Using T Cell Receptor Repertoires to Understand the Principles of Adaptive Immune Recognition.

Adaptive immune recognition is mediated by antigen receptors on B and T cells generated by somatic recombination during lineage development. The high level of diversity resulting from this process posed technical limitations that previously limited the comprehensive analysis of adaptive immune recognition. Advances over the last ten years have produced data and approaches allowing insights into how T cells develop, evolutionary signatures of recombination and selection, and the features of T cell receptors that mediate epitope-specific binding and T cell activation. The size and complexity of these data have necessitated the generation of novel computational and analytical approaches, which are transforming how T cell immunology is conducted. Here we review the development and application of novel biological, theoretical, and computational methods for understanding T cell recognition and discuss the potential for improved models of receptor:antigen interactions.

A Perspective on the Role of Computational Models in Immunology.

This is an exciting time for immunology because the future promises to be replete with exciting new discoveries that can be translated to improve health and treat disease in novel ways. Immunologists are attempting to answer increasingly complex questions concerning phenomena that range from the genetic, molecular, and cellular scales to that of organs, whole animals or humans, and populations of humans and pathogens. An important goal is to understand how the many different components involved interact with each other within and across these scales for immune responses to emerge, and how aberrant regulation of these processes causes disease. To aid this quest, large amounts of data can be collected using high-throughput instrumentation. The nonlinear, cooperative, and stochastic character of the interactions between components of the immune system as well as the overwhelming amounts of data can make it difficult to intuit patterns in the data or a mechanistic understanding of the phenomena being studied. Computational models are increasingly important in confronting and overcoming these challenges. I first describe an iterative paradigm of research that integrates laboratory experiments, clinical data, computational inference, and mechanistic computational models. I then illustrate this paradigm with a few examples from the recent literature that make vivid the power of bringing together diverse types of computational models with experimental and clinical studies to fruitfully interrogate the immune system.

Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes.

CD4+ T cells orchestrate the adaptive immune response against pathogens and cancer by recognizing epitopes presented on class II major histocompatibility complex (MHC-II) molecules. The high polymorphism of MHC-II genes represents an important hurdle toward accurate prediction and identification of CD4+ T cell epitopes. Here we collected and curated a dataset of 627,013 unique MHC-II ligands identified by mass spectrometry. This enabled us to precisely determine the binding motifs of 88 MHC-II alleles across humans, mice, cattle, and chickens. Analysis of these binding specificities combined with X-ray crystallography refined our understanding of the molecular determinants of MHC-II motifs and revealed a widespread reverse-binding mode in HLA-DP ligands. We then developed a machine-learning framework to accurately predict binding specificities and ligands of any MHC-II allele. This tool improves and expands predictions of CD4+ T cell epitopes and enables us to discover viral and bacterial epitopes following the aforementioned reverse-binding mode.

Predictability of antigen binding based on short motifs in the antibody CDRH3.

Adaptive immune receptors, such as antibodies and T-cell receptors, recognize foreign threats with exquisite specificity. A major challenge in adaptive immunology is discovering the rules governing immune receptor-antigen binding in order to predict the antigen binding status of previously unseen immune receptors. Many studies assume that the antigen binding status of an immune receptor may be determined by the presence of a short motif in the complementarity determining region 3 (CDR3), disregarding other amino acids. To test this assumption, we present a method to discover short motifs which show high precision in predicting antigen binding and generalize well to unseen simulated and experimental data. Our analysis of a mutagenesis-based antibody dataset reveals 11 336 position-specific, mostly gapped motifs of 3-5 amino acids that retain high precision on independently generated experimental data. Using a subset of only 178 motifs, a simple classifier was made that on the independently generated dataset outperformed a deep learning model proposed specifically for such datasets. In conclusion, our findings support the notion that for some antibodies, antigen binding may be largely determined by a short CDR3 motif. As more experimental data emerge, our methodology could serve as a foundation for in-depth investigations into antigen binding signals.

Embracing computational immunology.

This Special Feature explores the fascinating field of Computational Immunology and features reviews on recent immunology research that used computational tools and concepts to understand the nexus of cancer immunology, autoimmunity and host-pathogen interactions.

Inherited human ezrin deficiency impairs adaptive immunity.

