Deep learning in next-generation vaccine development for infectious diseases

Abstract

The landscape of vaccine development was changed in the genomic era with the help of computer science. Computer-aided vaccine epitope selection has become a foundation of rational vaccine design. Similarly, artificial intelligence (AI) is quickly transforming the vaccine development landscape. Deep learning (DL), a subset of AI, is used in the landscape of vaccine development in terms of its algorithms, tools, and technologies. This review article discussed the developmental history of the modern era of vaccine development strategies using both immunoinformatics with DL models, identification strategies of T cell epitopes and B cell epitopes through immunoinformatics and DL models, vaccine constructs development strategies using linker and adjuvant, and characterization strategies of vaccine construct using bioinformatics and immunoinformatics. Similarly, the article discusses different tools and technologies, from epitope mapping and vaccine construct development to characterization. Again, it also highlighted recent paradigm shifts, DL-based strategies in vaccine development, and different DL-based tools used for epitope mapping and vaccine construct development. However, integrated frameworks connecting the bioinformatics and DL approaches are rapidly progressing, which are necessary for DL-assisted epitope prediction and the subsequent steps for vaccine development. DL-assisted vaccine development is rapid and cost-effective, changing the scenario of next-generation vaccine development very fast.

Graphical abstract

Bhattacharya and colleagues demonstrated the diverse strategies of immunoinformatics with DL models targeted for identifying T cell and B cell epitopes and vaccine development. They also highlighted the integrated frameworks connecting bioinformatics and DL-assisted vaccine development, and recent paradigm shifts that support fast, cost-effective next-generation vaccine development.

Introduction

In recent years, deep learning (DL) has emerged as a pivotal computational paradigm, a subset of machine learning (ML), earning recognition as the gold standard for solving complex cognitive and predictive tasks. DL uses artificial neural networks (ANNs) with multiple layers, called deep neural networks (DNNs), to understand complex patterns from data, extract features automatically, and perform predictions.^1,2 The rapid advancement of DL models has enabled them to achieve exceptional performance, often surpassing human capabilities in various domains.² DL is a sophisticated subset of ML, which has witnessed remarkable growth, particularly in healthcare and biomedical research. DL models leverage high-performance computing techniques to process and analyze extensive datasets, extracting valuable patterns and features that may not be immediately discernible through traditional computational approaches. These models can incorporate diverse data formats, including medical imaging (e.g., MRI and computed tomography [CT] scans), electronic health records (EHRs), genomic data, and time-series datasets. Among the most potent DL architectures are convolutional neural networks (CNNs), DNNs, and object detection models such as YOLO (You Only Look Once). In particular, DNNs have demonstrated immense potential by learning hierarchical data representations through multiple hidden layers, making them indispensable for complex classification and pattern recognition tasks.³

The DL has gained significant recognition within and beyond the scientific community, demonstrating remarkable achievements across multiple fields. It has been instrumental in surpassing human expertise in strategic games, achieving autonomous driving capabilities comparable to human drivers and contributing to discovering new mathematical proofs.^4,5,6,7 One of its most impactful applications lies in computational vaccine design, where ML is crucial in identifying vaccine targets. This article examines how ML can support key computational processes in rational vaccine design, particularly in detecting B and T cell epitopes and identifying correlates of protection. Various ML models are discussed, highlighting the data they utilize and the types of predictions they generate.

Traditional vaccine development started by culturing infectious pathogens, isolating the whole pathogen, and inactivating it or some of its purified components. Following the genomics revolution, vaccine research and development started with structural vaccinology, which uses different computational tools and techniques and is also called reverse vaccinology (RV).^8,9 Rino Rappuoli first defined computational vaccine development in 2000. This field uses genetic information of pathogens as a starting point in vaccine design. At the same time, computational systems have been applied to study the immune system. Immunoinformatics was used to start applying in different areas of vaccine development. It was used in antigenic epitope selection, T cell and B cell epitope identification, ranking the antigenic epitopes through scoring, etc.^10,11 Immunoinformatics was used in different types of vaccine development to fight against different pathogens such as SARS-CoV-2 to Helicobacter pylori.^{12,13,14,15,16} However, recently, with the development of computer science, a paradigm shift has been noted in the vaccine development field. Several ML and DL-based algorithms, tools, and techniques have helped to develop faster methods of epitope selection and immune simulation using big data sets, and several vaccines have been developed from time to time (Figure 1).

Figure 1.

Timeline showed the paradigm shift in vaccine development using DL applications and advanced vaccine development

Interpretable ML offers a valuable tool for advancing immunogen identification by uncovering the molecular mechanisms that drive vaccine-induced immune responses.¹⁷ Among DL’s most notable breakthroughs is its contribution to structural biology, particularly in solving the protein folding challenge. It is now widely employed to design antibodies without requiring extensive laboratory experimentation. The increasing availability of immune repertoire sequencing data has paralleled the advancement of DL, enabling predictions of immune response specificity and potential disease outcomes solely from sequencing information. Additionally, DL facilitates the phylogenetic analysis of global viral variants, examining mutation patterns and their effects on immune responses, pathogen adaptability, and population-level susceptibility.¹⁸

Furthermore, DL has been leveraged to model antibody-antigen interactions using data from directed evolution studies. These studies involve analyzing libraries of antibody sequences subjected to iterative diversification techniques such as error-prone PCR, and then selecting variants exhibiting enhanced antigen binding. Early DL applications in this field primarily focused on improving antigen-binding molecule selection through phage display experiments.^19,20

Algorithms offer several advantages in next-generation vaccine development. DL models or algorithms aid in surveilling pathogen evolution and personalized vaccine strategies. Despite data quality and interpretability challenges, DL promises to improve vaccine efficacy, reduce development timelines, and address emerging infectious diseases. The continued integration of artificial intelligence (AI) and biological research holds promise for more rapid, effective, and personalized vaccine solutions in the future.

