An In Silico Deep Learning Approach to Multi-Epitope Vaccine Design: A Hepatitis E Virus Case Study

Construction of an immunoinformatics-based multi-epitope vaccine candidate targeting Kyasanur forest disease virus

Kyasanur forest disease (KFD) is one of the neglected tick-borne viral zoonoses. KFD virus (KFDV) was initially considered endemic to the Western Ghats region of Karnataka state in India. Over the years, there have been reports of its spread to newer areas within and outside Karnataka. The absence of an effective treatment for KFD mandates the need for further research and development of novel vaccines. The present study was designed to develop a multi-epitope vaccine candidate against KFDV using immunoinformatics approaches. A total of 74 complete KFDV genome sequences were analysed for genetic recombination followed by phylogeny. Computational prediction of B- and T-cell epitopes belonging to envelope protein was performed and epitopes were prioritised based on IFN-Gamma, IL-4, IL-10 stimulation and checked for allergenicity and toxicity. The eight short-listed epitopes (three MHC-Class 1, three MHC-Class 2 and two B-cell) were then combined using various linkers to construct the vaccine candidate. Molecular docking followed by molecular simulations revealed stable interactions of the vaccine candidate with immune receptor complex namely Toll-like receptors (TLR2-TLR6). Codon optimization followed by in-silico cloning of the designed multi-epitope vaccine construct into the pET30b (+) expression vector was carried out. Immunoinformatics analysis of the multi-epitope vaccine candidate in the current study has potential to significantly accelerate the initial stages of vaccine development. Experimental validation of the potential multi-epitope vaccine candidate remains crucial to confirm effectiveness and safety in real-world conditions.

Development of a novel bivalent vaccine candidate against hepatitis A virus and rotavirus using reverse vaccinology and immunoinformatics

The hepatitis A virus (HAV) and rotavirus are mainly transmitted through fecal-oral and person-to-person contact, and cause severe gastrointestinal complications and liver disease. This work used reverse vaccinology and immunoinformatic methods to create a novel bivalent vaccine against rotavirus and HAV. The amino acid sequences of HAV-rotavirus proteins (VP1 and VP8∗) were retrieved from the GenBank database. Various computational approaches were employed to predict highly conserved regions and the most immunogenic B-cell and T-cell epitopes of VP8 and VP1 of rotavirus and HAV proteins in both humans and BALB/c. Moreover, the predicted fusion protein was analyzed regarding primary and secondary structures and homology validation. In this study, we used two highly conserved peptide sequences of VP8 and VP1 of rotavirus and HAV that induce T and B cell immunogenicity. According to T-cell epitope prediction, this area comprises 2713 antigenic peptides for HLA class II and 30 HLA class I antigenic peptides, both of which are virtually entirely conserved in the Iranian population. In this study, validation as well as analysis of the secondary and three-dimensional structure of the VP8∗-rotavirus + AAY + HAV-VP1 fusion protein, with the aim of designing a multi-epitope vaccine with different receptors. TLR 3, 4 high immunogenic binding ability with immunological properties and interaction between multi-epitope target and TLR were predicted, and it is expected that the target fusion protein has stable antigenic potency and compatible half-life. The above is suggested as a universal vaccination program.

Computational design of multi-epitope vaccine against Hepatitis C Virus infection using immunoinformatics techniques

Hepatitis C Virus (HCV) is a blood borne pathogen that affects around 200 million individuals worldwide. Immunizations against the Hepatitis C Virus are intended to enhance T-cell responses and have been identified as a crucial component of successful antiviral therapy. Nevertheless, attempts to mediate clinically relevant anti-HCV activity in people have mainly failed, despite the vaccines present satisfactory progress. In this study, we used an array of immunoinformatics approaches to design a multiepitope peptide-based vaccine against HCV by emphasizing 6 conserved epitopes from viral protein NS5B. The potential epitopes were examined for their possible antigenic combination with each other along with GPGPG linkers using structural modeling and epitope-epitope interaction analysis. An adjuvant (β-defensin) was introduced to the N-terminus to increase the immunogenicity of the vaccine construct. Molecular dynamics simulation discloses the most stable structure of the proposed vaccine. The designed vaccine is potentially antigenic in nature and can form stable and significant interaction with both receptors TLR2 and TLR3. The vaccine construct was also subjected to In-Silico cloning which confirmed its expression efficiency in a vector. The findings indicate that the designed multi-epitope vaccine have a great potential for preclinical and clinical research, which is an important step in addressing the problems related to HCV infection.