BACKGROUND: Inborn errors of immunity (IEI) are a group of monogenic diseases that confer susceptibility to infection, autoimmunity, and cancer. Despite the life-threatening consequences of some IEI, their genetic cause remains unknown in many patients. OBJECTIVE: We investigated a patient with an IEI of unknown genetic etiology. METHODS: Whole-exome sequencing identified a homozygous missense mutation of the gene encoding ezrin (EZR), substituting a threonine for an alanine at position 129. RESULTS: Ezrin is one of the subunits of the ezrin, radixin, and moesin (ERM) complex. The ERM complex links the plasma membrane to the cytoskeleton and is crucial for the assembly of an efficient immune response. The A129T mutation abolishes basal phosphorylation and decreases calcium signaling, leading to complete loss of function. Consistent with the pleiotropic function of ezrin in myriad immune cells, multidimensional immunophenotyping by mass and flow cytometry revealed that in addition to hypogammaglobulinemia, the patient had low frequencies of switched memory B cells, CD4+ and CD8+ T cells, MAIT, γδ T cells, and centralnaive CD4+ cells. CONCLUSIONS: Autosomal-recessive human ezrin deficiency is a newly recognized genetic cause of B-cell deficiency affecting cellular and humoral immunity.

Mass Spectrometry Based Immunopeptidomics Leads to Robust Predictions of Phosphorylated HLA Class I Ligands.

The presentation of peptides on class I human leukocyte antigen (HLA-I) molecules plays a central role in immune recognition of infected or malignant cells. In cancer, non-self HLA-I ligands can arise from many different alterations, including non-synonymous mutations, gene fusion, cancer-specific alternative mRNA splicing or aberrant post-translational modifications. Identifying HLA-I ligands remains a challenging task that requires either heavy experimental work for in vivo identification or optimized bioinformatics tools for accurate predictions. To date, no HLA-I ligand predictor includes post-translational modifications. To fill this gap, we curated phosphorylated HLA-I ligands from several immunopeptidomics studies (including six newly measured samples) covering 72 HLA-I alleles and retrieved a total of 2,066 unique phosphorylated peptides. We then expanded our motif deconvolution tool to identify precise binding motifs of phosphorylated HLA-I ligands. Our results reveal a clear enrichment of phosphorylated peptides among HLA-C ligands and demonstrate a prevalent role of both HLA-I motifs and kinase motifs on the presentation of phosphorylated peptides. These data further enabled us to develop and validate the first predictor of interactions between HLA-I molecules and phosphorylated peptides.

Immunopeptidomics toolkit library (IPTK): a python-based modular toolbox for analyzing immunopeptidomics data.

BACKGROUND: The human leukocyte antigen (HLA) proteins play a fundamental role in the adaptive immune system as they present peptides to T cells. Mass-spectrometry-based immunopeptidomics is a promising and powerful tool for characterizing the immunopeptidomic landscape of HLA proteins, that is the peptides presented on HLA proteins. Despite the growing interest in the technology, and the recent rise of immunopeptidomics-specific identification pipelines, there is still a gap in data-analysis and software tools that are specialized in analyzing and visualizing immunopeptidomics data. RESULTS: We present the IPTK library which is an open-source Python-based library for analyzing, visualizing, comparing, and integrating different omics layers with the identified peptides for an in-depth characterization of the immunopeptidome. Using different datasets, we illustrate the ability of the library to enrich the result of the identified peptidomes. Also, we demonstrate the utility of the library in developing other software and tools by developing an easy-to-use dashboard that can be used for the interactive analysis of the results. CONCLUSION: IPTK provides a modular and extendable framework for analyzing and integrating immunopeptidomes with different omics layers. The library is deployed into PyPI at https://pypi.org/project/IPTKL/ and into Bioconda at https://anaconda.org/bioconda/iptkl , while the source code of the library and the dashboard, along with the online tutorials are available at https://github.com/ikmb/iptoolkit .

The C-terminal extension landscape of naturally presented HLA-I ligands.

HLA-I molecules play a central role in antigen presentation. They typically bind 9- to 12-mer peptides, and their canonical binding mode involves anchor residues at the second and last positions of their ligands. To investigate potential noncanonical binding modes, we collected in-depth and accurate HLA peptidomics datasets covering 54 HLA-I alleles and developed algorithms to analyze these data. Our results reveal frequent (442 unique peptides) and statistically significant C-terminal extensions for at least eight alleles, including the common HLA-A03:01, HLA-A31:01, and HLA-A68:01. High resolution crystal structure of HLA-A68:01 with such a ligand uncovers structural changes taking place to accommodate C-terminal extensions and helps unraveling sequence and structural properties predictive of the presence of these extensions. Scanning viral proteomes with the C-terminal extension motifs identifies many putative epitopes and we demonstrate direct recognition by human CD8+ T cells of a 10-mer epitope from cytomegalovirus predicted to follow the C-terminal extension binding mode.