In this review article, we discussed developmental history of the modern era of vaccine development strategies using immunoinformatics and both immunoinformatics with DL models from identification strategies of T cell and B cell epitopes to vaccine development and its characterization. Again, we also highlighted recent paradigm shifts and DL-based strategies in vaccine development, different DL-based tools used for epitope mapping and vaccine construct development. The review broadly highlights key applications, advancements, and directions for DL to revolutionize vaccine development.

Evolution of DL

In recent years, DL has drawn increasing attention and gained great success in many real-world applications. DL was developed from different foundational works. It evolved from ANNs and ML. In 1965, DL like algorithms with multiple layers of non-linear features were outlined by Ivakhnenko and Lapa. The researchers used thin but deep models and neural networks-based polynomial activation functions. DL generally uses the DNN model to solve learning problems, including prediction and classification. Geoffrey Hinton coined the term “deep learning” in 2006. The rise of DL was fueled by improved computational power and the development of more sophisticated algorithms from time to time. Several researchers have suggested EC (evolutionary computation) algorithms to optimize DL, which is called evolutionary DL.²¹ It shows encouraging results for researchers. However, it has been noted that DL is a data-hungry method. Therefore, vast amounts of data are needed to train the models for DL models.^22,23 However, Rather et al. recently indicated that DL techniques could be performed using small datasets also.²⁴ In recent times, DL has been used immensely in different biological and medical science areas. Several DL-based algorithms and models have been developed for the use of in this direction. Similarly, DL algorithms have shown results in excellent next-generation, epitopes-based vaccine development this decade. Therefore, the increasing use of these DL algorithms has been noted in vaccine development.

Developmental history of the modern era of vaccine development strategies using immunoinformatics and both immunoinformatics with DL models as integrated frameworks

If we look back, the development of traditional vaccines began by culturing and isolating the pathogen, inactivating it, or using some of its components. In this modern era, vaccine development started with immunoinformatics using genomic information of the pathogen. Still, all the tools and technologies have shifted and changed especially AI-based ML/DL-based technologies. Rino Rappuoli first defined “reverse vaccinology” in 2000, describing in-silico or computational tools and techniques that use genomic information.^11,24 First, during the early stage of reverse vaccinology researchers used only immunoinformatics. Simultaneously, there has been massive progress in AI-enabled technologies in different biological and medical science domains. Then, during the paradigm shift in medical science and molecular biology with computer science, vaccine researchers are using immunoinformatics and DL-based models as integrated frameworks for vaccine development. Presently, all these in-silico approaches are merging with ML or DL models for vaccine development. One such strategy uses in-silico DL approaches to predict and design a multi-epitope vaccine called DeepVacPred.²⁵ Similarly, IntegralVac was developed using an ML-based vaccine design using a multivalent epitope.²⁶ DL represents a transformative tool in vaccine development, offering a more precise, data-driven approach to designing vaccines. By leveraging DL models, researchers can accelerate the development of vaccines against emerging pathogens, contributing to global efforts in infectious disease prevention and control.

As DL gradually integrates into biological research, it prompts discussions about its potential role in advancing vaccine development, particularly against viral infections.¹⁸ These pathogens pose a significant challenge due to their ability to mutate continuously and the emergence of new zoonotic diseases transmitted from animals to humans. Although antiviral treatments, such as those for HIV-1, vaccines remain the most effective tool for disease prevention.¹⁸ Some success stories include eradicating smallpox and polio through vaccine development, which might show the future path for vaccine development and vaccination. Similarly, the success stories of COVID-19 vaccine development with the highest speed during the pandemic might also show the path to vaccine researchers.^27,28

However, creating safe and efficient viral vaccines is a highly complex process marked by more obstacles than achievements. The rise of DL and big data suggest a new frontier in vaccine research, with the potential for transformative breakthroughs in public health. Specifically, advancements in protein structure prediction, immune system related analysis, and phylogenetics are expected to enhance vaccine development efforts.¹⁸

New methods have been initiated the vaccine research more easier with the arrival of informatics and computers. Immunoinformatics uses computational and mathematical approaches to tackle various immunological issues. Several immunoinformatics methods have been created and utilized since the 1980s to identify T cell and B cell immune epitopes.²⁹ When the entire genome of the pathogenic bacterium influenza was released in 1995, a breakthrough in vaccine research was found.³⁰ The in-silico selection of vaccination targets has been made possible by the bioinformatics-based analysis of the microbial genome data, which has occurred concurrently with developments in sequencing technologies and molecular biology. Numerous novel vaccine design algorithms have been developed due to further developments in immunoinformatics. Immunome-derived vaccine design or RV are the two terms to describe this unique vaccine development approach.³¹ The first use of this approach, called “reverse vaccinology,” was implemented in making vaccines for serogroup B Neisseria meningitides (MenB).³² In the 1980s, DeLisi, Berzofsky, and others developed the first immunoinformatics-based tool to create vaccine candidates.³³ Epitope-mapping algorithms are one of the most essential informatics tools necessary for vaccine construction. Due to the linear linkage of the T cell epitopes with the human leukocyte antigen, it is possible to accurately describe the interface between the T cells and the ligands. As a result, numerous T cell epitope-mapping algorithms are developed.^34,35 With these techniques, it is possible to quickly identify probable T cell epitopes by initiating the process with a pathogen’s whole proteome. Such knowledge is highly beneficial for researching the pathophysiological landscape of infectious diseases, developing novel vaccines, and performing diagnostic procedures.^{29,36,37,38,39,40,41,42} Rino Rappuoli used the term “RV” in 2000 to describe the process of developing vaccines using genetic information as an initiating point rather than the pathogen’s activity.^42,43 The RV method failed to identify potential vaccine candidates such as polysaccharides or glycolipids. Therefore, it was quickly applied to pathogens with smaller genomes. The first successful vaccine created using this approach was for the serogroup B Neisseria meningitidis, which causes meningitis and sepsis in young adults and children.^11,44 Afterwords, DNA microarrays and proteomics were added to the original RV methodology to support genome mining for vaccine discovery.^45,46 The analysis of the surface proteome of group A Streptococci led to the identification of vaccine candidates⁴⁷ and was considered as “next chapter of reverse vaccinology.”⁴⁸ By 2006, it was clear that analyzing multiple strains of a pathogen’s genome was essential for creating universal vaccines, particularly for bacteria, as demonstrated with group B Streptococci.⁴⁹ A new challenge suggested that scientists combine vaccinology with structural biology.⁵⁰ Since these early studies, computational medicine’s goals have advanced to unimaginable levels.⁵¹ The accuracy in molecular dynamics simulations (MDS),³⁴ DL methods for predicting peptide folding,⁵² and extensive databases carefully curated to make it simple to access information about the structure and function of a protein have all revolutionized the development of potential vaccine candidates.⁵¹ Numerous vaccines based on multiple epitopes have been formulated and created to protect against viruses and cancers. Some examples of vaccines against different pathogens are viruses (such as coronavirus,^53,54 HIV-1,^55,56,57 HPV,⁵⁸ Ebola,^59,60 and Zika⁶¹), bacteria (including H. pylori^62,63 and Mycobacterium tuberculosis^64,65), and cancers such as breast cancer.^66,67 It has been noted that all the recent vaccine development efforts use DL-based tools and techniques that use DL algorithms. It helps vaccine researchers develop fast and next-generation vaccines.

