The rise of taxon-specific epitope predictors

Computational predictors of immunogenic peptides, or epitopes, are traditionally built based on data from a broad range of pathogens without consideration for taxonomic information. While this approach may be reasonable if one aims to develop one-size-fits-all models, it may be counterproductive if the proteins for which the model is expected to generalize are known to come from a specific subset of phylogenetically related pathogens. There is mounting evidence that, for these cases, taxon-specific models can outperform generalist ones, even when trained with substantially smaller amounts of data. In this comment, we provide some perspective on the current state of taxon-specific modelling for the prediction of linear B-cell epitopes, and the challenges faced when building and deploying these predictors.

Single-residue linear and conformational B cell epitopes prediction using random and ESM-2 based projections

B cell epitope prediction methods are separated into linear sequence-based predictors and conformational epitope predictions that typically use the measured or predicted protein structure. Most linear predictions rely on the translation of the sequence to biologically based representations and the applications of machine learning on these representations. We here present CALIBER 'Conformational And LInear B cell Epitopes pRediction', and show that a bidirectional long short-term memory with random projection produces a more accurate prediction (test set AUC=0.789) than all current linear methods. The same predictor when combined with an Evolutionary Scale Modeling-2 projection also improves on the state of the art in conformational epitopes (AUC = 0.776). The inclusion of the graph of the 3D distances between residues did not increase the prediction accuracy. However, the long-range sequence information was essential for high accuracy. While the same model structure was applicable for linear and conformational epitopes, separate training was required for each. Combining the two slightly increased the linear accuracy (AUC 0.775 versus 0.768) and reduced the conformational accuracy (AUC = 0.769).

Computational identification of a multi-peptide vaccine candidate in E2 glycoprotein against diverse Hepatitis C virus genotypes

Hepatitis C Virus (HCV) is estimated to affect nearly 180 million people worldwide, culminating in ∼0.7 million yearly casualties. However, a safe vaccine against HCV is not yet available. This study endeavored to identify a multi-genotypic, multi-epitopic, safe, and globally competent HCV vaccine candidate. We employed a consensus epitope prediction strategy to identify multi-epitopic peptides in all known envelope glycoprotein (E2) sequences, belonging to diverse HCV genotypes. The obtained peptides were screened for toxicity, allergenicity, autoimmunity and antigenicity, resulting in two favorable peptides viz., P2 (VYCFTPSPVVVG) and P3 (YRLWHYPCTV). Evolutionary conservation analysis indicated that P2 and P3 are highly conserved, supporting their use as part of a designed multi-genotypic vaccine. Population coverage analysis revealed that P2 and P3 are likely to be presented by >89% Human Leukocyte Antigen (HLA) molecules from six geographical regions. Indeed, molecular docking predicted the physical binding of P2 and P3 to various representative HLAs. We designed a vaccine construct using these peptides and assessed its binding to toll-like receptor 4 (TLR-4) by molecular docking and simulation. Subsequent analysis by energy-based and machine learning tools predicted high binding affinity and pinpointed the key binding residues (i.e. hotspots) in P2 and P3. Also, a favorable immunogenic profile of the construct was predicted by immune simulations. We encourage the scientific community to validate our vaccine construct in vitro and in vivo.Communicated by Ramaswamy H. Sarma.

Immunoinformatics applications in the development of therapeutic vaccines against human papillomavirus-related infections and cervical cancer

The human papillomavirus (HPV) represents the most prevalent sexually transmitted infectious agent worldwide. HPV penetrates the epithelium through microlesions and establishes an infectious focus that can lead to the development of cervical cancer. Prophylactic HPV vaccines are available, but do not affect already-established infections. Using in silico prediction tools is a promising strategy for identifying and selecting vaccine candidate T cell epitopes. An advantage of this strategy is that epitopes can be selected according to the degree of conservation within a group of antigenic proteins. This makes achieving comprehensive genotypic coverage possible with a small set of epitopes. Therefore, this paper revises the general characteristics of HPV biology and the current knowledge on developing therapeutic peptide vaccines against HPV-related infections and cervical cancer.

Bioinformatics analysis of Muscovy duck parvovirus REP and VP1 proteins

This article was aimed at analyzing the sequence, structure, and function of the two Muscovy duck parvovirus proteins, including REP and VP1. The antigenicity, physical and chemical properties, transmembrane regions, phosphorylation sites, glycosylation sites, three-dimensional structure, and linear epitope of VP1 and REP were predicted and analyzed through bioinformatics methods. A multi-epitope vaccine was also constructed based on the screened epitopes, and the vaccine was characterized, modeled, molecularly docked and molecularly cloned. The epitopes were screened according to the criteria of antigenicity, non-allergenicity and non-toxicity, and 12 epitope fragments were obtained. The B cell epitopes were analyzed according to four scales: β-turn, hydrophilicity, surface accessibility and antigenicity. Combined with the epitope prediction results based on structure, the final epitope prediction results were obtained. The multi-epitope vaccine used an EAAAK-linked adjuvant, a GPGPG-linked T-cell epitope, and a KK-linked B-cell epitope. The analysis showed that the vaccine was stable hydrophilic, antigenic, conserved and non-allergenic. Based on molecular docking it was shown that good interactions between the vaccine and the immune receptor were generated and were essential to generate an immune response. The final vaccine was reverse translated into cDNA and the DNA vaccine was designed by codon optimization and molecular cloning. Further trials are still needed to demonstrate the immunogenicity and other aspects of vaccine efficacy.Communicated by Ramaswamy H. Sarma.

Mapping antibody footprints using binding profiles

The increasing use of monoclonal antibodies (mAbs) in biology and medicine necessitates efficient methods for characterizing their binding epitopes. Here, we developed a high-throughput antibody footprinting method based on binding profiles. We used an antigen microarray to profile 23 human anti-influenza hemagglutinin (HA) mAbs using HA proteins of 43 human influenza strains isolated between 1918 and 2018. We showed that the mAb's binding profile can be used to characterize its influenza subtype specificity, binding region, and binding site. We present mAb-Patch-an epitope prediction method that is based on a mAb's binding profile and the 3D structure of its antigen. mAb-Patch was evaluated using four mAbs with known solved mAb-HA structures. mAb-Patch identifies over 67% of the true epitope when considering only 50-60 positions along the antigen. Our work provides proof of concept for utilizing antibody binding profiles to screen large panels of mAbs and to down-select antibodies for further functional studies.

Development of a Universal Multi-Epitope Vaccine Candidate against Streptococcus suis Infections Using Immunoinformatics Approaches

Streptococcus suis is a significant zoonotic pathogen that is a great threat not only to the swine industry but also to human health, causing arthritis, meningitis, and even streptococcal toxic shock-like syndrome. Owing to its many serotypes and high geographic variability, an efficacious cross-protective S. suis vaccine is not readily available. Therefore, this study aimed to design a universal multi-epitope vaccine (MVHP6) that involved three highly immunogenic proteins of S. suis, namely, the surface antigen containing a glycosaminoglycan binding domain (HP0197), endopeptidase (PepO), and 6-phosphogluconate dehydrogenase (6PGD). Forecasted T-cell and B-cell epitopes with high antigenic properties and a suitable adjuvant were linked to construct a multi-epitope vaccine. In silico analysis showed that the selected epitopes were conserved in highly susceptible serotypes for humans. Thereafter, we evaluated the different parameters of MVHP6 and showed that MVHP6 was highly antigenic, non-toxic, and non-allergenic. To verify whether the vaccine could display appropriate epitopes and maintain high stability, the MVHP6 tertiary structure was modeled, refined, and validated. Molecular docking studies revealed a strong binding interaction between the vaccine and the toll-like receptor (TLR4), whereas molecular dynamics simulations demonstrated the vaccine's compatibility, binding stability, and structural compactness. Moreover, the in silico analysis showed that MVHP6 could evoke strong immune responses and enable worldwide population coverage. Moreover, MVHP6 was cloned into the pET28a (+) vector in silico to ensure the credibility, validation, and proper expression of the vaccine construct. The findings suggested that the proposed multi-epitope vaccine can provide cross-protection against S. suis infections.