Genetic and structural analyses of affinity maturation in the humoral response to HIV-1.

Most broadly neutralizing antibodies (BNAbs) elicited in response to HIV-1 infection are extraordinarily mutated. One goal of HIV-1 vaccine development is to induce antibodies that are similar to the most potent and broad BNAbs isolated from infected subjects. The most effective BNAbs have very high mutation frequencies, indicative of the long periods of continual activation necessary to acquire the BNAb phenotype through affinity maturation. Understanding the mutational patterns that define the maturation pathways in BNAb development is critical to vaccine design efforts to recapitulate through vaccination the successful routes to neutralization breadth and potency that have occurred in natural infection. Studying the mutational changes that occur during affinity maturation, however, requires accurate partitioning of sequence data into B-cell clones and identification of the starting point of a B-cell clonal lineage, the initial V(D)J rearrangement. Here, we describe the statistical framework we have used to perform these tasks. Through the recent advancement of these and similar computational methods, many HIV-1 ancestral antibodies have been inferred, synthesized and their structures determined. This has allowed, for the first time, the investigation of the structural mechanisms underlying the affinity maturation process in HIV-1 antibody development. Here, we review what has been learned from this atomic-level structural characterization of affinity maturation in HIV-1 antibodies and the implications for vaccine design.

Implementing sequence-based antigenic distance calculation into immunological shape space model.

BACKGROUND: In 2009, a novel influenza vaccine was distributed worldwide to combat the H1N1 influenza "swine flu" pandemic. However, antibodies induced by the vaccine display differences in their specificity and cross-reactivity dependent on pre-existing immunity. Here, we present a computational model that can capture the effect of pre-existing immunity on influenza vaccine responses. The model predicts the region of the virus hemagglutinin (HA) protein targeted by antibodies after vaccination as well as the level of cross-reactivity induced by the vaccine. We tested our model by simulating a scenario similar to the 2009 pandemic vaccine and compared the results to antibody binding data obtained from human subjects vaccinated with the monovalent 2009 H1N1 influenza vaccine. RESULTS: We found that both specificity and cross-reactivity of the antibodies induced by the 2009 H1N1 influenza HA protein were affected by the viral strain the individual was originally exposed. Specifically, the level of antigenic relatedness between the original exposure HA antigen and the 2009 HA protein affected antigenic-site immunodominance. Moreover, antibody cross-reactivity was increased when the individual's pre-existing immunity was specific to an HA protein antigenically distinct from the 2009 pandemic strain. Comparison of simulation data with antibody binding data from human serum samples demonstrated qualitative and quantitative similarities between the model and real-life immune responses to the 2009 vaccine. CONCLUSION: We provide a novel method to evaluate expected outcomes in antibody specificity and cross-reactivity after influenza vaccination in individuals with different influenza HA antigen exposure histories. The model produced similar outcomes as what has been previously reported in humans after receiving the 2009 influenza pandemic vaccine. Our results suggest that differences in cross-reactivity after influenza vaccination should be expected in individuals with different exposure histories.

How to dissect the plasticity of antigen-specific immune response: a tissue perspective.

Generation of antigen-specific humoral responses following vaccination or infection requires the maturation and function of highly specialized immune cells in secondary lymphoid organs (SLO), such as lymph nodes or tonsils. Factors that orchestrate the dynamics of these cells are still poorly understood. Currently, experimental approaches that enable a detailed description of the function of the immune system in SLO have been mainly developed and optimized in animal models. Conversely, methodological approaches in humans are mainly based on the use of blood-associated material because of the challenging access to tissues. Indeed, only few studies in humans were able to provide a discrete description of the complex network of cytokines, chemokines and lymphocytes acting in tissues after antigenic challenge. Furthermore, even fewer data are currently available on the interaction occurring within the complex micro-architecture of the SLO. This information is crucial in order to design particular vaccination strategies, especially for patients affected by chronic and immune compromising medical conditions who are under-vaccinated or who respond poorly to immunizations. Analysis of immune cells in different human tissues by high-throughput technologies, able to obtain data ranging from gene signature to protein expression and cell phenotypes, is needed to dissect the peculiarity of each immune cell in a definite human tissue. The main aim of this review is to provide an in-depth description of the current available methodologies, proven evidence and future perspectives in the analysis of immune mechanisms following immunization or infections in SLO.