Identification strategies of T cell epitopes through immunoinformatics and DL models

The epitopes recognizing by T cell are called T cell epitopes. T cell epitopes are specific short peptides derived from antigens. When presented through the major histocompatibility complex (MHC) molecules, TCRs (T cell receptors) determine these epitopes. It is crucial for the immune response mechanism.⁶⁸

The prediction process of T cell epitopes is associated with determining the peptide-binding particularity of specific class I or class II MHC alleles. Then, in silico, the prediction of epitopes was performed. Peptide sequence data have been used to construct many T cell epitope prediction algorithms.⁶⁹

MHC class I represents peptides to cytotoxic T cells and CD8⁺ T cells, which might kill infected cells. These peptides or epitopes are typically 8–11 amino acids long.⁷⁰ On the other hand, MHC class II represents longer peptides to helper T cells or CD4⁺ T cells, which help coordinate the immune response. These peptides or epitopes are typically 12–25 amino acids long, with a nine amino acid core.⁷¹

Identifying T cell epitopes is associated with computational or immunoinformatics methods, now prevalent vaccine research techniques. These methods can pre-predict and analyze the parts of a protein that can be recognized by T cells, an essential segment of the adaptive immune system. Immunoinformatics methods for T cell epitope identification generally contain antigen sequence retrieval of pathogen from databases such as GenBank or UniProt. MHC binding affinity prediction has been performed using different methods such as matrix-based methods, ANNs, support vector machines (SVMs), proteasomal processing prediction (for MHC class I), transfer associated protein (TAP) transport prediction (for MHC class I), and immunogenicity prediction.¹⁴

Immunoinformatic tools are used in T cell epitope identification and vaccine design, such as IEDB (comprehensive database of epitopes), NetMHC, NetCTL, NetMHCpan, SYFPEITHI, RANKPEP, EpiMatrix, ProPred, and ProPred1.^72,73 Some DL models are Deepitope, TRAP, iTCep, iTCep, etc. which are using for vaccine research. The DL has advantages in T cell epitope identification, including improved accuracy, handling large datasets, and pan-specificity (Figure 2). This DL models are highly capable of automatically extracting relevant features from data. Immunogenic epitopes are extracted from the peptide sequence using the feature extraction properties (Figure 3). Other advantages include identifying novel epitopes and integrating multiple data sources.

Figure 2.

Application of DL for epitope prediction, through the improved accuracy, handling large datasets, and pan-specificity

Figure 3.

DL feature extraction properties have been used for potential immunogenic epitopes extraction or epitopes mapping from the amino acids sequences

Identification strategies of B cell epitopes through immunoinformatics and DL models

B cell epitopes are the specific regions on an antigen that are recognized and bound by antibodies (B cell receptors or secreted antibodies). Identifying these epitopes is crucial for various applications, including vaccine design. Identifying B cell epitopes through immunoinformatics generally involves linear (continuous) epitopes and conformational (discontinuous) epitope identification.^74,75 Key immunoinformatics tools and databases for B cell epitope prediction are IEDB, ABCpred, and LBtope.⁷⁵ Immunoinformatics provides a valuable and increasingly sophisticated toolkit for identifying B cell epitopes. It provides high-throughput screening, cost-effective identification of B cell epitopes, and prioritization of candidates.⁷⁶ ML and DL algorithms have significantly advanced the B cell epitope prediction with general immunoinformatics models. ML models use SVMs and random forest algorithms. Liu et al. use DL methods to analyze 214,679 non-epitopes and 25,884 linear B cell epitope predictions and developed a model the models as DLBEpitope.⁷⁷

DL architectures use CNNs, recurrent neural networks, and hybrid architectures. The advantages of ML and DL in B cell epitope prediction are improved accurateness’s, automated feature learning, and specificity.