Recent trends in next generation immunoinformatics harnessed for universal coronavirus vaccine design

The ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has globally devastated public health, the economies of many countries and quality of life universally. The recent emergence of immune-escaped variants and scenario of vaccinated individuals being infected has raised the global concerns about the effectiveness of the current available vaccines in transmission control and disease prevention. Given the high rate mutation of SARS-CoV-2, an efficacious vaccine targeting against multiple variants that contains virus-specific epitopes is desperately needed. An immunoinformatics approach is gaining traction in vaccine design and development due to the significant reduction in time and cost of immunogenicity studies and increasing reliability of the generated results. It can underpin the development of novel therapeutic methods and accelerate the design and production of peptide vaccines for infectious diseases. Structural proteins, particularly spike protein (S), along with other proteins have been studied intensively as promising coronavirus vaccine targets. Numbers of promising online immunological databases, tools and web servers have widely been employed for the design and development of next generation COVID-19 vaccines. This review highlights the role of immunoinformatics in identifying immunogenic peptides as potential vaccine targets, involving databases, and prediction and characterization of epitopes which can be harnessed for designing future coronavirus vaccines.

Structural Analysis and Epitope Prediction of S2 Domain of SARS-CoV-2, Conservation Analysis Among Major Variants

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic. There are four structural proteins of the virus: spike, envelope, membrane, and nucleocapsid proteins. Various vaccines were designed and are effectively being used against the spike protein of the virus. However, several vaccine-related complications have been reported worldwide. Assuming that the structural integrity of the whole protein might be contributing to these complications, this study was performed to design epitopes using the S2 domain of the spike protein, which could trigger a strong immune response. We have also predicted antigenic and allergenic properties of the selected epitopes. A total of 49 B cell epitopes passing antigenicity and other assessment filters were found using three methods. Among them, RDLICAQ had the highest antigenicity score (1.1443). However, only one cytotoxic T lymphocyte epitope, RSFIEDLLF, passed the essential filters with an antigenicity score of 0.5782 to show an appropriate immune response for T cells, while among 21 helper T cell lymphocyte epitopes that were filtered, FAMQMAYRFNGIGVT showed the highest (1.3688) antigenicity score. Conservation analysis revealed that the S2 domain is significantly conserved, thus making it an ideal candidate for vaccine development. We have also designed a vaccine construct based on the best suiting components found during the whole study. This construct and S2 domain solely can be future subjects of interest or might be included in a subunit cocktail formulation for attaining unabridged immunogenicity.

Systems Biology Approaches for the Improvement of Oncolytic Virus-Based Immunotherapies

Oncolytic virus (OV)-based immunotherapy is mainly dependent on establishing an efficient cell-mediated antitumor immunity. OV-mediated antitumor immunity elicits a renewed antitumor reactivity, stimulating a T-cell response against tumor-associated antigens (TAAs) and recruiting natural killer cells within the tumor microenvironment (TME). Despite the fact that OVs are unspecific cancer vaccine platforms, to further enhance antitumor immunity, it is crucial to identify the potentially immunogenic T-cell restricted TAAs, the main key orchestrators in evoking a specific and durable cytotoxic T-cell response. Today, innovative approaches derived from systems biology are exploited to improve target discovery in several types of cancer and to identify the MHC-I and II restricted peptide repertoire recognized by T-cells. Using specific computation pipelines, it is possible to select the best tumor peptide candidates that can be efficiently vectorized and delivered by numerous OV-based platforms, in order to reinforce anticancer immune responses. Beyond the identification of TAAs, system biology can also support the engineering of OVs with improved oncotropism to reduce toxicity and maintain a sufficient portion of the wild-type virus virulence. Finally, these technologies can also pave the way towards a more rational design of armed OVs where a transgene of interest can be delivered to TME to develop an intratumoral gene therapy to enhance specific immune stimuli.

Identification and Structure of Epitopes on Cashew Allergens Ana o 2 and Ana o 3 Using Phage Display

BACKGROUND

Cashew (Anacardium occidentale L.) is a commercially important plant. Cashew nuts are a popular food source that belong to the tree nut family. Tree nuts are one of the eight major food allergens identified by the Food and Drug Administration in the USA. Allergies to cashew nuts cause severe and systemic immune reactions. Tree nut allergies are frequently fatal and are becoming more common.

AIM

We aimed to identify the key allergenic epitopes of cashew nut proteins by correlating the phage display epitope prediction results with bioinformatics analysis.

DESIGN

We predicted and experimentally confirmed cashew nut allergen antigenic peptides, which we named Ana o 2 (cupin superfamily) and Ana o 3 (prolamin superfamily). The Ana o 2 and Ana o 3 epitopes were predicted using DNAstar and PyMoL (incorporated in the Swiss-model package). The predicted weak and strong epitopes were synthesized as peptides. The related phage library was built. The peptides were also tested using phage display technology. The expressed antigens were tested and confirmed using microtiter plates coated with pooled human sera from patients with cashew nut allergies or healthy controls.

RESULTS

The Ana o 2 epitopes were represented by four linear peptides, with the epitopes corresponding to amino acids 108-111, 113-119, 181-186, and 218-224. Furthermore, the identified Ana o 3 epitopes corresponding to amino acids 10-24, 13-27, 39-49, 66-70, 101-106, 107-114, and 115-122 were also screened out and chosen as the key allergenic epitopes.

DISCUSSION

The Ana o 3 epitopes accounted for more than 40% of the total amino acid sequence of the protein; thus, Ana o 3 is potentially more allergenic than Ana o 2.

CONCLUSIONS

The bioinformatic epitope prediction produced subpar results in this study. Furthermore, the phage display method was extremely effective in identifying the allergenic epitopes of cashew nut proteins. The key allergenic epitopes were chosen, providing important information for the study of cashew nut allergens.

Experimental Validation of MHC Class I and II Peptide-Based Potential Vaccine Candidates for Human Papilloma Virus Using Sprague-Dawly Models

Human papilloma virus (HPV) causes cervical and many other cancers. Recent trend in vaccine design is shifted toward epitope-based developments that are more specific, safe, and easy to produce. In this study, we predicted eight immunogenic peptides of CD4+ and CD8+ T-lymphocytes (MHC class I and II as M1 and M2) including early proteins (E2 and E6), major (L1) and minor capsid protein (L2). Male and female Sprague Dawly rats in groups were immunized with each synthetic peptide. L1M1, L1M2, L2M1, and L2M2 induced significant immunogenic response compared to E2M1, E2M2, E6M1 and E6M2. We observed optimal titer of IgG antibodies (>1.25 g/L), interferon-γ (>64 ng/L), and granzyme-B (>40 pg/mL) compared to control at second booster dose (240 µg/500 µL). The induction of peptide-specific IgG antibodies in immunized rats indicates the T-cell dependent B-lymphocyte activation. A substantial CD4+ and CD8+ cell count was observed at 240 µg/500 µL. In male and female rats, CD8+ cell count for L1 and L2 peptide is 3000 and 3118, and CD4+ is 3369 and 3484 respectively compared to control. In conclusion, we demonstrated that L1M1, L1M2, L2M1, L2M2 are likely to contain potential epitopes for induction of immune responses supporting the feasibility of peptide-based vaccine development for HPV.

Immunoinformatics Study: Multi-Epitope Based Vaccine Design from SARS-CoV-2 Spike Glycoprotein

The coronavirus disease 2019 outbreak has become a huge challenge in the human sector for the past two years. The coronavirus is capable of mutating at a higher rate than other viruses. Thus, an approach for creating an effective vaccine is still needed to induce antibodies against multiple variants with lower side effects. Currently, there is a lack of research on designing a multiepitope of the COVID-19 spike protein for the Indonesian population with comprehensive immunoinformatic analysis. Therefore, this study aimed to design a multiepitope-based vaccine for the Indonesian population using an immunoinformatic approach. This study was conducted using the SARS-CoV-2 spike glycoprotein sequences from Indonesia that were retrieved from the GISAID database. Three SARS-CoV-2 sequences, with IDs of EIJK-61453, UGM0002, and B.1.1.7 were selected. The CD8+ cytotoxic T-cell lymphocyte (CTL) epitope, CD4+ helper T lymphocyte (HTL) epitope, B-cell epitope, and IFN-γ production were predicted. After modeling the vaccines, molecular docking, molecular dynamics, in silico immune simulations, and plasmid vector design were performed. The designed vaccine is antigenic, non-allergenic, non-toxic, capable of inducing IFN-γ with a population reach of 86.29% in Indonesia, and has good stability during molecular dynamics and immune simulation. Hence, this vaccine model is recommended to be investigated for further study.