A personalized computational model of edema formation in myocarditis based on long-axis biventricular MRI images.

BACKGROUND: Myocarditis is defined as the inflammation of the myocardium, i.e. the cardiac muscle. Among the reasons that lead to this disease, we may include infections caused by a virus, bacteria, protozoa, fungus, and others. One of the signs of the inflammation is the formation of edema, which may be a consequence of the interaction between interstitial fluid dynamics and immune response. This complex physiological process was mathematically modeled using a nonlinear system of partial differential equations (PDE) based on porous media approach. By combing a model based on Biot's poroelasticity theory with a model for the immune response we developed a new hydro-mechanical model for inflammatory edema. To verify this new computational model, T2 parametric mapping obtained by Magnetic Resonance (MR) imaging was used to identify the region of edema in a patient diagnosed with unspecific myocarditis. RESULTS: A patient-specific geometrical model was created using MRI images from the patient with myocarditis. With this model, edema formation was simulated using the proposed hydro-mechanical mathematical model in a two-dimensional domain. The computer simulations allowed us to correlate spatiotemporal dynamics of representative cells of the immune systems, such as leucocytes and the pathogen, with fluid accumulation and cardiac tissue deformation. CONCLUSIONS: This study demonstrates that the proposed mathematical model is a very promising tool to better understand edema formation in myocarditis. Simulations obtained from a patient-specific model reproduced important aspects related to the formation of cardiac edema, its area, position, and shape, and how these features are related to immune response.

Sustained antigen availability during germinal center initiation enhances antibody responses to vaccination.

Natural infections expose the immune system to escalating antigen and inflammation over days to weeks, whereas nonlive vaccines are single bolus events. We explored whether the immune system responds optimally to antigen kinetics most similar to replicating infections, rather than a bolus dose. Using HIV antigens, we found that administering a given total dose of antigen and adjuvant over 1-2 wk through repeated injections or osmotic pumps enhanced humoral responses, with exponentially increasing (exp-inc) dosing profiles eliciting >10-fold increases in antibody production relative to bolus vaccination post prime. Computational modeling of the germinal center response suggested that antigen availability as higher-affinity antibodies evolve enhances antigen capture in lymph nodes. Consistent with these predictions, we found that exp-inc dosing led to prolonged antigen retention in lymph nodes and increased Tfh cell and germinal center B-cell numbers. Thus, regulating the antigen and adjuvant kinetics may enable increased vaccine potency.

Development of an ancestral DC and TLR4-inducing multi-epitope peptide vaccine against the spike protein of SARS-CoV and SARS-CoV-2 using the advanced immunoinformatics approaches.

The oldest human coronavirus that started pandemics is severe acute respiratory syndrome virus (SARS-CoV). While SARS-CoV was eradicated, its new version, SARS-CoV2, caused the global pandemic of COVID-19. Evidence highlights the harmful events orchestrated by these viruses are mediated by Spike (S)P protein. Experimental epitopes of the S protein which were overlapping and ancestral between SARS-CoV and SARS-CoV-2 were obtained from the immune epitopes database (IEDB). The epitopes were then assembled in combination with a 50 S ribosomal protein L7/L12 adjuvant, a Mycobacterium tuberculosis-derived element and mediator of dendritic cells (DCs) and toll-like receptor 4 (TLR4). The immunogenic sequence was modeled by the GalaxyWeb server. After the improvement and validation of the protein structure, the physico-chemical properties and immune simulation were performed. To investigate the interaction with TLR3/4, Molecular Dynamics Simulation (MDS) was used. By merging the 17 B- and T-lymphocyte (HTL/CTL) epitopes, the vaccine sequence was created. Also, the Ramachandran plot presented that most of the residues were located in the most favorable and allowed areas. Moreover, SnapGene was successful in cloning the DNA sequence linked to our vaccine in the intended plasmid. A sequence was inserted between the XhoI and SacI position of the pET-28a (+) vector, and simulating the agarose gel revealed the existence of the inserted gene in the cloned plasmid with SARS vaccine (SARSV) construct, which has a 6565 bp in length overall. In terms of cytokines/IgG response, immunological simulation revealed a strong immune response. The stabilized vaccine showed strong interactions with TLR3/4, according to Molecular Dynamics Simulation (MDS) analysis. The present ancestral vaccine targets common sequences which seem to be valuable targets even for the new variant SARS-CoV-2.

Assessing the immunogenicity risk of salmon calcitonin peptide impurities using in silico and in vitro methods.

