Identification of B-Cell Linear Epitopes in the Nucleocapsid (N) Protein B-Cell Linear Epitopes Conserved among the Main SARS-CoV-2 Variants.

The Nucleocapsid (N) protein is highlighted as the main target for COVID-19 diagnosis by antigen detection due to its abundance in circulation early during infection. However, the effects of the described mutations in the N protein epitopes and the efficacy of antigen testing across SARS-CoV-2 variants remain controversial and poorly understood. Here, we used immunoinformatics to identify five epitopes in the SARS-CoV-2 N protein (N(34-48), N(89-104), N(185-197), N(277-287), and N(378-390)) and validate their reactivity against samples from COVID-19 convalescent patients. All identified epitopes are fully conserved in the main SARS-CoV-2 variants and highly conserved with SARS-CoV. Moreover, the epitopes N(185-197) and N(277-287) are highly conserved with MERS-CoV, while the epitopes N(34-48), N(89-104), N(277-287), and N(378-390) are lowly conserved with common cold coronaviruses (229E, NL63, OC43, HKU1). These data are in accordance with the observed conservation of amino acids recognized by the antibodies 7R98, 7N0R, and 7CR5, which are conserved in the SARS-CoV-2 variants, SARS-CoV and MERS-CoV but lowly conserved in common cold coronaviruses. Therefore, we support the antigen tests as a scalable solution for the population-level diagnosis of SARS-CoV-2, but we highlight the need to verify the cross-reactivity of these tests against the common cold coronaviruses.

Computational design of a broad-spectrum multi-epitope vaccine candidate against seven strains of human coronaviruses.

Spike (S) proteins are an attractive target as it mediates the binding of the SARS-CoV-2 to the host through ACE-2 receptors. We hypothesize that the screening of the S protein sequences of all the seven known HCoVs would result in the identification of potential multi-epitope vaccine candidates capable of conferring immunity against various HCoVs. In the present study, several machine learning-based in-silico tools were employed to design a broad-spectrum multi-epitope vaccine candidate targeting the S protein of seven known strains of human coronaviruses. Herein, multiple B-cell epitopes and T-cell epitopes (CTL and HTL) were predicted from the S protein sequences of all seven known HCoVs. Post-prediction they were linked together with an adjuvant to construct a potential broad-spectrum vaccine candidate. Secondary and tertiary structures were predicted and validated, and the refined 3D-model was docked with an immune receptor. The vaccine candidate was evaluated for antigenicity, allergenicity, solubility, and its ability to achieve high-level expression in bacterial hosts. Finally, the immune simulation was carried out to evaluate the immune response after three vaccine doses. The designed vaccine is antigenic (with or without the adjuvant), non-allergenic, binds well with TLR-3 receptor and might elicit a diverse and strong immune response. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-022-03286-0.

Development a novel multiepitope DNA vaccine against human SARS coronavirus-2: an immunoinformatic designing study

Human SARS coronavirus 2 (SARS-CoV-2) causes the current global COVID-19 pandemic. The production of an efficient vaccine against COVID-19 is under heavy investigation. In this study, we have designed a novel multiepitope DNA vaccine against SARS-CoV-2 using reverse vaccinology and DNA vaccine approaches. Applying these strategies led to reduce the time and costs of vaccine development and also improve the immune protective characteristics of the vaccine. For this purpose, epitopes of nucleocapsid, membrane glycoprotein, and ORF8 proteins of SARS-CoV-2 chose as targets for B and T-cell receptors. Accordingly, DNA sequences of selected epitopes have optimized for protein expression in the eukaryotic system. To this end, the Kozak and tissue plasminogen activator sequences were added into the epitope sequences for proper protein expression and secretion, respectively. Furthermore, interleukin-2 and beta-defensin 1 preproprotein sequences were incorporated to the designed DNA vaccine as an adjuvant. Modeling and refinement of fused protein composed of SARS-CoV-2 multiepitope antigens (fuspMA) have performed based on homology modeling of orthologous peptides, then constructed 3D model of fuspMA was more investigated during 50 ns of molecular dynamics simulation. Further bioinformatics predictions demonstrated that fuspMA is a stable protein with acceptable antigenic features and no allergenicity or toxicity characteristics. Finally, the affinity of fuspMA to the MHC I and II and TLRs molecules validated by the molecular docking procedure. In conclusion, it seems the designed multiepitope DNA vaccine could have a chance to be introduced as an efficient vaccine against COVID-19 after more in vivo evaluations. © TÜBÍTAK.