Designing a multi-epitope vaccine against coxsackievirus B based on immunoinformatics approaches

Coxsackievirus B (CVB) is one of the major viral pathogens of human myocarditis and cardiomyopathy without any effective preventive measures; therefore, it is necessary to develop a safe and efficacious vaccine against CVB. Immunoinformatics methods are both economical and convenient as in-silico simulations can shorten the development time. Herein, we design a novel multi-epitope vaccine for the prevention of CVB by using immunoinformatics methods. With the help of advanced immunoinformatics approaches, we predicted different B-cell, cytotoxic T lymphocyte (CTL), and helper T lymphocyte (HTL) epitopes, respectively. Subsequently, we constructed the multi-epitope vaccine by fusing all conserved epitopes with appropriate linkers and adjuvants. The final vaccine was found to be antigenic, non-allergenic, and stable. The 3D structure of the vaccine was then predicted, refined, and evaluated. Molecular docking and dynamics simulation were performed to reveal the interactions between the vaccine with the immune receptors MHC-I, MHC-II, TLR3, and TLR4. Finally, to ensure the complete expression of the vaccine protein, the sequence of the designed vaccine was optimized and further performed in-silico cloning. In conclusion, the molecule designed in this study could be considered a potential vaccine against CVB infection and needed further experiments to evaluate its safety and efficacy.

Genome-wide scan for potential CD4+ T-cell vaccine candidates in Candida auris by exploiting reverse vaccinology and evolutionary information

Candida auris is a globally emerging fungal pathogen responsible for causing nosocomial outbreaks in healthcare associated settings. It is known to cause infection in all age groups and exhibits multi-drug resistance with high potential for horizontal transmission. Because of this reason combined with limited therapeutic choices available, C. auris infection has been acknowledged as a potential risk for causing a future pandemic, and thus seeking a promising strategy for its treatment is imperative. Here, we combined evolutionary information with reverse vaccinology approach to identify novel epitopes for vaccine design that could elicit CD4+ T-cell responses against C. auris. To this end, we extensively scanned the family of proteins encoded by C. auris genome. In addition, a pathogen may acquire substitutions in epitopes over a period of time which could cause its escape from the immune response thus rendering the vaccine ineffective. To lower this possibility in our design, we eliminated all rapidly evolving genes of C. auris with positive selection. We further employed highly conserved regions of multiple C. auris strains and identified two immunogenic and antigenic T-cell epitopes that could generate the most effective immune response against C. auris. The antigenicity scores of our predicted vaccine candidates were calculated as 0.85 and 1.88 where 0.5 is the threshold for prediction of fungal antigenic sequences. Based on our results, we conclude that our vaccine candidates have the potential to be successfully employed for the treatment of C. auris infection. However, in vivo experiments are imperative to further demonstrate the efficacy of our design.

The CD4+ T cell response to a commensal-derived epitope transitions from a tolerant to an inflammatory state in Crohn's disease

Reciprocal interactions between host T helper cells and gut microbiota enforce local immunological tolerance and modulate extra-intestinal immunity. However, our understanding of antigen-specific tolerance to the microbiome is limited. Here, we developed a systematic approach to predict HLA class-II-specific epitopes using the humanized bacteria-originated T cell antigen (hBOTA) algorithm. We identified a diverse set of microbiome epitopes spanning all major taxa that are compatible with presentation by multiple HLA-II alleles. In particular, we uncovered an immunodominant epitope from the TonB-dependent receptor SusC that was universally recognized and ubiquitous among Bacteroidales. In healthy human subjects, SusC-reactive T cell responses were characterized by IL-10-dominant cytokine profiles, whereas in patients with active Crohn's disease, responses were associated with elevated IL-17A. Our results highlight the potential of targeted antigen discovery within the microbiome to reveal principles of tolerance and functional transitions during inflammation.

A Promising Tool in Serological Diagnosis: Current Research Progress of Antigenic Epitopes in Infectious Diseases

Infectious diseases, caused by various pathogens in the clinic, threaten the safety of human life, are harmful to physical and mental health, and also increase economic burdens on society. Infections are a complex mechanism of interaction between pathogenic microorganisms and their host. Identification of the causative agent of the infection is vital for the diagnosis and treatment of diseases. Etiological laboratory diagnostic tests are therefore essential to identify pathogens. However, due to its rapidity and automation, the serological diagnostic test is among the methods of great significance for the diagnosis of infections with the basis of detecting antigens or antibodies in body fluids clinically. Epitopes, as a special chemical group that determines the specificity of antigens and the basic unit of inducing immune responses, play an important role in the study of immune responses. Identifying the epitopes of a pathogen may contribute to the development of a vaccine to prevent disease, the diagnosis of the corresponding disease, and the determination of different stages of the disease. Moreover, both the preparation of neutralizing antibodies based on useful epitopes and the assembly of several associated epitopes can be used in the treatment of disease. Epitopes can be divided into B cell epitopes and T cell epitopes; B cell epitopes stimulate the body to produce antibodies and are therefore commonly used as targets for the design of serological diagnostic experiments. Meanwhile, epitopes can fall into two possible categories: linear and conformational. This article reviews the role of B cell epitopes in the clinical diagnosis of infectious diseases.

A comparative analysis of amino acid encoding schemes for the prediction of flexible length linear B-cell epitopes

Linear B-cell epitopes have a prominent role in the development of peptide-based vaccines and disease diagnosis. High variability in the length of these epitopes is a major reason for low accuracy in their prediction. Most of the B-cell epitope prediction methods considered fixed length of epitope sequences and achieved good accuracy. Though a number of tools are available for the prediction of flexible length linear B-cell epitopes with reasonable accuracy, further improvement in the prediction performance is still expected. Thus, here we made an attempt to analyze the performance of machine learning approaches (MLA) with 18 different amino acid encoding schemes in the prediction of flexible length linear B-cell epitopes. We considered B-cell epitope sequences of variable lengths (11-56 amino acids) from well-established public resources. The performances of machine learning algorithms with the encoded epitope sequence datasets were evaluated. Besides, the feasible combinations of encoding schemes were also explored and analyzed. The results revealed that amino-acid composition (AC) and distribution component of composition-transition-distribution encoding schemes are suitable for heterogeneous epitope data, whereas amino-acid-anchoring-pair-composition (APC), dipeptide-composition and amino-acids-pair-propensity-scale (APP) are more appropriate for homogeneous data. Further, two combinations of peptide encoding schemes, i.e. APC + AC and APC + APP with random forest classifier were identified to have improved performance over the state-of-the-art tools for flexible length linear B-cell epitope prediction. The study also revealed better performance of random forest over other considered MLAs in the prediction of flexible length linear B-cell epitopes.

CAVES: A Novel Tool for Comparative Analysis of Variant Epitope Sequences

In silico methods for immune epitope prediction have become essential for vaccine and therapeutic design, but manual intra-species comparison of putative epitopes remains challenging and subject to human error. Created initially for analyzing SARS-CoV-2 variants of concern, comparative analysis of variant epitope sequences (CAVES) is a novel tool designed to carry out rapid comparative analyses of epitopes amongst closely related pathogens, substantially reducing the required time and user workload. CAVES applies a two-level analysis approach. The Level-one (L1) analysis compares two epitope prediction files, and the Level-two (L2) analysis incorporates search results from the IEDB database of experimentally confirmed epitopes. Both L1 and L2 analyses sort epitopes into categories of exact matches, partial matches, or novel epitopes based on the degree to which they match with peptides from the compared file. Furthermore, CAVES uses positional sequence data to improve its accuracy and speed, taking only a fraction of the time required by manual analyses and minimizing human error. CAVES is widely applicable for evolutionary analyses and antigenic comparisons of any closely related pathogen species. CAVES is open-source software that runs through a graphical user interface on Windows operating systems, making it widely accessible regardless of coding expertise. The CAVES source code and test dataset presented here are publicly available on the CAVES GitHub page.

