This in silico ZIKV vaccine was developed by utilizing a consensus sequence derived from the ZIKV sequences stored in the databank. From all ZIKV proteins, we isolated CD4+ and CD8+ T cell epitopes on the basis of predicted binding to human leukocyte antigen (HLA) class II and class I molecules, considering their ability to bind a wide array of HLA molecules (promiscuity), and their potential to trigger an immune response (immunogenicity). The addition of ZIKV Envelope protein's domain III to the construct facilitated the discovery of B cell epitopes. Adjuvants contributed to heightened immunogenicity. Different linkers were employed to connect the CD4+ and CD8+ T cell epitopes, EDIII, and the adjuvants. Several analyses, including antigenicity, population coverage, allergenicity, autoimmunity, and the secondary and tertiary structures of the vaccine, were subjected to scrutiny by employing various immunoinformatics tools and online web servers. We explain the protocols, their underlying rationale, and the detailed steps required for designing the in silico multi-epitope ZIKV vaccine in this chapter.Reverse vaccinology (RV) identifies potentially protective antigens from any organism's genomic information, analyzed in silico, to generate novel vaccine candidates against a variety of pathogens. This approach employs bioinformatics to survey the complete genomic sequence of a targeted pathogen, seeking epitopes that are most likely to trigger a robust immune response. In silico techniques allow for a dramatic reduction in the time and cost associated with vaccine identification, enhancing the otherwise tedious selection process for antigens that would be impossible to isolate or cultivate using conventional approaches. Vaccine identification against serogroup B meningococcus (MenB), Bacillus anthracis, Streptococcus pneumonia, Staphylococcus aureus, Chlamydia pneumoniae, Porphyromonas gingivalis, Edwardsiella tarda, and Mycobacterium tuberculosis has seen the successful implementation of RV methodologies. Through an in-depth analysis of RV techniques, Influenza A virus will be examined as a case study.Structure-based vaccine design (SBVD), a significant computational vaccine design approach, leverages structural information of a targeted protein to produce novel vaccine candidates. The capacity to rapidly model the structural details of proteins and antibodies has significantly expanded the scientific community's inventory of potential vaccine targets, thus fostering novel avenues for future vaccine discoveries. This chapter offers a thorough examination of the state of in silico SBVD, along with an exploration of the current obstacles and restrictions. A case study regarding the design of COVID-19 vaccines, concentrating on the SARS-CoV-2 spike protein, provides an example of key strategies within the field of SBVD.Scientific advancements, particularly in genomic data, computational tools, software, databases, and machine learning, have resulted in the emergence of immunoinformatics, an efficient technique for immunologists to design potential vaccines swiftly. Numerous tools and databases are readily accessible for the purpose of screening parasite/pathogen genome sequences, allowing the identification of highly immunogenic peptides or epitopes suitable for effective vaccine development. This chapter introduces a readily applicable protocol for constructing multi-epitope subunit vaccines. Although computational immunoinformatics-based strategies effectively create potential vaccine candidates expeditiously, pre-clinical laboratory testing is indispensable for evaluating their immunogenicity and safety before any clinical trial.Bioinformatics-driven reverse vaccinology (RV) represented a substantial advancement in vaccinology, extracting valuable features from protein sequences to inform the selection of potential vaccine candidates (Rappuoli, Curr Opin Microbiol 3(5)445-450, 2000). Pioneering work by Rino Rappuoli, first demonstrating its efficacy against serogroup B meningococcus, has led to its subsequent use in diverse bacterial vaccine formulations, with a corresponding evolution in the bioinformatics approaches employed. Based on our firsthand knowledge of RV applications and a broad review of the related literature, we have constructed a lean bioinformatic tool pipeline accessible to the public, which is elucidated in this contribution. The extracted protein features, discussed in this paper, can be input in a matrix format for machine learning-based analysis.Various immune signaling functions are fulfilled by the distinctive group of molecules known as interleukins. Immunoregulatory cytokine Interleukin 13 (IL13) is primarily manufactured by activated T-helper 2 cells, mast cells, and basophils. IL13's role in instigating allergic and autoimmune diseases is apparent in conditions like asthma, rheumatoid arthritis, systemic sclerosis, ulcerative colitis, heightened airway responsiveness, an increase in glycoprotein secretion, and an overabundance of goblet cells. In addition to its influence on various other disorders, IL13 directly hinders tumor immunosurveillance, ultimately promoting carcinogenesis. To