Prediction of B cell and T-helper cell epitopes candidates of bovine leukaemia virus (BLV) by in silico approach

An immunoinformatics study reveals a new BoLA-DR-restricted CD4+ T cell epitopes on the Gag protein of bovine leukemia virus

Bovine leukemia virus (BLV) is the causative agent of enzootic bovine leucosis (EBL), which has been reported worldwide. The expression of viral structural proteins: surface glycoprotein (gp51) and three core proteins - p15 (matrix), p24 (capsid), and p12 (nucleocapsid) induce a strong humoral and cellular immune response at first step of infection. CD4+ T-cell activation is generally induced by bovine leukocyte antigen (BoLA) region- positive antigen-presenting cells (APC) after processing of an exogenous viral antigen. Limited data are available on the BLV epitopes from the core proteins recognized by CD4+ T-cells. Thus, immunoinformatic analysis of Gag sequences obtained from 125 BLV isolates from Poland, Canada, Pakistan, Kazakhstan, Moldova and United States was performed to identify the presence of BoLA-DRB3 restricted CD4+ T-cell epitopes. The 379 15-mer overlapping peptides spanning the entire Gag sequence were run in BoLA-DRB3 allele-binding regions using a BoLA-DRB- peptide binding affinity prediction algorithm. The analysis identified 22 CD4+ T-cell peptide epitopes of variable length ranging from 17 to 22 amino acids. The predicted epitopes interacted with 73 different BoLA-DRB3 alleles found in BLV-infected cattle. Importantly, two epitopes were found to be linked with high proviral load in PBMC. A majority of dominant and subdominant epitopes showed high conservation across different viral strains, and therefore could be attractive targets for vaccine development.

Inferring linear-B cell epitopes using 2-step metaheuristic variant-feature selection using genetic algorithm

Linear-B cell epitopes (LBCE) play a vital role in vaccine design; thus, efficiently detecting them from protein sequences is of primary importance. These epitopes consist of amino acids arranged in continuous or discontinuous patterns. Vaccines employ attenuated viruses and purified antigens. LBCE stimulate humoral immunity in the body, where B and T cells target circulating infections. To predict LBCE, the underlying protein sequences undergo a process of feature extraction, feature selection, and classification. Various system models have been proposed for this purpose, but their classification accuracy is only moderate. In order to enhance the accuracy of LBCE classification, this paper presents a novel 2-step metaheuristic variant-feature selection method that combines a linear support vector classifier (LSVC) with a Modified Genetic Algorithm (MGA). The feature selection model employs mono-peptide, dipeptide, and tripeptide features, focusing on the most diverse ones. These selected features are fed into a machine learning (ML)-based parallel ensemble classifier. The ensemble classifier combines correctly classified instances from various classifiers, including k-Nearest Neighbor (kNN), random forest (RF), logistic regression (LR), and support vector machine (SVM). The ensemble classifier came up with an impressively high accuracy of 99.3% as a result of its work. This accuracy is superior to the most recent models that are considered to be state-of-the-art for linear B-cell classification. As a direct consequence of this, the entire system model can now be utilised effectively in real-time clinical settings.

Peptide Vaccines as Therapeutic and Prophylactic Agents for Female-Specific Cancers: The Current Landscape

Breast and gynecologic cancers are significant global threats to women's health and those living with the disease require lifelong physical, financial, and social support from their families, healthcare providers, and society as a whole. Cancer vaccines offer a promising means of inducing long-lasting immune response against the disease. Among various types of cancer vaccines available, peptide vaccines offer an effective strategy to elicit specific anti-tumor immune responses. Peptide vaccines have been developed based on tumor associated antigens (TAAs) and tumor specific neoantigens which can also be of viral origin. Molecular alterations in HER2 and non-HER2 genes are established to be involved in the pathogenesis of female-specific cancers and hence were exploited for the development of peptide vaccines against these diseases, most of which are in the latter stages of clinical trials. However, prophylactic vaccines for viral induced cancers, especially those against Human Papillomavirus (HPV) infection are well established. This review discusses therapeutic and prophylactic approaches for various types of female-specific cancers such as breast cancer and gynecologic cancers with special emphasis on peptide vaccines. We also present a pipeline for the design and evaluation of a multiepitope peptide vaccine that can be active against female-specific cancers.