Computational vaccinology guided design of multi-epitope subunit vaccine against a neglected arbovirus of the Americas

Mayaro virus (MAYV) is an arbovirus found in the Americas that can cause debilitating arthritogenic disease. Although it is an emerging virus, the only current approach is vector control, as there are no approved vaccines to prevent MAYV infection nor therapeutics to treat it. In search of an effective vaccine candidate against MAYV, we used immunoinformatics and molecular modeling to attempt to identify promiscuous T-cell epitopes of the nonstructural polyproteins (nsP1, nsP2, nsP3, and nsP4) from 127 MAYV genomes sequenced in the Americas (08 Bolivia, 72 Brazil, 04 French Guiana, 05 Haiti, 20 Peru, 04 Trinidad and Tobago, and 14 Venezuela). For this purpose, consensus sequences of 360 proteins were used to identify short protein sequences that can bind to MHC I class (MHC II). Our analysis revealed 56 potential MHC-I/TCD8+ (29 MHC-II/TCD4+) epitopes, but only 6 (16) TCD8+ (TCD4+) epitopes showed high antigenicity and conservation, non-allergenicity, non-toxicity, and excellent population coverage. Finally, classical and quantum mechanical calculations (QM:MM) were used to improve the quality of the docking calculations, with the QM part of the simulations performed using the density functional theory formalism (DFT). These results provide insights for the advancement of diagnostic platforms, vaccine development, and immunotherapeutic interventions.Communicated by Ramaswamy H. Sarma.

CRESSP: a comprehensive pipeline for prediction of immunopathogenic SARS-CoV-2 epitopes using structural properties of proteins

The development of autoimmune diseases following SARS-CoV-2 infection, including multisystem inflammatory syndrome, has been reported, and several mechanisms have been suggested, including molecular mimicry. We developed a scalable, comparative immunoinformatics pipeline called cross-reactive-epitope-search-using-structural-properties-of-proteins (CRESSP) to identify cross-reactive epitopes between a collection of SARS-CoV-2 proteomes and the human proteome using the structural properties of the proteins. Overall, by searching 4 911 245 proteins from 196 352 SARS-CoV-2 genomes, we identified 133 and 648 human proteins harboring potential cross-reactive B-cell and CD8+ T-cell epitopes, respectively. To demonstrate the robustness of our pipeline, we predicted the cross-reactive epitopes of coronavirus spike proteins, which were recognized by known cross-neutralizing antibodies. Using single-cell expression data, we identified PARP14 as a potential target of intermolecular epitope spreading between the virus and human proteins. Finally, we developed a web application (https://ahs2202.github.io/3M/) to interactively visualize our results. We also made our pipeline available as an open-source CRESSP package (https://pypi.org/project/cressp/), which can analyze any two proteomes of interest to identify potentially cross-reactive epitopes between the proteomes. Overall, our immunoinformatic resources provide a foundation for the investigation of molecular mimicry in the pathogenesis of autoimmune and chronic inflammatory diseases following COVID-19.

In silico Design of a Crimean-Congo Hemorrhagic Fever Virus Glycoprotein Multi-Epitope Antigen for Vaccine Development

Objective

There is no licensed vaccine available to prevent the severe tick-borne disease Crimean-Congo hemorrhagic fever (CCHF), caused by the CCHF virus (CCHFV). This study sought to show that a combination of computational methods and data from published literature can inform the design of a multi-epitope antigen for CCHFV that has the potential to be immunogenic.

Methods

Cytotoxic and helper T-cell epitopes were evaluated on the CCHFV GPC using bioinformatic servers, and this data was combined with work from previous studies to identify potentially immunodominant regions of the GPC. Regions of the GPC were selected for generation of a model multi-epitope antigen in silico, and the percent residue identity and similarity of each region was compared across sequences representing the widespread geographical and ecological distribution of CCHFV.

Results

Eleven multi-epitope regions were joined together with flexible linkers in silico to generate a model multi-epitope antigen, termed EPIC, which included 812 (75.7%) of all predicted epitopes. EPIC was predicted to be antigenic by two independent bioinformatic servers, suggesting that multi-epitope antigens should be explored further for CCHFV vaccine development.

Conclusion

The results presented within this manuscript provide information for potential targets within the CCHFV GPC for guiding future vaccine development.

Investigation of Protein and Epitope Characteristics of Oats and Its Implications for Celiac Disease

The use of pure oats (oats cultivated with special care to avoid gluten contamination from wheat, rye, and barley) in the gluten-free diet (GFD) represents important nutritional benefits for the celiac consumer. However, emerging evidence suggests that some oat cultivars may contain wheat gliadin analog polypeptides. Consequently, it is necessary to screen oats in terms of protein and epitope composition to be able to select safe varieties for gluten-free applications. The overall aim of our study is to investigate the variability of oat protein composition directly related to health-related and techno-functional properties. Elements of an oat sample population representing 162 cultivated varieties from 20 countries and the protein composition of resulting samples have been characterized. Size distribution of the total protein extracts has been analyzed by size exclusion-high performance liquid chromatography (SE-HPLC) while the 70% ethanol-extracted proteins were analyzed by RP-HPLC. Protein extracts separated into three main groups of fractions on the SE-HPLC column: polymeric proteins, avenins (both containing three subgroups based on their size), and soluble proteins, representing respectively 68.79–86.60, 8.86–27.72, and 2.89–11.85% of the total protein content. The ratio of polymeric to monomeric proteins varied between 1.37 and 3.73. Seventy-six reversed phase-HPLC-separated peaks have been differentiated from the ethanol extractable proteins of the entire population. Their distribution among the cultivars varied significantly, 6–23 peaks per cultivar. The number of appearances of peaks also showed large variation: one peak has been found in 107 samples, while 15 peaks have been identified, which appeared in less than five cultivars. An estimation method for ranking the avenin-epitope content of the samples has been developed by using MS spectrometric data of collected RP-HPLC peaks and bioinformatics methods. Using ELISA methodology with the R5 antibody, a high number of the investigated samples were found to be contaminated with wheat, barley, or rye.

T Cell Epitope Prediction and Its Application to Immunotherapy

T cells play a crucial role in controlling and driving the immune response with their ability to discriminate peptides derived from healthy as well as pathogenic proteins. In this review, we focus on the currently available computational tools for epitope prediction, with a particular focus on tools aimed at identifying neoepitopes, i.e. cancer-specific peptides and their potential for use in immunotherapy for cancer treatment. This review will cover how these tools work, what kind of data they use, as well as pros and cons in their respective applications.

Infectious disease mRNA vaccines and a review on epitope prediction for vaccine design

Messenger RNA (mRNA) vaccines have recently emerged as a new type of vaccine technology, showing strong potential to combat the COVID-19 pandemic. In addition to SARS-CoV-2 which caused the pandemic, mRNA vaccines have been developed and tested to prevent infectious diseases caused by other viruses such as Zika virus, the dengue virus, the respiratory syncytial virus, influenza H7N9 and Flavivirus. Interestingly, mRNA vaccines may also be useful for preventing non-infectious diseases such as diabetes and cancer. This review summarises the current progresses of mRNA vaccines designed for a range of diseases including COVID-19. As epitope study is a primary component in the in silico design of mRNA vaccines, we also survey on advanced bioinformatics and machine learning algorithms which have been used for epitope prediction, and review on user-friendly software tools available for this purpose. Finally, we discuss some of the unanswered concerns about mRNA vaccines, such as unknown long-term side effects, and present with our perspectives on future developments in this exciting area.