Vaccine construct development strategies using DL and immunoinformatics

Due to the susceptibility of antigenic peptides to degradation by proteases within the body, the immune cell receptors struggle to recognize antigen epitopes, resulting in a weak immune response toward pathogens. An effective epitope-based vaccine requires a well-designed combination of epitope peptides, a delivery mechanism, and an adjuvant to address this issue.⁷⁸ Adjuvants have multiple functions, such as immunostimulating properties, good carrier, and proper delivery of vaccines. Cox and Coulter identified five ways in which vaccine adjuvants work. These include (1) altering the cytokine network, which is called immunomodulation; (2) inducing a response from cytotoxic T lymphocytes (CTLs); (3) generating a temporary or permanent depot to provide a continuous or intermittent release of the antigen; (4) preserving the antigen’s structure and presenting it to the immune cells; and (5) delivering the antigen to immune cells through antigen-presenting cells.⁷⁹ The construction process utilizes three essential components to develop an effective vaccine capable of inducing an immune response. These components include linker sequences, adjuvants, and suitable B and T cell epitopes. Moreover, molecules with immunomodulatory capabilities were introduced to the vaccine constructs as secure vaccine adjuvants to enhance the immune system’s potential.⁸⁰ Adjuvants for vaccinations are typically researched alongside vaccines since they are a crucial component of a vaccine construct. The adjuvants for vaccines, however, are produced separately and can be examined for various reasons. Vaccine adjuvants, for instance, are frequently delivered to the host organisms in vivo or in vitro to analyze their produced immune responses and negative consequences. In general, the domain of informatics that includes the vaccine adjuvant tackles scientific issues apart from those about the entire vaccine or the specific components of the vaccine constructs, such as vaccine antigens.⁷⁷ The adjuvants can be classified according to their physical or chemical properties, source (natural, endogenous, or synthetic), or mechanism of action. Adjuvants should be selected depending on the immune responses from the host cell. They should be combined with an antigenic epitope to produce the best immune response possible with the fewest adverse effects. Major adjuvant categories include polymeric microsphere adjuvants, bacteria-derived adjuvants (gram-negative bacteria), liposome adjuvants, cytokines, tentative adjuvants (Quil A), and adjuvant emulsions. Other mineral salt categories include zirconium, calcium, and iron. Some molecular adjuvants in our bodies include C3d, IgGFc, and granulocyte-macrophage colony-stimulating factor.^81,82,83 For instance, alum adjuvants are thought to trigger primarily prime Th2-type immunological responses and the pro-inflammatory NLPR3 pathway. To combat this, researchers have paired alum along with PRR-based adjuvants, including toll-like receptor agonists, to synergistically boost the immunogenicity property of a vaccine antigen and elicit an optimal equilibrium in the Th1/Th2 immune response that is long-lasting against the viral infections, namely SARS-CoV-2, and the other evolved variants. Furthermore, it is recognized that CD4⁺ T cells play a crucial role in coordinating the body’s defense against tumors and in initiating and sustaining the activities of CD8⁺ T cells. By incorporating CD4⁺ T cell epitopes in peptide vaccines, immune responses have been boosted.^84,85 Adding a universal T helper epitope, such as the pan DR-binding epitope (PADRE), significantly increased the antibody immune responses triggered by a malaria recombinant antigen vaccine.⁸⁶ PADRE is a synthetic 13-amino acid peptide that is universal, activating CD4⁺ T cells.⁸⁷ Its high affinity for 15 of the 16 most prevalent human HLA-DR types enables it to elicit effective CD4⁺ T cell responses^87,88 and potentially address the issues caused by HLA-DR molecule polymorphism in the population.⁸⁹ In a proliferation assay, researchers noted that the PADRE was 100 times more effective than the universal T helper epitopes, such as the tetanus toxin-derived universal epitope.⁹⁰ Some emulsion adjuvants, AS03 and MF59, are safe and well tolerated in managing the COVID-19 pandemic. Additionally, employing emulsion adjuvants can be the safest strategy to minimize the doses of vaccines and increase immunization coverage to combat COVID-19.⁹¹

Combining different protein components to create fusion or chimeric proteins has become common in protein engineering and biotechnology. This technique is frequently utilized to produce soluble proteins and purify them. A fusion protein is created by connecting two distinct protein domains using a peptide linker.⁹² Linkers are essential for establishing conformational stability, separation of the functional domains, and folding proteins, all of which contribute to the stability of the protein structure.⁹³ Besides, molecular linkers can also alter the pharmacokinetic profile of a vaccine by increasing the expression yields.^92,94 The composition of the linker sequence has an impact on the fusion protein’s folding stability. A linker sequence with a high tendency to form α-helices or β-strands could restrict the protein’s flexibility and limit its functional activity, making a linker sequence that prefers to adopt an extended conformation more desirable. Currently, most designed linker sequences contain significant glycine residues, which induce the linker to take on a loop conformation.⁹⁵ An epitope-based vaccine is made up of multiple types of epitopes as opposed to only one. The vaccine construct is made by joining screened epitopes together through specific linkers, overcoming the drawbacks of single-peptide-based vaccinations, which cannot effectively elicit the host immune system against the variants of the same pathogen.⁹³ Oli and his colleagues stated that constructing a single/multi-epitope vaccine candidate utilizes the GPGPG and AAY linker peptides to link the different epitopes involved in the vaccine construction. Similarly, the EAAAK linker peptide links the epitopes with the adjuvant of the final vaccine construct.⁹⁶

Chakraborty et al. have also discussed that the overall stability of the multi-epitopic vaccine construct also depends on the appropriate use of linker peptides. This is one of the significant limitations of vaccine research involving immunoinformatics.⁹⁷ The use of these standard linker peptides has also been seen in the vaccine constructs developed by various groups of scientists. For example, Kar et al., Samad et al., and Naz et al. designed a multi-epitopic vaccine candidate against the SARS-CoV-2 virus in which the HTL (Helper T lymphocyte) and CTL epitopes are linked with the B cell epitopes using the GPGPG and AAY linkers, respectively.^98,99,100 The PEAK linker was used to link the adjuvant (50S ribosomal protein L7/L12) to the N-terminal of the final vaccine construct.^98,100 The design of linkers in fusion proteins is now more crucial than ever due to the quick development of biotechnology along with protein engineering. Future biomedical research will provide a complete understanding of linkers’ structures, conformations, and activities, which will substantially simplify the development of suitable recombinant proteins that will surely enhance their roles in next generation vaccines.⁹⁴

