance the immunogenicity of BLPs vaccines were discussed, providing directions for future research. We hope that further developments in the field of antigen delivery of subunit vaccines or others will benefit from BLPs.

CCHFV vaccine development, current challenges, limitations, and future directions

Crimean-Congo hemorrhagic fever (CCHF) is the most prevalent tick-borne viral disease affecting humans. The disease is life-threatening in many regions of the developing world, including Africa, Asia, the Middle East, and Southern Europe. In line with the rapidly increasing disease prevalence, various vaccine strategies are under development. Despite a large number of potential vaccine candidates, there are no approved vaccines as of yet. This paper presents a detailed comparative analysis of current efforts to develop vaccines against CCHFV, limitations associated with current efforts, and future research directions.

A Random Forest Model for Peptide Classification Based on Virtual Docking Data

The affinity of peptides is a crucial factor in studying peptide-protein interactions. Despite the development of various techniques to evaluate peptide-receptor affinity, the results may not always reflect the actual affinity of the peptides accurately. The current study provides a free tool to assess the actual peptide affinity based on virtual docking data. This study employed a dataset that combined actual peptide affinity information (active and inactive) and virtual peptide-receptor docking data, and different machine learning algorithms were utilized. Compared with the other algorithms, the random forest (RF) algorithm showed the best performance and was used in building three RF models using different numbers of significant features (four, three, and two). Further analysis revealed that the four-feature RF model achieved the highest Accuracy of 0.714 in classifying an independent unknown peptide dataset designed with the PEDV spike protein, and it also revealed overfitting problems in the other models. This four-feature RF model was used to evaluate peptide affinity by constructing the relationship between the actual affinity and the virtual docking scores of peptides to their receptors.

Design and Identification of a Novel Antiviral Affinity Peptide against Fowl Adenovirus Serotype 4 (FAdV-4) by Targeting Fiber2 Protein

Outbreaks of hydropericardium hepatitis syndrome caused by fowl adenovirus serotype 4 (FAdV-4) with a novel genotype have been reported in China since 2015, with significant economic losses to the poultry industry. Fiber2 is one of the important structural proteins on FAdV-4 virions. In this study, the C-terminal knob domain of the FAdV-4 Fiber2 protein was expressed and purified, and its trimer structure (PDB ID: 7W83) was determined for the first time. A series of affinity peptides targeting the knob domain of the Fiber2 protein were designed and synthesized on the basis of the crystal structure using computer virtual screening technology. A total of eight peptides were screened using an immunoperoxidase monolayer assay and RT-qPCR, and they exhibited strong binding affinities to the knob domain of the FAdV-4 Fiber2 protein in a surface plasmon resonance assay. Treatment with peptide number 15 (P15; WWHEKE) at different concentrations (10, 25, and 50 μM) significantly reduced the expression level of the Fiber2 protein and the viral titer during FAdV-4 infection. P15 was found to be an optimal peptide with antiviral activity against FAdV-4 in vitro with no cytotoxic effect on LMH cells up to 200 μM. This study led to the identification of a class of affinity peptides designed using computer virtual screening technology that targeted the knob domain of the FAdV-4 Fiber2 protein and may be developed as a novel potential and effective antiviral strategy in the prevention and control of FAdV-4.

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 K_D 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.

Precise Assembly of Multiple Antigens on Nanoparticles with Specially Designed Affinity Peptides

Antigen proteins, assembled on nanoparticles, can be recognized by antigen-presenting cells effectively to enhance antigen immunogenicity. The ability to simultaneously display multiantigens on the same nanoparticle could have numerous applications but remained technical challenges. Here, we described a method for precise assembly of multiple antigens on nanoparticles with specially designed affinity peptides. First, we designed and screened affinity peptides with high affinity and specificity, which could respectively target the key amino acid residues of classical swine fever virus (CSFV) E2 protein or porcine circovirus type 2 capsid protein (PCV2 Cap) accurately. Then, we conjugated the antigen proteins to poly(lactic acid-glycolic acid) copolymer (PLGA) and Gram-positive enhancer matrix (GEM) nanoparticles through the peptides and perfectly assembled two kinds of multiantigen display nanoparticles with different particle sizes. Subsequently, the immunological properties of the assembled nanoparticles were tested. The results showed that the antigen display nanoparticles could promote the maturation, phagocytosis, and proinflammatory effects of antigen-presenting cells (APCs). Besides, compared with the antigen proteins, multiantigen display nanoparticles could induce much higher levels of antibodies and neutralizing antibodies in mice. This strategy may provide a technical support for the study of protein structure and the research and development of polyvalent vaccines.

The Development of Classical Swine Fever Marker Vaccines in Recent Years

Classical swine fever (CSF) is a severe disease that has caused serious economic losses for the global pig industry and is widely prevalent worldwide. In recent decades, CSF has been effectively controlled through compulsory vaccination with a live CSF vaccine (C strain). It has been successfully eradicated in some countries or regions. However, the re-emergence of CSF in Japan and Romania, where it had been eradicated, has brought increased attention to the disease. Because the traditional C-strain vaccine cannot distinguish between vaccinated and infected animals (DIVA), this makes it difficult to fight CSF. The emergence of marker vaccines is considered to be an effective strategy for the decontamination of CSF. This paper summarizes the progress of the new CSF marker vaccine and provides a detailed overview of the vaccine design ideas and immunization effects. It also provides a methodology for the development of a new generation of vaccines for CSF and vaccine development for other significant epidemics.