Immunoprophylaxis using polypeptide chimera vaccines plus adjuvant system promote Th1 response controlling the spleen parasitism in hamster model of visceral leishmaniasis.

In recent years, several advances have been observed in vaccinology especially for neglected tropical diseases (NTDs). One of the tools employed is epitope prediction by immunoinformatic approaches that reduce the time and cost to develop a vaccine. In this scenario, immunoinformatics is being more often used to develop vaccines for NTDs, in particular visceral leishmaniasis (VL) which is proven not to have an effective vaccine yet. Based on that, in a previous study, two predicted T-cell multi-epitope chimera vaccines were experimentally validated in BALB/c mice to evaluate the immunogenicity, central and effector memory and protection against VL. Considering the results obtained in the mouse model, we assessed the immune response of these chimeras inMesocricetus auratushamster, which displays, experimentally, similar pathological status to human and dog VL disease. Our findings indicate that both chimeras lead to a dominant Th1 response profile, inducing a strong cellular response by increasing the production of IFN-γ and TNF-α cytokines associated with a decrease in IL-10. Also, the chimeras reduced the spleen parasite load and the weight a correlation between protector immunological mechanisms and consistent reduction of the parasitic load was observed. Our results demonstrate that both chimeras were immunogenic and corroborate with findings in the mouse model. Therefore, we reinforce the use of the hamster as a pre-clinical model in vaccination trials for canine and human VL and the importance of immunoinformatic to identify epitopes to design vaccines for this important neglected disease.

Immunoinformatics Tools: A boon in vaccine Development Against Covid-19

The first evidences of SARS Covid 19 virus were reported from Labs in Wuhan, China's Hubei Province, at the end of 2019. It spread very quickly throughout China, leading in an epidemic and a global pandemic. A large population was affected and died due to the pandemic in 2019. It shares genetic similarities with SARS-CoV-2 and MERS-COV. The development of an effective SARS-CoV-2 vaccine is important for reducing COVID-19 deaths and giving immunological protection to the worldwide community. The lengthy and expensive process of vaccine production can be shortened by using immunoinformatics approaches. immunoinformatics tools such as Vaxijen, IEDB, NetCTL 1.2, PEP-FOLD etc have previously been used in reverse vaccinology for SARS-CoV-2 vaccine development in areas such as antigen selection, toxicity, predicting vaccine targets, allergenicity prediction and selection of MHC-I and II binding epitopes etc. In this review, we summarize some of the most useful immunoinformatics tools like vexijen, Bepipred 2.0, SVMTrip, FNepitope etc and their role in the development of covid 19 vaccines. The characteristics of such tools have been thoroughly reviewed, and which may provide experimental biologists with prediction insights that may enhance active research attempts to identify therapies for the infectious COVID-19 illness. © 2022 IEEE.

An Immunoinformatic Strategy to Develop New Mycobacterium tuberculosis Multi-epitope Vaccine.