Characterization strategies of vaccine construct using bioinformatics and immunoinformatics

The usual ways to create vaccines are slow and expensive, taking between 5 and 15 years. A big challenge nowadays is to figure out which parts of a pathogen will stimulate the immune system the most.¹⁰¹ However, using computer-based immunoinformatics can help design vaccines quickly and cheaply. Although there is some progress, there is no agreed-upon approach to vaccine design. One promising method is epitope-based vaccines, which can provide preventive or therapeutic effects and have shown good results in targeting specific pathogens.¹⁰² Conventional vaccines, such as those made from inactive bacteria (such as pertussis) or weakened viruses (such as rabies, smallpox, mumps, measles, and polio), have successfully saved lives so far. However, these vaccines have been known to pose risks to certain individuals due to the possibility of the virus regaining its harmful qualities.^103,104,105 To overcome the drawbacks of traditional vaccines, scientists have proposed the development of modern vaccines. One such vaccine is the multi-epitope candidate, which has proven more effective in stimulating targeted and powerful immune responses while avoiding unwanted immune responses to harmful epitopes.^62,106 An advancement of bioinformatics along with computational simulation techniques has been vividly used to examine biological data and predict gene regulatory networks.⁷² It has been used well in all avenues of vaccine research, including preclinical, clinical, and post-vaccination phases.¹⁰⁷ Another branch of bioinformatics, known as immunoinformatics, utilizes a plethora of computational and mathematical models to develop and interpret immunological data, which helps make necessary predictions on immunity and the progression of diseases.^107,108 Designing effective vaccines against different viruses involves several crucial steps, including identifying T cell and B cell epitopes, analyzing antigen processing and antigenicity, assessing population coverage, evaluating allergenicity and toxicity, and evaluating protein-peptide docking and conservancy analysis. To carry out all these analyses, various bioinformatics tools, and online web servers have been created.¹⁰⁹ For the last two decades, a team of vaccine experts, bioinformatics specialists, and skilled programmers in Providence, Rhode Island, USA, have made significant progress in creating a comprehensive toolkit named iVAX for designing vaccines. iVAX is accessible through the internet and ensures user security. The toolkit comprises a range of immunoinformatics tools that can score and prioritize candidate antigens, choose immunogenic and persistent T cell epitopes, modify or eliminate regulatory T cell epitopes, and redesign antigens to trigger immunity and safeguard against diseases in livestock and humans.¹¹⁰ The immunogenic targets of multi-epitope and epitope vaccines are mostly amino acid residues. Potential B cell and T cell epitopes can be predicted to help with vaccine formulation, immunization modeling, and immunity protein analysis using different databases, and other computational tools.^{111,112,113,114,115} Several cutting-edge bioinformatics and immunoinformatics techniques are actively aiding research on the COVID-19 vaccine. The first SARS-CoV-2 vaccine involving the immunoinformatics technology was released in February 2020. This development was a breakthrough in developing COVID-19 vaccines.⁹⁷ The extensive immunoinformatics approach facilitates epitope identification and vaccine design for several serious diseases. Several researchers are working to develop a peptide-based vaccine candidate against the SARS-CoV-2 virus and to identify and define the various B cell and T cell epitopes using the immunoinformatics approach.¹⁴ RV refers to the employment of several immunoinformatics approaches in the development of vaccines.¹¹⁴ The construction of a multiepitope vaccine candidate using several antigenic structures has been expedited using this technique. Immunoinformatics has successfully advanced the creation of vaccines by identifying numerous previously unidentified antigens. Immunoinformatic methods are frequently used in RV to interpret various antigenic roles. Immunoinformatic techniques are a godsend in vaccine production since they help researchers better understand the biology of mutagenic antigens and pathogens. This next-generation technique could, therefore, address the difficulties encountered while addressing viruses with mutagenic antigens. Creating a multiepitope vaccination with either a regular antigen or a mutagenic antigen can offer protection against these infections.^24,115 The development of efficient and safe medicines and prophylactics, notably a vaccine to defend against the illness, is required due to the havoc COVID-19 pandemic caused by the SARS-CoV-2 virus, notably a public health emergency of global importance. The several functions the SARS-CoV-2 Spike glycoprotein performs in attachment, fusion, and entrance into the host cell make it a desirable candidate for creating antibodies, inhibitors, and vaccines. For instance, Saba Ismail and his colleagues used immunoinformatics approaches to analyze the SARS-CoV-2 Spike protein to identify possible B cell and T cell epitopes. Then, those epitopes were used to construct a multi-epitopic peptide vaccine construct. The strong immune simulation was seen in the multi-epitopic peptide vaccine and the significant synthesis of interleukins, immunoglobulins, and cytokines.¹¹⁶

Immunoinformatics tools and technologies: From epitope mapping and vaccine construct development to characterization

Immunoinformatics is the optimal approach for identifying potential vaccines for various pathogens, and it is essential to choose the most precise methods for predicting and creating effective therapeutic vaccines. To thoroughly assess the effectiveness of the computer-based vaccine design, in vitro and in vivo analysis is necessary to develop suitable vaccine candidates.¹⁰⁸ With bioinformatics, various servers and software are created to analyze immunological data, which can also help comprehend the workings of the immune system.¹¹⁷ Bioinformatics and immunoinformatics played a significant role in developing the COVID-19 vaccine construct and selecting the antigenic epitopes. Research on the COVID-19 vaccine has dramatically benefited from the use of immunoinformatics, vaccinogenomics, MDS, bioinformatics, and structural biology. The development of numerous vaccine designs employing immuno- and bio-informatics was noted.¹⁴ Some of the cutting-edge tools and technologies required for developing, characterizing, and validating dating a vaccine construct are discussed in the folowing text.