Broad and Efficient Activation of Memory CD4+ T Cells by Novel HAdV- and HCMV-Derived Peptide Pools

Reactivation of Human Cytomegalovirus (HCMV) and Human Adenovirus (HAdV) in immunocompromised patients following stem cell transplantation (SCT) or solid organ transplantation (SOT) is associated with high morbidity and mortality. The adoptive transfer of virus-specific CD8⁺ and CD4⁺ T cells has been shown to re-establish the antiviral T-cell response and improve clinical outcome. The viral load in immunocompromised patients can efficiently be reduced solely by the infusion of virus-specific CD4⁺ T cells. The identification of CD4⁺ T-cell epitopes has mainly focused on a limited number of viral proteins that were characterized as immunodominant. Here, we used in silico prediction to determine promiscuous CD4⁺ T-cell epitopes from the entire proteomes of HCMV and HAdV. Immunogenicity testing with enzyme-linked immuno spot (ELISpot) assays and intracellular cytokine staining (ICS) revealed numerous novel CD4⁺ T-cell epitopes derived from a broad spectrum of viral antigens. We identified 17 novel HCMV-derived and seven novel HAdV-derived CD4⁺ T-cell epitopes that were recognized by > 50% of the assessed peripheral blood mononuclear cell (PBMC) samples. The newly identified epitopes were pooled with previously published, retested epitopes to stimulate virus-specific memory T cells in PBMCs from numerous randomly selected blood donors. Our peptide pools induced strong IFNγ secretion in 46 out of 48 (HCMV) and 31 out of 31 (HAdV) PBMC cultures. In conclusion, we applied an efficient method to screen large viral proteomes for promiscuous CD4⁺ T-cell epitopes to improve the detection and isolation of virus-specific T cells in a clinical setting.

Recent progress on MHC-I epitope prediction in tumor immunotherapy

Tumor immunotherapy has now become one of the most potential therapy for those intractable cancer diseases. The antigens on the cancer cell surfaces are the keys for the immune system to recognize and eliminate them. As reported, the immunogenicity of the tumor antigens could be determined by the binding between the key epitope peptides and MHC molecules. In recent years, the approaches to anticipate the peptides from the candidate epitopes have gradually changed into more efficient methods. Including the improved conventional methods, more diverse methods were coming into view. Here we review the anticipated methods of the tumor associated epitopes that specifically bind with major histocompatibility complex (MHC) class I molecules, and the recent advances and applications of those epitope prediction methods.

Challenges targeting cancer neoantigens in 2021: a systematic literature review

Introduction: Cancer neoantigens represent important targets of cancer immunotherapy. The goal of cancer neoantigen vaccines is to induce neoantigen-specific immune responses and antitumor immunity while minimizing the potential for autoimmune toxicity. Advances in sequencing technologies, neoantigen prediction algorithms, and other technologies have dramatically improved the ability to identify and prioritize cancer neoantigens. Unfortunately, results from preclinical studies and early phase clinical trials highlight important challenges to the successful clinical translation of neoantigen cancer vaccines.Areas covered: In this review, we provide an overview of current strategies for the identification and prioritization of cancer neoantigens with a particular emphasis on the two most common strategies used for neoantigen identification: (1) direct identification of peptide ligands eluted from peptide-MHC complexes, and (2) next-generation sequencing combined with neoantigen prediction algorithms. We highlight the limitations of current neoantigen prediction pipelines, and discuss broader challenges associated with cancer neoantigen vaccines including tumor purity/heterogeneity and the immunosuppressive tumor microenvironment.Expert opinion: Despite current limitations, neoantigen prediction is likely to improve rapidly based on advances in sequencing, machine learning, and information sharing. The successful development of robust cancer neoantigen prediction strategies is likely to have a significant impact, with the potential to facilitate cancer neoantigen vaccine design.

Identification of CD4(+) T cell epitopes from Staphylococcus aureus secretome using immunoinformatic prediction and molecular docking

One major reason for the lack of clinical success of Staphylococcus aureus vaccine candidates is the inability of the antigens to develop a CD4⁺ T cell-mediated immune response. Hence, it is important to identify CD4⁺ T cell antigens from S. aureus. CD4⁺ T cells are activated following the presentation of epitopes derived from exogenous proteins on HLA class II molecules. Fifty-nine secretory proteins of S. aureus were analyzed computationally for the presence of HLA class II binding peptides. Fifteen-mer peptides were generated, and their binding to 26 HLA class II alleles was predicted. The structural feasibility of the peptides binding to HLA-II was studied using molecular docking. Of the 16,724 peptides generated, 6991 (41.8%) were predicted to bind to any one of the alleles with an IC₅₀ value below 50 nM. Comparative sequence analysis revealed that only 545 of the strong binding peptides are non-self in the human system. Approximately 50% of the binding peptides were monoallele-specific. Moreover, approximately 95% of the predicted strong binding non-self peptides interacted with the binding groove of at least one HLA class II molecule with a glide score better than -10 kcal/mol. On the basis of the analysis of the strength of binding, non-self presentation in the human host, propensity to bind to a higher number of alleles, and energetically favorable interactions with HLA molecules, a set of 11 CD4⁺ T cell epitopes that can be used as vaccine candidates was identified.

Design of an epitope-based peptide vaccine against the SARS-CoV-2: a vaccine-informatics approach

The recurrent and recent global outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has turned into a global concern which has infected more than 42 million people all over the globe, and this number is increasing in hours. Unfortunately, no vaccine or specific treatment is available, which makes it more deadly. A vaccine-informatics approach has shown significant breakthrough in peptide-based epitope mapping and opens the new horizon in vaccine development. In this study, we have identified a total of 15 antigenic peptides [including thymus cells (T-cells) and bone marrow or bursa-derived cells] in the surface glycoprotein (SG) of SARS-CoV-2 which is nontoxic and nonallergenic in nature, nonallergenic, highly antigenic and non-mutated in other SARS-CoV-2 virus strains. The population coverage analysis has found that cluster of differentiation 4 (CD4+) T-cell peptides showed higher cumulative population coverage over cluster of differentiation 8 (CD8+) peptides in the 16 different geographical regions of the world. We identified 12 peptides ((LTDEMIAQY, WTAGAAAYY, WMESEFRVY, IRASANLAA, FGAISSVLN, VKQLSSNFG, FAMQMAYRF, FGAGAALQI, YGFQPTNGVGYQ, LPDPSKPSKR, QTQTNSPRRARS and VITPGTNTSN) that are $80\hbox{--} 90\%$ identical with experimentally determined epitopes of SARS-CoV, and this will likely be beneficial for a quick progression of the vaccine design. Moreover, docking analysis suggested that the identified peptides are tightly bound in the groove of human leukocyte antigen molecules which can induce the T-cell response. Overall, this study allows us to determine potent peptide antigen targets in the SG on intuitive grounds, which opens up a new horizon in the coronavirus disease (COVID-19) research. However, this study needs experimental validation by in vitro and in vivo.

Discordance Between the Predicted Versus the Actually Recognized CD8+ T Cell Epitopes of HCMV pp65 Antigen and Aleatory Epitope Dominance

CD8+ T cell immune monitoring aims at measuring the size and functions of antigen-specific CD8+ T cell populations, thereby providing insights into cell-mediated immunity operational in a test subject. The selection of peptides for ex vivo CD8+ T cell detection is critical because within a complex antigen exists a multitude of potential epitopes that can be presented by HLA class I molecules. Further complicating this task, there is HLA class I polygenism and polymorphism which predisposes CD8+ T cell responses towards individualized epitope recognition profiles. In this study, we compare the actual CD8+ T cell recognition of a well-characterized model antigen, human cytomegalovirus (HCMV) pp65 protein, with its anticipated epitope coverage. Due to the abundance of experimentally defined HLA-A*02:01-restricted pp65 epitopes, and because in silico epitope predictions are most advanced for HLA-A*02:01, we elected to focus on subjects expressing this allele. In each test subject, every possible CD8+ T cell epitope was systematically covered testing 553 individual peptides that walk the sequence of pp65 in steps of single amino acids. Highly individualized CD8+ T cell response profiles with aleatory epitope recognition patterns were observed. No correlation was found between epitopes' ranking on the prediction scale and their actual immune dominance. Collectively, these data suggest that accurate CD8+ T cell immune monitoring may necessitate reliance on agnostic mega peptide pools, or brute force mapping, rather than electing individual peptides as representative epitopes for tetramer and other multimer labeling of surface antigen receptors.