In silico vaccine design for Yersinia enterocolitica: A comprehensive approach to enhanced immunogenicity, efficacy and protection

Yersinia enterocolitica, a foodborne pathogen, has emerged as a significant public health concern due to its increased prevalence and multidrug resistance. This study employed reverse vaccinology to identify novel vaccine candidates against Y. enterocolitica through comprehensive in silico analyses. The core genome's conserved protein translocase subunit SecY was selected as the target, and potential B-cell, MHC class I, and MHC class II epitopes were mapped. 3B-cell epitopes, 3 MHCI and 11 MHCII epitopes were acquired. A multi-epitope vaccine construct was designed by incorporating the identified epitopes, TLR4 Agonist was used as adjuvants to enhance the immunogenic response. EAAAK, CPGPG and AYY linkers were used to form a vaccine construct, followed by extensive computational evaluations. The vaccine exhibited desirable physicochemical properties, stable secondary and tertiary structures as evaluated by PDBSum and trRosetta. Moreover, favorable interactions with the human Toll-like receptor 4 (TLR4) was observed by ClusPro. Population coverage analysis estimated the vaccine's applicability across 99.74 % in diverse populations. In addition, molecular dynamics simulations and normal mode analysis confirmed the vaccine's structural stability and dynamics in a simulated biological environment. Furthermore, codon optimization and in silico cloning facilitated the evaluation of the vaccine's expression potential in E. coli and pET-28a was used a recombinant plasmid. This study provides a promising foundation for the development of an efficacious vaccine against Y. enterocolitica infections.

Sustainable integration of artificial intelligence and machine learning approaches within the African infectious disease vaccine research and development ecosystem

Artificial Intelligence and Machine Learning (AI/ML) techniques, including reverse vaccinology and predictive models, have already been applied for developing vaccine candidates for COVID-19, HIV, and Hepatitis, streamlining the vaccine development lifecycle from discovery to deployment. The application of AI and ML technologies for improving heath interventions, including drug discovery and clinical development, are expanding across Africa, particularly in South Africa, Kenya, and Nigeria. Further initiatives are required however to expand AI/ML capabilities across the continent to ensure the development of a sustainable ecosystem including enhancing the requisite knowledge base, fostering collaboration between stakeholders, ensuring robust regulatory and ethical frameworks and investment in requisite infrastructure.

Design and validation of a novel multi-epitopes vaccine against hantavirus

Hantavirus is a member of the order Bunyavirales and an emerging global pathogen. Hantavirus infections have affected millions of people globally based on available epidemiological data and research studies. Hemorrhagic fever with renal syndrome (HFRS) and hantavirus pulmonary syndrome (HPS) are the two main human diseases associated with hantavirus infections. Hence, efforts are required to develop a potent vaccine against the pathogen. The only vaccine that is in use for hantavirus is an inactivated virus vaccine, "Hantavax", but it failed to produce neutralizing antibodies. Vaccine development is of much importance in dealing with the surge of hantavirus globally. In this study, hantavirus five proteins (N protein, G1 and G2, L protein, and non-structural proteins) were used in NetCTL 1.2 program to predict T-cell epitopes. To predict major histocompatibility complex (MHC) binding alleles, an immune epitope database (IEDB) was used. All predicted epitopes were then investigated for different immunoinformatics analyses such as antigenicity and toxicity analyses. The good water-soluble, non-toxic, probable antigenic, and DRB*0101 binder was selected. A multi-epitopes-based vaccine designing was then done where linkers were used to connect the shortlisted epitopes. In addition, an adjuvant molecule was supplementary to the multi-epitopes peptide to improve the vaccine's immunogenic potential. The final vaccine construct's three-dimensional structure was modeled by ab initio method. The vaccine molecule was then evaluated for its binding potential with TLR-3 immune receptor, which is key for its recognition and processing by the host immune system. Docking studies were performed using HADDOCK software. The best-docked complex was selected and visualized for intermolecular binding and interactions using UCSF Chimera 1.16 software. The findings revealed that the designed vaccine might be a potential vaccine against hantavirus and can be used in experimental animal model testings.Communicated by Ramaswamy H. Sarma.