Tools and technologies used for epitope mapping and vaccine construct development

Understanding the T cell and the B cell epitope-mediated immunological responses has significantly increased. Understanding the structure and amino acid sequences of epitopes is aided by various databases, bioinformatics tools, and prediction algorithms. This information is essential for researching vaccines, basic fundamental immunological studies, and treating and diagnosing numerous diseases.¹¹⁸ Antigen-based B cell epitope prediction is helpful for medication and vaccine development and understanding the immunological basis of antibody-antigen interaction.¹¹⁹ In order to construct successful vaccine candidates against any infection, we must consider B cell epitopes. B cell epitopes, however, can be divided into two categories: linear or continuous and discontinuous. However, approximately 85% of the B cell epitopes are assumed to be continuous in sequence.¹²⁰ Some of the immunoinformatics tools used for identifying the linear B cell epitopes using the ML approach include BepiPred,¹²¹ ABCpred,¹²² LBtope,¹²³ BCPREDS,¹²⁴ and SVMtrip.¹²⁵ Additionally, the LBtope server has been a successful tool in predicting the linear B cell epitopes through the primary protein sequence of the antigen.¹²³ Similarly, some of the immunoinformatics tools that assist the vaccine research by predicting the discontinuous B cell epitopes using the structure-based methodology include CEP,¹²⁶ DiscoTope,¹²⁷ PEPITO,¹²⁸ SEPPA,¹²⁹ EPITOPIA,¹³⁰ EPSVR,¹³¹ EPIPRED,¹³² and PEASE.¹³³

Several immunoinformatics-based methods were developed to predict T cell epitopes.^69,134,135 T cell epitope prediction aims to locate the most minor peptides in an antigen that can activate CD4⁺ or CD8⁺ T cells.¹³⁶ All the T cell epitopes do not possess a high affinity for the MHC binders. MHC-peptide binding and the specific interaction of the MHC-peptide ligand with a particular TCR are necessary for a functioning T cell response. To describe the process of peptide binding to MHCs and TAP, which serve as T cell epitopes, we require well-characterized data from immunoinformatics.¹³⁷ Some of the immunoinformatics tools that created a breakthrough in predicting the T cell epitopes using the motif-matrix approach include Rankpep,¹³⁷ SYFPEITHI,¹³⁷ MAPPP,¹³⁸ PREDIVAC,¹³⁹ PEPVAC,¹⁴⁰ EPISOPT,¹⁴¹ and Vaxign.¹⁴² Other than the motif-matrix method, some of the immunoinformatics tools such as NetMHC,¹⁴³ NetMHCII,¹⁴⁴ NetMHCpan,¹⁴⁵ NetMHCIIpan,¹⁴⁶ nHLApred¹⁴⁷ work on the ANN methodology to predict the T cell epitopes. Apart from this, BIMAS,¹⁴⁸ Propred,¹⁴⁹ Propred-1,¹⁵⁰ and EpiGen¹⁵¹ utilize the quantitative-affinity matrix method to identify the T cell-specific antigens. Additionally, some immunoinformatics tools such as MHCPred¹⁵² and EpiTOP¹⁵³ utilize the quantitative structure-activity relationship model to predict the distinct T cell epitopes.

Tools and technologies used for the characterization and validation of the vaccine construct

A vaccine construct developed against any disease needs to be characterized and validated by several in-silico approaches. Those parameters include antigenicity, allergenicity, solubility, toxicity, secondary and tertiary structure prediction, etc. The domain of immunoinformatics has provided many tools and technologies that are of utmost importance for researchers in successfully characterizing and validating any vaccine construct. Some of the tools such as AllergenFP,¹⁵⁴ AllerTOP,¹⁵⁵ Allermatch,¹⁵⁶ APPEL,¹⁵⁷ and EVALLER¹⁵⁸ are used to predict the allergenicity of the developed vaccine construct against any of the infectious diseases. Moreover, Oli et al. stated that the VaxiJen tool could analyze the antigenicity and allergenicity of the final vaccine construct along with other physicochemical data. The AlgPred server can determine the vaccine’s antigenicity, and the ProtParam server can identify some of the physicochemical properties of the vaccines, namely half-life, average hydropathicity, molecular weight, isoelectric point, aliphatic index, and solubility.⁹⁶

Furthermore, the final vaccine construct’s validation, refinement, and structural remodeling can be predicted using the ProSA-web, Galaxy Refine Server, and the RaptorX/SOPMA server, respectively. This step is finally preceded by molecular dynamics and molecular docking studies between the epitopes and host receptor using the Desmond and ClusPro tools, respectively.¹⁵⁹ The CHARMM27 immunoinformatics tool can also recognize molecular simulation patterns. Other immunoinformatics tools also play a significant role in validating and characterizing the final vaccine construct. For instance, protein solubility is a significant feature, ranging from the manufacturing of recombinant proteins to the creation of bio-therapeutics. A web server named Protein-Sol is used to calculate the solubility index of the vaccine construct.¹⁶⁰ Further validation of the vaccine constructs includes in-silico cloning, usually performed using the Java Codon Adaptation Tool to evaluate the prepared vaccine candidate’s expression levels on specific hosts.¹⁶¹ One of the significant steps associated with developing epitope-based vaccines is tertiary-structure modeling of the vaccine construct.^162,163 The SPARKS-X web server is one of the promising options in this case. It utilizes the template search for the tertiary structure by implementing BLASTp.¹⁶⁴ Besides the tertiary structure, the secondary structure of the vaccine construct can also be predicted using the PSIPRED 4.0 web server, which uses the two feed-forward neural network-based algorithms for structure development.¹⁶⁵ The properties of pathogens have been evaluated using microbiological, serological, and biochemical techniques to pinpoint the parts that can be used to develop vaccines. Although effective in many instances, this method takes a lot of time and has certain limitations when the pathogens cannot be grown in a lab or when the most prevalent antigens have varied sequences.⁴³ The advancement in the field of immunoinformatics has created a significant breakthrough for researchers in order to design various vaccine candidates against some of the highly contagious diseases.