Predicted Structural Variability of Mycobacterium tuberculosis PPE18 Protein With Immunological Implications Among Clinical Strains

Recent advancements in vaccinology have led to the development of the M72/AS01E subunit vaccine, of which the major component is the Mycobacterium tuberculosis (MTB) PPE18 protein. Previous studies have demonstrated the genetic variability of the gene encoding PPE18 protein and the resulting peptide changes in diverse clinical strains of MTB; however, none have modeled the structural changes resulting from these peptide changes and their immunological implications. In this study, we investigated the structural predictions of 29 variant PPE18 proteins previously reported. We found evidence that PPE18 is at least a two-domain protein, with a highly conserved first domain and a largely variable second domain that has different coevolutionary clusters. Further, we investigated putative epitope sites in the clinical variants of PPE18 using prediction software. We found a negative relationship between T-cell epitope number and residue variability, while B-cell epitope likelihood was positively correlated with residue variability. Moreover, we found far more residues in the second domain predicted to be B-cell epitopes compared with the first domain. These results suggest an important functional role of the first domain and a role in immune evasion for the second, which extends our knowledge base of the basic biology of the PPE18 protein and indicates the need for further study into non-traditional immunological responses to TB.

Identification of 22 N-glycosites on spike glycoprotein of SARS-CoV-2 and accessible surface glycopeptide motifs: Implications for vaccination and antibody therapeutics

Coronaviruses hijack human enzymes to assemble the sugar coat on their spike glycoproteins. The mechanisms by which human antibodies may recognize the antigenic viral peptide epitopes hidden by the sugar coat are unknown. Glycosylation by insect cells differs from the native form produced in human cells, but insect cell-derived influenza vaccines have been approved by the US Food and Drug Administration. In this study, we analyzed recombinant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein secreted from BTI-Tn-5B1-4 insect cells, by trypsin and chymotrypsin digestion followed by mass spectrometry analysis. We acquired tandem mass spectrometry (MS/MS) spectrums for glycopeptides of all 22 predicted N-glycosylated sites. We further analyzed the surface accessibility of spike proteins according to cryogenic electron microscopy and homolog-modeled structures and available antibodies that bind to SARS-CoV-1. All 22 N-glycosylated sites of SARS-CoV-2 are modified by high-mannose N-glycans. MS/MS fragmentation clearly established the glycopeptide identities. Electron densities of glycans cover most of the spike receptor-binding domain of SARS-CoV-2, except YQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQ, similar to a region FSPDGKPCTPPALNCYWPLNDYGFYTTTGIGYQ in SARS-CoV-1. Other surface-exposed domains include those located on central helix, connecting region, heptad repeats and N-terminal domain. Because the majority of antibody paratopes bind to the peptide portion with or without sugar modification, we propose a snake-catching model for predicted paratopes: a minimal length of peptide is first clamped by a paratope and sugar modifications close to the peptide either strengthen or do not hinder the binding.

Subtractive proteomics to identify targets for vaccine development against vancomycin-resistant Enterococcus faecalis

Aim: Due to the increased level of vancomycin resistance in Enterococci species, an aggressive treatment involving targeted antibiotics is required to manage this frequently occurring infection. Materials & methods: Here, subtractive proteomics and reverse vaccinology approaches were employed to identify potential target and for the prediction of B cell and T cell epitopes against vancomycin-resistant Enterococcus faecalis (VRE V583). Results: The results exhibited the presence of 73 out of 805 non-homologous protein sequences in the proteome which can be employed as unique targets to develop the novel drugs and vaccine to counter the deadly infections caused by this microbe. Conclusion: The identified novel target in VRE V583 will equip our knowledge to design effective vaccine against probable protease EEP proteins.

Development of a novel platform of virus-like particle (VLP)-based vaccine against COVID-19 by exposing epitopes: an immunoinformatics approach

The emergence of a rapidly spreading and highly infectious coronavirus disease 2019 (COVID-19) outbreak by a novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a global pandemic with unprecedented social and economic dimensions. Therefore, the development of effective strategies is urgent to control the COVID-19 outbreak. According to recent investigations, cell entry of coronaviruses relies on binding of the viral spike glycoprotein to the host cellular receptors. Therefore, the present study aimed to predict immunogenic epitopes in silico by analysing the spike protein. In parallel, by screening the immunogenic SARS-CoV-2 spike-derived epitopes provided in the literature, we chose a set of epitopes that we believed would induce immunogenic response. Next, provided with the epitopes selected by using both approaches, we performed immunoinformatic analysis that mapped identically to the antigen regions and antigenic properties. Finally, after selecting a screened set of epitopes, we designed a novel virus-like particle vaccine optimized to be produced in plants by using molecular farming biotechnology techniques. Our assay may be used as a starting point for guiding experimental efforts towards the development of a vaccine against SARS-CoV-2.

Characterizing Epitope Binding Regions of Entire Antibody Panels by Combining Experimental and Computational Analysis of Antibody: Antigen Binding Competition

Vaccines and immunotherapies depend on the ability of antibodies to sensitively and specifically recognize particular antigens and specific epitopes on those antigens. As such, detailed characterization of antibody-antigen binding provides important information to guide development. Due to the time and expense required, high-resolution structural characterization techniques are typically used sparingly and late in a development process. Here, we show that antibody-antigen binding can be characterized early in a process for whole panels of antibodies by combining experimental and computational analyses of competition between monoclonal antibodies for binding to an antigen. Experimental "epitope binning" of monoclonal antibodies uses high-throughput surface plasmon resonance to reveal which antibodies compete, while a new complementary computational analysis that we call "dock binning" evaluates antibody-antigen docking models to identify why and where they might compete, in terms of possible binding sites on the antigen. Experimental and computational characterization of the identified antigenic hotspots then enables the refinement of the competitors and their associated epitope binding regions on the antigen. While not performed at atomic resolution, this approach allows for the group-level identification of functionally related monoclonal antibodies (i.e., communities) and identification of their general binding regions on the antigen. By leveraging extensive epitope characterization data that can be readily generated both experimentally and computationally, researchers can gain broad insights into the basis for antibody-antigen recognition in wide-ranging vaccine and immunotherapy discovery and development programs.

Contriving Multi-Epitope Subunit of Vaccine for COVID-19: Immunoinformatics Approaches

COVID-19 has recently become the most serious threat to public health, and its prevalence has been increasing at an alarming rate. The incubation period for the virus is ~1-14 days and all age groups may be susceptible to a fatality rate of about 5.9%. COVID-19 is caused by a novel single-stranded, positive (+) sense RNA beta coronavirus. The development of a vaccine for SARS-CoV-2 is an urgent need worldwide. Immunoinformatics approaches are both cost-effective and convenient, as in silico predictions can reduce the number of experiments needed. In this study, with the aid of immunoinformatics tools, we tried to design a multi-epitope vaccine that can be used for the prevention and treatment of COVID-19. The epitopes were computed by using B cells, cytotoxic T lymphocytes (CTL), and helper T lymphocytes (HTL) base on the proteins of SARS-CoV-2. A vaccine was devised by fusing together the B cell, HTL, and CTL epitopes with linkers. To enhance the immunogenicity, the β-defensin (45 mer) amino acid sequence, and pan-HLA DR binding epitopes (13aa) were adjoined to the N-terminal of the vaccine with the help of the EAAAK linker. To enable the intracellular delivery of the modeled vaccine, a TAT sequence (11aa) was appended to C-terminal. Linkers play vital roles in producing an extended conformation (flexibility), protein folding, and separation of functional domains, and therefore, make the protein structure more stable. The secondary and three-dimensional (3D) structure of the final vaccine was then predicted. Furthermore, the complex between the final vaccine and immune receptors (toll-like receptor-3 (TLR-3), major histocompatibility complex (MHC-I), and MHC-II) were evaluated by molecular docking. Lastly, to confirm the expression of the designed vaccine, the mRNA of the vaccine was enhanced with the aid of the Java Codon Adaptation Tool, and the secondary structure was generated from Mfold. Then we performed in silico cloning. The final vaccine requires experimental validation to determine its safety and efficacy in controlling SARS-CoV-2 infections.