Mycobacterium tuberculosis causes a life-threatening disease known as tuberculosis (TB). In 2021, tuberculosis was the second cause of death after COVID-19 among infectious diseases. Latent life cycle and development of multidrug resistance in one hand and lack of an effective vaccine in another hand have made TB a global health issue. Here, a multi-epitope vaccine have been designed against TB using five new antigenic protein and immunoinformatic tools. To do so, immunodominant MHC-I/MHC-II binding epitopes of Rv2346, Rv2347, Rv3614, Rv3615 and Rv2031 antigenic proteins have been selected using advanced computational procedures. The vaccine was designed by linking ten epitopes from the antigenic proteins and flagellin and TpD as adjuvant. Three-dimensional (3D) structure of the vaccine was modeled, was refined and was evaluated using bioinformatics tools. The 3D structure of the vaccine was docked into the toll-like-receptors (TLR3, 4, 8) to evaluate potential interaction between the vaccine and TLRs. Evaluation of immunological and physicochemical properties of the constructed vaccine have demonstrated the vaccine construct can induce significant humoral and cellular immune responses, the vaccine is non-allergenic and can be recognized by TLR proteins. The immunoinformatic results reported in the present study demonstrates that it is worth following the designed vaccine by experimental investigations.

EpiCurator: an immunoinformatic workflow to predict and prioritize SARS-CoV-2 epitopes.

The ongoing coronavirus 2019 (COVID-19) pandemic, triggered by the emerging SARS-CoV-2 virus, represents a global public health challenge. Therefore, the development of effective vaccines is an urgent need to prevent and control virus spread. One of the vaccine production strategies uses the in silico epitope prediction from the virus genome by immunoinformatic approaches, which assist in selecting candidate epitopes for in vitro and clinical trials research. This study introduces the EpiCurator workflow to predict and prioritize epitopes from SARS-CoV-2 genomes by combining a series of computational filtering tools. To validate the workflow effectiveness, SARS-CoV-2 genomes retrieved from the GISAID database were analyzed. We identified 11 epitopes in the receptor-binding domain (RBD) of Spike glycoprotein, an important antigenic determinant, not previously described in the literature or published on the Immune Epitope Database (IEDB). Interestingly, these epitopes have a combination of important properties: recognized in sequences of the current variants of concern, present high antigenicity, conservancy, and broad population coverage. The RBD epitopes were the source for a multi-epitope design to in silico validation of their immunogenic potential. The multi-epitope overall quality was computationally validated, endorsing its efficiency to trigger an effective immune response since it has stability, high antigenicity and strong interactions with Toll-Like Receptors (TLR). Taken together, the findings in the current study demonstrated the efficacy of the workflow for epitopes discovery, providing target candidates for immunogen development.

Multi-epitope vaccine against SARS-CoV-2 applying immunoinformatics and molecular dynamics simulation approaches.

COVID-19, caused by SARS-CoV-2, is severe respiratory illnesses leading to millions of deaths worldwide in very short span. The high case fatality rate and the lack of medical counter measures emphasize for an urgent quest to develop safe and effective vaccine. Receptor-binding domain (RBD) of spike protein of SARS-CoV-2 binds to the ACE2 receptor on human host cell for the viral attachment and entry, hence considered as a key target to develop vaccines, antibodies and therapeutics. In this study, immunoinformatics approach was employed to design a novel multi-epitope vaccine using RBD of SARS-CoV-2 spike protein. The potential B- and T-cell epitopes were selected from RBD sequence using various bioinformatics tools to design the vaccine construct. The in silico designed multi-epitope vaccine encompasses 146 amino acids with an adjuvant (human beta-defensin-2), which was further computationally evaluated for several parameters including antigenicity, allergenicity and stability. Subsequently, three-dimensional structure of vaccine construct was modelled and then docked with various toll-like receptors. Molecular dynamics (MD) study of docked TLR3-vaccine complex delineated it to be highly stable during simulation time and the stabilization of interaction was majorly contributed by electrostatic energy. The docked complex also showed low deformation and increased rigidity in motion of residues during dynamics. Furthermore, in silico cloning of the multi-epitope vaccine was carried out to generate the plasmid construct for expression in a bacterial system. Altogether, our study suggests that the designed vaccine candidate containing RBD region could provide the specific humoral and cell-mediated immune responses against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.