design effective and secure protein-based vaccines and therapies, precisely determining IL13-inducing peptides or regions within a protein is essential, considering their role in multiple diseases. IL13pred serves as an in silico tool, facilitating the identification, prediction, and design of IL13-inducing peptides. The IL13pred web server, along with its standalone software, is accessible at the following link: (https://webs.iiitd.edu.in/raghava/il13pred/).The pro-inflammatory cytokine, interleukin 6 (IL6), is central to both innate and adaptive immune processes. In prior research, a significant body of studies pointed to a correlation between high interleukin-6 (IL-6) levels and the proliferation of cancer, the onset of autoimmune disorders, and the characteristic cytokine storm in COVID-19 patients. Consequently, accurate identification and removal of antigenic regions from therapeutic proteins or vaccine candidates that could induce IL6-associated immunotoxicity is critically important. This difficulty was overcome by our team by developing IL6pred, a computational tool, in order to discover IL6-inducing peptides within a vaccine candidate. A key objective of this chapter is to articulate the methodological underpinnings and applications of IL6pred. The IL6pred webserver and standalone package (https://webs.iiitd.edu.in/raghava/il6pred/), through its prediction, design, and scanning modules, provides illuminating insights.The process of vaccine development is both intricate and lengthy. Clinical trials, along with computational studies, experimental analyses, and studies on animal models, comprise the comprehensive process. To accelerate this process, in silico antigen screening can be utilized to pinpoint prospective vaccine candidates. Employing a deep learning methodology, this chapter outlines a technique that uses 18 biological and 9154 physicochemical protein properties to pinpoint prospective vaccine candidates. This innovative technique spurred the development of a new online system, called Vaxi-DL, which proved beneficial in uncovering new vaccine prospects from sources like bacteria, protozoa, viruses, and fungi. The platform providing Vaxi-DL is accessible at the link https://vac.kamalrawal.in/vaxidl/.A key element in reverse vaccinology-driven vaccine development is the prediction of effective bacterial immunogens. chk signals Employing in silico methods for protein analysis within a bacterial species drastically cuts down on time and expense when searching for vaccine candidates. The prediction algorithm entails collecting protein sequence datasets of known bacterial immunogens and non-immunogens, processing the data to convert protein sequences into numerical matrices suitable for machine learning training and testing, and deriving predictive models. The derived models' performance is quantified through the use of classification metrics. The protocol's framework for model development explains the methodology, from initial data collection and manipulation to the subsequent training and validation of the models.Initiating an adaptive immune response hinges on the formation of major histocompatibility (MHC) - peptide - T cell receptor (TCR) complexes. The formation of these complexes begins with a short peptide binding to the MHC, initiating a stabilizing effect. A subsequent engagement of the TCR then completes the process, forming a ternary complex. Crucially, these interactions are predominantly localized to the complementarity-determining region (CDR) loops of the TCR. A central component of cancer immunotherapy is the stimulation of an immune reaction. Identifying the optimal combinations of MHC molecules, peptides, and TCRs is essential for this approach to trigger an anti-tumor immune response. This prediction poses a current, significant problem in the field of computational biochemistry. This chapter details a predictive method, generating multiple peptide and TCR CDR3 loop conformations. These conformations are solvated within the MHC-peptide-TCR ternary complex, parameters extracted, and an AI model used to evaluate the resulting ternary complex's capacity for supporting an immune response.Major histocompatibility complexes (MHC), in all jawed vertebrates, are pivotal components of the immune surveillance system. Randomly sampling cytosolic peptides from the cellular interior is a function of MHC class I molecules, while MHC class II molecules target peptides from outside the cell. Both types of peptide-MHC complex are then exhibited on the cell surface, enabling recognition by the T cells, including CD8+ and CD4+ types. The intricate three-dimensional structures within such complexes provide essential information concerning the procedures of presentation and recognition. For this purpose, the PANDORA software, among others, has been crafted to generate peptide-MHC (pMHC) 3D structures with great rapidity and accuracy. The PANDORA protocol's operation is explained within this chapter. Structural knowledge about MHC molecule anchor pockets, used for peptide docking, is employed by Pandora. The modeling process benefits from PANDORA's use of anchor positions as restraints for direction.