Identification and Analysis of Unstructured, Linear B-Cell Epitopes in SARS-CoV-2 Virion Proteins for Vaccine Development

The efficacy of SARS-CoV-2 nucleic acid-based vaccines may be limited by proteolysis of the translated product due to anomalous protein folding. This may be the case for vaccines employing linear SARS-CoV-2 B-cell epitopes identified in previous studies since most of them participate in secondary structure formation. In contrast, we have employed a consensus of predictors for epitopic zones plus a structural filter for identifying 20 unstructured B-cell epitope-containing loops (uBCELs) in S, M, and N proteins. Phylogenetic comparison suggests epitope switching with respect to SARS-CoV in some of the identified uBCELs. Such events may be associated with the reported lack of serum cross-protection between the 2003 and 2019 pandemic strains. Incipient variability within a sample of 1639 SARS-CoV-2 isolates was also detected for 10 uBCELs which could cause vaccine failure. Intermediate stages of the putative epitope switch events were observed in bat coronaviruses in which additive mutational processes possibly facilitating evasion of the bat immune system appear to have taken place prior to transfer to humans. While there was some overlap between uBCELs and previously validated SARS-CoV B-cell epitopes, multiple uBCELs had not been identified in prior studies. Overall, these uBCELs may facilitate the development of biomedical products for SARS-CoV-2.

The combination of artificial intelligence and systems biology for intelligent vaccine design

INTRODUCTION

A new body of evidence depicts the applications of artificial intelligence and systems biology in vaccine design and development. The combination of both approaches shall revolutionize healthcare, accelerating clinical trial processes and reducing the costs and time involved in drug research and development.

AREAS COVERED

This review explores the basics of artificial intelligence and systems biology approaches in the vaccine development pipeline. The topics include a detailed description of epitope prediction tools for designing epitope-based vaccines and agent-based models for immune system response prediction, along with a focus on their potentiality to facilitate clinical trial phases.

EXPERT OPINION

Artificial intelligence and systems biology offer the opportunity to avoid the inefficiencies and failures that arise in the classical vaccine development pipeline. One promising solution is the combination of both methodologies in a multiscale perspective through an accurate pipeline. We are entering an 'in silico era' in which scientific partnerships, including a more and more increasing creation of an 'ecosystem' of collaboration and multidisciplinary approach, are relevant for addressing the long and risky road of vaccine discovery and development. In this context, regulatory guidance should be developed to qualify the in silico trials as evidence for intelligent vaccine development.

Epitope Selection for Fighting Visceral Leishmaniosis: Not All Peptides Function the Same Way

Visceral leishmaniosis (VL) caused by Leishmania infantum is a disease with an increasing prevalence worldwide. Treatments are expensive, toxic, and ineffective. Therefore, vaccination seems to be a promising approach to control VL. Peptide-based vaccination is a useful method due to its stability, absence of local side effects, and ease of scaling up. In this context, bioinformatics seems to facilitate the use of peptides, as this analysis can predict high binding affinity epitopes to MHC class I and II molecules of different species. We have recently reported the use of HisAK70 DNA immunization in mice to induce a resistant phenotype against L. major, L. infantum, and L. amazonensis infections. In the present study, we used bioinformatics tools to select promising multiepitope peptides (HisDTC and AK) from the polyprotein encoded in the HisAK70 DNA to evaluate their immunogenicity in the murine model of VL by L. infantum. Our results revealed that both multiepitope peptides were able to induce the control of VL in mice. Furthermore, HisDTC was able to induce a better cell-mediated immune response in terms of reduced parasite burden, protective cytokine profile, leishmanicidal enzyme modulation, and specific IgG2a isotype production in immunized mice, before and after infectious challenge. Overall, this study indicates that the HisDTC chimera may be considered a satisfactory tool to control VL because it is able to activate a potent CD4+ and CD8+ T-cell protective immune responses.

Prioritization of SARS-CoV-2 epitopes using a pan-HLA and global population inference approach

SARS-CoV-2 T cell response assessment and vaccine development may benefit from an approach that considers the global landscape of the human leukocyte antigen (HLA) proteins. We predicted the binding affinity between 9-mer and 15-mer peptides from the SARS-CoV-2 peptidome for 9,360 class I and 8,445 class II HLA alleles, respectively. We identified 368,145 unique combinations of peptide-HLA complexes (pMHCs) with a predicted binding affinity less than 500nM, and observed significant overlap between class I and II predicted pMHCs. Using simulated populations derived from worldwide HLA frequency data, we identified sets of epitopes predicted in at least 90% of the population in 57 countries. We also developed a method to prioritize pMHCs for specific populations. Collectively, this public dataset and accessible user interface (Shiny app: https://rstudio-connect.parkerici.org/content/13/) can be used to explore the SARS-CoV-2 epitope landscape in the context of diverse HLA types across global populations.

Uncovering the Tumor Antigen Landscape: What to Know about the Discovery Process

According to the latest available data, cancer is the second leading cause of death, highlighting the need for novel cancer therapeutic approaches. In this context, immunotherapy is emerging as a reliable first-line treatment for many cancers, particularly metastatic melanoma. Indeed, cancer immunotherapy has attracted great interest following the recent clinical approval of antibodies targeting immune checkpoint molecules, such as PD-1, PD-L1, and CTLA-4, that release the brakes of the immune system, thus reviving a field otherwise poorly explored. Cancer immunotherapy mainly relies on the generation and stimulation of cytotoxic CD8 T lymphocytes (CTLs) within the tumor microenvironment (TME), priming T cells and establishing efficient and durable anti-tumor immunity. Therefore, there is a clear need to define and identify immunogenic T cell epitopes to use in therapeutic cancer vaccines. Naturally presented antigens in the human leucocyte antigen-1 (HLA-I) complex on the tumor surface are the main protagonists in evocating a specific anti-tumor CD8+ T cell response. However, the methodologies for their identification have been a major bottleneck for their reliable characterization. Consequently, the field of antigen discovery has yet to improve. The current review is intended to define what are today known as tumor antigens, with a main focus on CTL antigenic peptides. We also review the techniques developed and employed to date for antigen discovery, exploring both the direct elution of HLA-I peptides and the in silico prediction of epitopes. Finally, the last part of the review analyses the future challenges and direction of the antigen discovery field.

EpitoCore: Mining Conserved Epitope Vaccine Candidates in the Core Proteome of Multiple Bacteria Strains

In reverse vaccinology approaches, complete proteomes of bacteria are submitted to multiple computational prediction steps in order to filter proteins that are possible vaccine candidates. Most available tools perform such analysis only in a single strain, or a very limited number of strains. But the vast amount of genomic data had shown that most bacteria contain pangenomes, i.e., their genomic information contains core, conserved genes, and random accessory genes specific to each strain. Therefore, in reverse vaccinology methods it is of the utmost importance to define core proteins and core epitopes. EpitoCore is a decision-tree pipeline developed to fulfill that need. It provides surfaceome prediction of proteins from related strains, defines core proteins within those, calculate their immunogenicity, predicts epitopes for a given set of MHC alleles defined by the user, and then reports if epitopes are located extracellularly and if they are conserved among the core homologs. Pipeline performance is illustrated by mining peptide vaccine candidates in Mycobacterium avium hominissuis strains. From a total proteome of ~4,800 proteins per strain, EpitoCore predicted 103 highly immunogenic core homologs located at cell surface, many of those related to virulence and drug resistance. Conserved epitopes identified among these homologs allows the users to define sets of peptides with potential to immunize the largest coverage of tested HLA alleles using peptide-based vaccines. Therefore, EpitoCore is able to provide automated identification of conserved epitopes in bacterial pangenomic datasets.