Component-Resolved Diagnosis Based on a Recombinant Variant of Mus m 1 Lipocalin Allergen

Exposure to the Mus m 1 aeroallergen is a significant risk factor for laboratory animal allergy. This allergen, primarily expressed in mouse urine where it is characterized by a marked and dynamic polymorphism, is also present in epithelium and dander. Considering the relevance of sequence/structure assessment in protein antigenic reactivity, we compared the sequence of the variant Mus m 1.0102 to other members of the Mus m 1 allergen, and used Discotope 2.0 to predict conformational epitopes based on its 3D-structure. Conventional diagnosis of mouse allergy is based on serum IgE testing, using an epithelial extract as the antigen source. Given the heterogeneous and variable composition of extracts, we developed an indirect ELISA assay based on the recombinant component Mus m 1.0102. The assay performed with adequate precision and reasonable diagnostic accuracy (AUC = 0.87) compared to a routine clinical diagnostic test that exploits the native allergen. Recombinant Mus m 1.0102 turned out to be a valuable tool to study the fine epitope mapping of specific IgE reactivity to the major allergen responsible for mouse allergy. We believe that advancing in its functional characterization will lead to the standardization of murine lipocalins and to the development of allergen-specific immunotherapy.

T-B cell epitope peptides induce protective immunity against Mycoplasma pneumoniae respiratory tract infection in BALB/c mice

Mycoplasma pneumoniae is the most common pathogen of community-acquired pneumonia in humans. Due to its high rates of antibiotic resistance, vaccination has become the best method to control the dissemination of M. pneumoniae. The recombinant carboxyl terminus of the P1 (P1C) protein is an immunodominant antigen, but it has negative effects such as poor stability and lower purity. In the current study, T-B epitopes of the P1C protein were predicted according to bioinformatics analysis and assessed for efficacy in peptide vaccination. BALB/c mice were subcutaneously inoculated with the T-B epitope peptides four times and then infected with M. pneumoniae through the respiratory tract. The results showed that the T-B epitope peptides of the P1C protein (P1C_103-117, P1C_155-169, P1C_224-238 and P1C_244-258) induced strong antigen-specific serum antibody responses and cellular immune responses with high levels of serum IgG, IgA antibodies and Th1-biased (IFN-γ and IL-2) cytokines. Immunization with T-B epitope peptides significantly reduced the M. pneumoniae burden and the degree of inflammation in the challenged mice. Furthermore, the levels of IFN-γ and TNF-α in the supernatants of lung homogenates were observably reduced compared to those in the PBS group. Overall, our findings demonstrate that T-B epitopes (P1C_103-117, P1C_155-169, P1C_224-238 and P1C_244-258) play significant roles in the P1C protein and can be used to induce powerful humoral and cellular immune responses to provide significant protection against M. pneumoniae pulmonary infection, which provides new insight into the design of potential multiepitope vaccines to prevent host infection by M. pneumoniae.

Prediction of B cell and T-helper cell epitopes candidates of bovine leukaemia virus (BLV) by in silico approach

The bovine leukaemia virus (BLV) is a retrovirus responsible for enzootic bovine leukaemia (EBL) disease, the most common cattle disease leading to high annual economic losses to the cattle breeding industry. Virus monitoring among the sheep and cattle herds is usually done by vaccination. Inactivated virus vaccines can partially protect the livestock from viral challenge. However, vaccinated animals are likely to be infected. So, there is an essential need for producing vaccine by other methods. Gp60 SU, encoded by Env gene, is the surface glycoprotein of BLV detected to be the major target for the host immunity against the virus. Different stages were performed to predict the potential B and T-helper cell epitopes. The general framework of the method includes retrieving the amino acid sequence of gp60 SU, conducting the sequence alignment, getting the entropy plot, retrieving the previously found epitopes, predicting the hydropathy parameters, modelling the tertiary structure of the glycoprotein, minimizing the structure energy, validating the model by Ramachandran plot, predicting the linear and discontinuous epitopes by various servers and eventually choosing the consensus immunogenic regions. Ramachandran plot scrutiny has demonstrated that the modelled prediction is accurate and suitable. By surveying overlaps of various results, 4 and 2 immunogenic regions were selected as linear and conformational epitopes respectively. Amino acids 35-53, 67-97, 288-302 and 410-421 and those of numbers 37-58 and 72-100 were the regions selected as linear and conformational epitopes respectively. The tertiary structure of the final epitope was modelled as well. A comparison of the predicted epitopes structure with that of gp60 SU envelope, illustrated that the tertiary structure of these epitopes does not change after being separated from the primary complete one. The present achievements will lead to a better interpretation of the antigen-antibody interactions against gp60 in the designing process of safe and efficient vaccines.