The Three-Dimensional Reference Interaction Site Model Approach as a Promising Tool for Studying Hydrated Viruses and Their Complexes with Ligands

Viruses are the most numerous biological form living in any ecosystem. Viral diseases affect not only people but also representatives of fauna and flora. The latest pandemic has shown how important it is for the scientific community to respond quickly to the challenge, including critically assessing the viral threat and developing appropriate measures to counter this threat. Scientists around the world are making enormous efforts to solve these problems. In silico methods, which allow quite rapid obtention of, in many cases, accurate information in this field, are effective tools for the description of various aspects of virus activity, including virus-host cell interactions, and, thus, can provide a molecular insight into the mechanism of virus functioning. The three-dimensional reference interaction site model (3D-RISM) seems to be one of the most effective and inexpensive methods to compute hydrated viruses, since the method allows us to provide efficient calculations of hydrated viruses, remaining all molecular details of the liquid environment and virus structure. The pandemic challenge has resulted in a fast increase in the number of 3D-RISM calculations devoted to hydrated viruses. To provide readers with a summary of this literature, we present a systematic overview of the 3D-RISM calculations, covering the period since 2010. We discuss various biophysical aspects of the 3D-RISM results and demonstrate capabilities, limitations, achievements, and prospects of the method using examples of viruses such as influenza, hepatitis, and SARS-CoV-2 viruses.

Multi-epitope vaccine design for hepatitis E virus based on protein ORF2 and ORF3

Introduction

Hepatitis E virus (HEV), with heightened virulence in immunocompromised individuals and pregnant women, is a pervasive threat in developing countries. A globaly available vaccine against HEV is currently lacking.

Methods

We designed a multi-epitope vaccine based on protein ORF2 and ORF3 of HEV using immunoinformatics.

Results

The vaccine comprised 23 nontoxic, nonallergenic, soluble peptides. The stability of the docked peptide vaccine-TLR3 complex was validated by molecular dynamic simulations. The induction of effective cellular and humoral immune responses by the multi-peptide vaccine was verified by simulated immunization.

Discussion

These findings provide a foundation for future HEV vaccine studies.

Innovative translational platforms for rapid developing clinical vaccines against COVID-19 and other infectious disease

A vaccine is a biological preparation that contains the antigen capable of stimulating the immune system to form the defense against pathogens. Vaccine development often confronts big challenges, including time/energy-consuming, low efficacy, lag to pathogen emergence and mutation, and even safety concern. However, these seem now mostly conquerable through constructing the advanced translational platforms that can make innovative vaccines, sometimes, potentiated with a distinct multifunctional VADS (vaccine adjuvant delivery system), as evidenced by the development of various vaccines against the covid-19 pandemic at warp speed. Particularly, several covid-19 vaccines, such as the viral-vectored vaccines, mRNA vaccines and DNA vaccines, regarded as the innovative ones that are rapidly made via the high technology-based translational platforms. These products have manifested powerful efficacy while showing no unacceptable safety profile in clinics, allowing them to be approved for massive vaccination at also warp speed. Now, the proprietary translational platforms integrated with the state-of-the-art biotechnologies, and even the artificial intelligence (AI), represent an efficient mode for rapid making innovative clinical vaccines against infections, thus increasingly attracting interests of vaccine research and development. Herein, the advanced translational platforms for making innovative vaccines, together with their design principles and immunostimulatory efficacies, are comprehensively elaborated.