CD4+ T Cells in the Blood of MS Patients Respond to Predicted Epitopes From B cell Receptors Found in Spinal Fluid

B cells are important pathogenic players in multiple sclerosis (MS), but their exact role is not known. We have previously demonstrated that B cells from cerebrospinal fluid (CSF) of MS patients can activate T cells that specifically recognize antigenic determinants (idiotopes) from their B cell receptors (BCRs). The aim of this study was to evaluate whether in silico prediction models could identify antigenic idiotopes of immunoglobulin heavy-chain variable (IGHV) transcriptomes in MS patients. We utilized a previously assembled dataset of CSF IGHV repertoires from MS patients. To guide selection of potential antigenic idiotopes, we used in silico predicted HLA-DR affinity, endosomal processing, as well as transcript frequency from nine MS patients. Idiotopes with predicted low affinity and low likelihood of cathepsins cleavage were inert controls. Peripheral blood mononuclear cells from these patients were stimulated with the selected idiotope peptides in presence of anti-CD40 for 12 h. T cells were then labeled for activation status with anti-CD154 antibodies and CD3+CD4+ T cells phenotyped as memory (CD45RO+) or naïve (CD45RO-), with potential for brain migration (CXCR3 and/or CCR6 expression). Anti-CD14 and -CD8 were utilized to exclude monocytes and CD8+ T cells. Unstimulated cells or insulin peptides were negative controls, and EBNA-1 peptides or CD3/CD28 beads were positive controls. The mean proportion of responding memory CD4+ T cells from all nine MS patients was significantly higher for idiotope peptides with predicted high HLA-DR affinity and high likelihood of cathepsin cleavage, than toward predicted inert peptides. Responses were mainly observed toward peptides affiliated with the CDR3 region. Activated memory CD4+ T cells expressed the chemokine receptor CCR6, affiliated with a Th17 phenotype and allowing passage into the central nervous system (CNS). This in vitro study suggests that that antigenic properties of BCR idiotopes can be identified in silico using HLA affinity and endosomal processing predictions. It further indicates that MS patients have a memory T cell repertoire capable of recognizing frequent BCR idiotopes found in endogenous CSF, and that these T cells express chemokine receptors allowing them to reach the CSF B cells expressing these idiotopes.

Immunoinformatics-aided identification of T cell and B cell epitopes in the surface glycoprotein of 2019-nCoV

The 2019 novel coronavirus (2019-nCoV) outbreak has caused a large number of deaths with thousands of confirmed cases worldwide, especially in East Asia. This study took an immunoinformatics approach to identify significant cytotoxic T lymphocyte (CTL) and B cell epitopes in the 2019-nCoV surface glycoprotein. Also, interactions between identified CTL epitopes and their corresponding major histocompatibility complex (MHC) class I supertype representatives prevalent in China were studied by molecular dynamics simulations. We identified five CTL epitopes, three sequential B cell epitopes and five discontinuous B cell epitopes in the viral surface glycoprotein. Also, during simulations, the CTL epitopes were observed to be binding MHC class I peptide-binding grooves via multiple contacts, with continuous hydrogen bonds and salt bridge anchors, indicating their potential in generating immune responses. Some of these identified epitopes can be potential candidates for the development of 2019-nCoV vaccines.

Computational tools for modern vaccine development

Vaccines play an essential role in controlling the rates of fatality and morbidity. Vaccines not only arrest the beginning of different diseases but also assign a gateway for its elimination and reduce toxicity. This review gives an overview of the possible uses of computational tools for vaccine design. Moreover, we have described the initiatives of utilizing the diverse computational resources by exploring the immunological databases for developing epitope-based vaccines, peptide-based drugs, and other resources of immunotherapeutics. Finally, the applications of multi-graft and multivalent scaffolding, codon optimization and antibodyomics tools in identifying and designing in silico vaccine candidates are described.

Development and Application of Computational Methods in Phage Display Technology

BACKGROUND

Phage display is a powerful and versatile technology for the identification of peptide ligands binding to multiple targets, which has been successfully employed in various fields, such as diagnostics and therapeutics, drug-delivery and material science. The integration of next generation sequencing technology with phage display makes this methodology more productive. With the widespread use of this technique and the fast accumulation of phage display data, databases for these data and computational methods have become an indispensable part in this community. This review aims to summarize and discuss recent progress in the development and application of computational methods in the field of phage display.

METHODS

We undertook a comprehensive search of bioinformatics resources and computational methods for phage display data via Google Scholar and PubMed. The methods and tools were further divided into different categories according to their uses.

RESULTS

We described seven special or relevant databases for phage display data, which provided an evidence-based source for phage display researchers to clean their biopanning results. These databases can identify and report possible target-unrelated peptides (TUPs), thereby excluding false-positive data from peptides obtained from phage display screening experiments. More than 20 computational methods for analyzing biopanning data were also reviewed. These methods were classified into computational methods for reporting TUPs, for predicting epitopes and for analyzing next generation phage display data.

CONCLUSION

The current bioinformatics archives, methods and tools reviewed here have benefitted the biopanning community. To develop better or new computational tools, some promising directions are also discussed.

A synthetic peptide analog of in silico-predicted immunogenic epitope unique to dengue virus serotype 2 NS1 antigen specifically binds immunoglobulin G antibodies raised in rabbits

Development of a serotyping-capable dengue detection test is hampered by the absence of an identified unique marker that can detect specific dengue virus (DENV) serotype. In the current commercially available antibody-capture diagnostic methods, immobilized nonstructural 1 (NS1) antigen indiscriminately binds and detects immunoglobulin M or immunoglobulin G against any serotype, thus limiting its capability to distinguish existing serotypes of dengue. Identification of dengue serotype is important because certain serotypes are associated with severe forms of dengue as well as dengue hemorrhagic fever. In this study, we aimed to identify an immunogenic epitope unique to DENV2 NS1 antigen and determine the binding specificity of its synthetic peptide mimotope to antibodies raised in animal models. Selection of a putative B-cell epitope from the reported DENV2 NS1 antigen was done using Kolaskar and Tongaonkar Antigenicity prediction, Emini surface accessibility prediction, and Parker hydrophilicity prediction available at the immune epitope database and analysis resource. Uniqueness of the B-cell epitope to DENV2 was analyzed by BLASTp. Immunogenicity of the synthetic peptide analog of the predicted immunogenic epitope was tested in rabbits. The binding specificity of the antibodies raised in animals and the synthetic peptide mimotope was tested by indirect ELISA. A synthetic peptide analog comprising the unique epitope of DENV2 located at the 170th-183rd position of DENV2 NS1 was found to be immunogenic in animal models. The antipeptide antibody produced in rabbits showed specific binding to the synthetic peptide mimotope of the predicted unique DENV2 NS1 immunogenic epitope.

Peptide-Major Histocompatibility Complex Class I Binding Prediction Based on Deep Learning With Novel Feature

Peptide-based vaccine development needs accurate prediction of the binding affinity between major histocompatibility complex I (MHC I) proteins and their peptide ligands. Nowadays more and more machine learning methods have been developed to predict binding affinity and some of them have become the popular tools. However most of them are designed by the shallow neural networks. Bengio said that deep neural networks can learn better fits with less data than shallow neural networks. In our case, some of the alleles only have dozens of peptide data. In addition, we transform each peptide into a characteristic matrix and input it into the model. As we know when dealing with the problem that the input is a matrix, convolutional neural network (CNN) can find the most critical features by itself. Obviously, compared with the traditional neural network model, CNN is more suitable for predicting binding affinity. Different from the previous studies which are based on blocks substitution matrix (BLOSUM), we used novel feature to do the prediction. Since we consider that the order of the sequence, hydropathy index, polarity and the length of the peptide could affect the binding affinity and the properties of these amino acids are key factors for their binding to MHC, we extracted these information from each peptide. In order to make full use of the data we have obtained, we have integrated different lengths of peptides into 15mer based on the binding mode of peptide to MHC I. In order to demonstrate that our method is reliable to predict peptide-MHC binding, we compared our method with several popular methods. The experiments show the superiority of our method.

Setting Up an Undergraduate Immunology Lab: Resources and Examples

Laboratory courses in immunology require a different skill set for their development than lecture courses. They vary widely in their form based on factors like institutional budget and class size, and also in the prioritization of learning goals centered around reinforcing lecture concepts and/or building fundamental skills in the field of immunology. Lab activities can come from a variety of sources including published research protocols, commercial kits, computer-based tools or simulations, and case studies. Each has their own strengths, which will be explored here. There are also important decisions to make about how students will report their data, and what level of guidance in interpreting data is best to enhance student learning and growth. Finally, methods like use of rubrics can help ensure fair and efficient grading, especially with skills-based learning goals. Periodic assessment is important to ensure that activities contribute effectively to student learning and to guide improvements to the lab course over time.