DL-based strategies in vaccine development: Recent paradigm shift

In recent years, DL has emerged as a pivotal computational paradigm within ML, earning recognition as the gold standard for solving complex cognitive and predictive tasks. The rapid advancement of DL models has enabled them to achieve exceptional performance, often surpassing human capabilities in various domains.²

The DL is a sophisticated subset of ML, has witnessed remarkable growth, particularly in healthcare and biomedical research. DL models leverage high-performance computing techniques to process and analyze extensive datasets, extracting valuable patterns and features that may not be immediately discernible through traditional computational approaches. These models can incorporate diverse data formats, including medical imaging (e.g., MRI and CT scans), EHRs, genomic data, and time-series datasets. Among the most potent DL architectures are CNNs, DNNs, transformer-based models, and deep reinforcement learning (Table 1). These DL-based models have occasionally originated and can be classified into three broad categories: supervised, semi-supervised, and unsupervised (Figure 4). All the DL models have inherent complexity, are well suited for complex patterns, and can involve large datasets (Figure 5). In particular, DNNs have demonstrated immense potential by learning hierarchical representations of data through multiple hidden layers, making them indispensable for complex classification and pattern recognition tasks.³

Table 1.

DL-based algorithms used for vaccine development

Sl. no.	Algorithms name	Remarks	References
1.	convolutional neural networks (CNNs)	these are mostly useful for analyzing biological sequences (nucleotide or protein) and identifying patterns that indicate potential vaccine targets for antigenicity determination	Chuang et al.166
2.	multi-layer perceptrons (MLPs)	the MLPs are used in several vaccine development tasks, including predicting the protein properties and identifying potential vaccine candidates	Conti et al.167
3.	bidirectional long short-term memory neural networks (BiLSTM)	this is used for sequence-based predictions in viral evolution and immune response modeling	Zeng et al.168
4.	recurrent neural networks (RNNs)	RNNs are well-suited for processing of sequential data, making them valuable for analyzing viral genomic data and predicting the viral mutations	Yin et al.169
5.	transformer-based models	this algorithm is used for protein sequence understanding and representation learning of potential epitopic part of amino acid chains	Ye et al.170
6.	deep neural networks (DNNs)	this algorithm is used for protein structure prediction (e.g., AlphaFold), immune repertoire analysis, and also for the prediction of vaccine efficacy	MacKerell et al.160
7.	graph neural networks (GNNs)	this is used for analyzing molecular interactions between antigens and immune system components. Subsequently, it helps in predicting vaccine candidates by modeling protein-protein interactions	Chinery et al.171
8.	autoencoders	this algorithm is applied for dimensionality reduction and feature extraction from biological data, such as identifying conserved viral epitopes by learning compact representations of viral sequences	Nasir et al.172
9.	generative adversarial networks	this algorithm is used for synthetic antigen generation and optimizing the vaccine formulations; it also designing novel protein sequences that mimic real viral antigens for better vaccine response	Surana et al.173
10.	deep reinforcement learning	mostly used for optimizing vaccine formulations, distribution, and adjuvant selection. It identifies the best combination of peptides to enhance immune response	Faris et al.174
11.	support vector machines (SVMs)	this algorithm is widely used in vaccine development, particularly in epitope prediction, antigen classification, and immune response modeling	Zhao et al.175

Figure 4.

The DL model classification and categorization into three broad categories: supervised, semi-supervised, and unsupervised

Figure 5.

The examples of collective DL model architectures, which include inherent complexity, are well suited for complex patterns and can involve large datasets

DL has garnered immense popularity across multiple scientific and industrial sectors, emerging as a crucial research area in AI, ML, and data science. Due to its ability to extract meaningful insights from vast amounts of data, DL has attracted considerable interest from major technology corporations, including Google, Microsoft, and Nokia, which actively invest in its research and development. These companies leverage DL to enhance decision-making processes, optimize predictive models, and improve automation in tasks such as object detection, recommendation systems, and real-time language translation.¹⁷⁶ Furthermore, DL serves as a crucial AI function mimicking human cognitive processes by learning complex representations from data, leading to widespread integration across industries. The increasing global recognition of DL’s transformative impact is well documented in recent literature.¹⁷⁷

One of the defining characteristics of DL is its ability to operate using various learning paradigms, including supervised, semi-supervised, and unsupervised learning strategies. This flexibility allows DL models to automatically extract hierarchical features from large-scale datasets, making them exceptionally well suited for applications in big data analysis. Studies have demonstrated that DL approaches effectively address complex problems ranging from autonomous systems and NLP (natural language processing) to biomedical diagnostics and financial forecasting. Some of the most prominent applications of DL include virtual assistants such as Alexa and Siri, facial recognition technologies, personalized recommendations, automatic handwriting generation, self-driving vehicles, news aggregation, black-and-white image colorization, silent film audio synthesis, pixel restoration, and the generation of synthetic imagery through deep dreaming techniques.¹⁷⁸

The backpropagation algorithm is at the core of DL’s success, a critical computational mechanism that enables ANNs to adjust their internal parameters based on error propagation. This iterative optimization process enhances the model’s ability to learn complex representations by refining the weights assigned to each neural connection across multiple layers. By leveraging backpropagation, DL models continuously improve their predictive accuracy, allowing them to discover intricate structures within massive datasets.³ The continued evolution of DL is expected to drive groundbreaking innovations across AI-driven applications, further solidifying its role as a cornerstone of modern computational intelligence.

Different DL-based tools used for epitope mapping and vaccine construct development

DL-based tools have been used occasionally, from epitope mapping to vaccine construct development. DL tools have been used for epitope prediction. Wohlwend et al. developed MUNIS, a DL-enabled predictor of CD8⁺ T cell epitopes (HLA-I epitopes) for vaccine development.¹⁷⁹ The DL-based tools have significantly contributed to vaccine development by improving the accuracy of antigen recognition, immune response, and protein structure modeling predictions (Table 2). Vaxi-DL¹⁸⁰ is a web-based server that predicts potential vaccine candidates, while MHCSeqNet¹⁸¹ and its advanced version, MHCSeqNet2¹⁸² utilize DNNs for MHC binding prediction. DeepVacPred²⁵ effectively identifies vaccine subunits from protein sequences. Tools such as ntegralVac²⁶ also screen peptide sequences for hemolytic potential. Protein structure modeling has been revolutionized by trRosetta¹⁸³ and the AlphaFold series,^184,185,186 which predict high-accuracy protein structures and their interactions. ProGen¹⁸⁷ generates artificial protein sequences across various families, while AminoBERT¹⁸⁸ determines structural information from unaligned proteins. IgFold¹⁸⁹ enhances antibody structure prediction using DL. In the immunology domain, DeepMHCII¹⁹⁰ improves MHC-peptide binding predictions, DeepTCR¹⁹¹ analyzes TCR sequences, and DeepImmuno¹⁹² simulates immunogenic peptides with high accuracy. DeepHLA¹⁹³ predicts T cell responses based on HLA-peptide interactions, further advancing immunotherapy and vaccine research. These tools collectively enhance the efficiency and precision of vaccine design and immunogenicity assessments.

Table 2.

DL-based tools/servers used for vaccine development

Sl. no.	Tools/servers name	Remarks	References
1.	Vaxi-DL	the web-based DL server that predicts potential vaccine candidates	Rawal et al.180
2.	MHCSeqNet	the deep neural network model based on NLP for accurate prediction of universal MHC binding	Phloyphisut et al.181
3.	DeepVacPred	the computational framework righty predicts potential vaccine subunits from the existing protein sequence	Yang et al.25
4.	HemoPI	the application used for prediction and screening of peptide sequences having hemolytic potency	Suri and Dakshanamurthy26
5.	MHCSeqNet2	using the sub-word-level peptide structures, a 3D structure for MHC alleles, with an extended training dataset for better generalizability on MHC alleles having low data quantity	Wongklaew et al.182
6.	trRosetta	the fast and accurate protein structure prediction tool used a deep residual-convolutional network	Du et al.183
7.	AlphaFold	the DL-based openly accessible, extensive database of high-accuracy protein-structure predictions	Varadi et al.184
8.	AlphaFold-2	the DL-based program used to understand accurate 3D of structure protein, dynamics, and functions	Laurents185
9.	AlphaFold-3	it used for the structure modeling of proteins and their interactions, allowing a massive range of applications in protein modeling and designing	Abramson et al.186
10.	ProGen	the conditional protein language model able to generates various artificial protein sequences across the protein families on the basis of input control tags	Madani et al.187
11.	AminoBERT	the protein language model applied for determination of latent structural information from unaligned proteins	Chowdhury et al.188
12.	MHCSeqNet	the neural networks used to predict peptide-MHC binding from the sequence data without requiring structural information	Phloyphisut et al.181
13.	IgFold	the fastest and accurate antibody structure prediction tool from DL on massive set of natural antibodies	Ruffolo et al.189
14.	DeepMHCII	the binding core-aware DL-based model having a binding interaction convolution layer; it permits to participate all potential binding cores peptide with the MHC binding sequence	You et al.190
15.	DeepTCR	the tool designed to analyze T cell receptor (TCR) sequences, helping to understand immune responses in vaccine development, immunotherapy, and infectious disease research from the from complex immunogenomic data	Sidhom et al.191
16.	DeepImmuno	the tool appropriately predicts the residues for most imperative for recognition of T cell antigen, and able to precisely simulate immunogenic peptides with added physicochemical properties, immunogenicity predictions comparable to the real antigens	Li et al.192
17.	DeepHLA	this tool used to predict T cell responses based on HLA-peptide interactions	Naito et al.193

Conclusion

DL, a subset of AI, has emerged as a powerful tool in vaccine development. By leveraging large datasets and complex algorithms, DL can accelerate vaccine discovery, design, and optimization. DL revolutionizes vaccine development by enabling faster, more accurate, cost-effective processes. DL played a important role in the rapid development of COVID-19 vaccines by predicting Spike protein structures, optimizing mRNA sequences, and accelerating clinical trials. From antigen discovery to clinical trial optimization, AI is becoming an indispensable tool to fight against infectious diseases and beyond. Integrating DL with traditional experimental methods will be key to developing next-generation vaccines as the field advances.

The DL presents a revolutionary approach to vaccine development by accelerating antigen discovery, enhancing vaccine design, facilitating RV, addressing emerging threats, and personalizing vaccination strategies. Ongoing research and development focused on data quality, model interpretability, and validation will be essential to fully realize the transformative potential of DL in creating the next-generation of effective and safe vaccines against infectious diseases.

Acknowledgments

This research was supported by Zuoying Armed Forces General Hospital (grant ZYAFGH_A_114018).

Author contributions

M.B., writing – original draft, data curation, methodology, visualization, and figure and table development. Y.-H.L., manuscript validation, formal analysis, and fund acquisition. S.C., writing – original draft, validation, and formal analysis. A.D., final manuscript draft validation and formal analysis. Z.-H.W., validation and formal analysis. C.C., conceptualization, analysis, software, methodology, writing – original draft, data curation, and supervision of the whole project administration.

Declaration of interests

The authors declare no competing interests.