Advances in synthetic peptide synthesis have enabled rapid and cost-effective peptide drug manufacturing. For this reason, peptide drugs that were first produced using recombinant DNA (rDNA) technology are now being produced using solid- and liquid-phase peptide synthesis. While peptide synthesis has some advantages over rDNA expression methods, new peptide-related impurities that differ from the active pharmaceutical ingredient (API) may be generated during synthesis. These impurity byproducts of the original peptide sequence feature amino acid insertions, deletions, and side-chain modifications that may alter the immunogenicity risk profile of the drug product. Impurities resulting from synthesis have become the special focus of regulatory review and approval for human use, as outlined in the FDA's Center for Drug Evaluation and Research guidance document, "ANDAs for Certain Highly Purified Synthetic Peptide Drug Products That Refer to Listed Drugs of rDNA Origin," published in 2021. This case study illustrates how in silico and in vitro methods can be applied to assess the immunogenicity risk of impurities that may be present in synthetic generic versions of the salmon calcitonin (SCT) drug product. Sponsors of generic drug abbreviated new drug applications (ANDAs) should consider careful control of these impurities (for example, keeping the concentration of the immunogenic impurities below the cut-off recommended by FDA regulators). Twenty example SCT impurities were analyzed using in silico tools and assessed as having slightly more or less immunogenic risk potential relative to the SCT API peptide. Class II human leukocyte antigen (HLA)-binding assays provided independent confirmation that a 9-mer sequence present in the C-terminus of SCT binds promiscuously to multiple HLA DR alleles, while T-cell assays confirmed the expected T-cell responses to SCT and selected impurities. In silico analysis combined with in vitro assays that directly compare the API to each individual impurity peptide may be a useful approach for assessing the potential immunogenic risk posed by peptide impurities that are present in generic drug products.

How to Predict Binding Specificity and Ligands for New MHC-II Alleles with MixMHC2pred.

MHC-II molecules are key mediators of antigen presentation in vertebrate species and bind to their ligands with high specificity. The very high polymorphism of MHC-II genes within species and the fast-evolving nature of these genes across species has resulted in tens of thousands of different alleles, with hundreds of new alleles being discovered yearly through large sequencing projects in different species. Here we describe how to use MixMHC2pred to predict the binding specificity of any MHC-II allele directly from its amino acid sequence. We then show how both MHC-II ligands and CD4+ T cell epitopes can be predicted in different species with our approach. MixMHC2pred is available at http://mixmhc2pred.gfellerlab.org/ .

Immunogenicity risk assessment of synthetic peptide drugs and their impurities.

Peptide drugs play an important part in medicine owing to their many therapeutic applications. Of the 80 peptide drugs approved for use in humans, at least five are now off-patent and are consequently being developed as generic alternatives to the originator products. To accelerate access to generic products, the FDA has proposed new regulatory pathways that do not require direct comparisons of generics to originators in clinical trials. The 'Abbreviated New Drug Application' (ANDA) pathway recommends that sponsors provide information on any new impurities in the generic drug, compared with the originator product, because the impurities can have potential to elicit unwanted immune responses owing to the introduction of T-cell epitopes. This review describes how peptide drug impurities can elicit unexpected immunogenicity and describes a framework for performing immunogenicity risk assessment of all types of bioactive peptide products. Although this report primarily focuses on generic peptides and their impurities, the approach might also be of interest for developers of novel peptide drugs who are preparing their products for an initial regulatory review.

Elucidation of novel compounds and epitope-based peptide vaccine design against C30 endopeptidase regions of SARS-CoV-2 using immunoinformatics approaches.

There has been progressive improvement in immunoinformatics approaches for epitope-based peptide design. Computational-based immune-informatics approaches were applied to identify the epitopes of SARS-CoV-2 to develop vaccines. The accessibility of the SARS-CoV-2 protein surface was analyzed, and hexa-peptide sequences (KTPKYK) were observed having a maximum score of 8.254, located between amino acids 97 and 102, whereas the FSVLAC at amino acids 112 to 117 showed the lowest score of 0.114. The surface flexibility of the target protein ranged from 0.864 to 1.099 having amino acid ranges of 159 to 165 and 118 to 124, respectively, harboring the FCYMHHM and YNGSPSG hepta-peptide sequences. The surface flexibility was predicted, and a 0.864 score was observed from amino acids 159 to 165 with the hepta-peptide (FCYMHHM) sequence. Moreover, the highest score of 1.099 was observed between amino acids 118 and 124 against YNGSPSG. B-cell epitopes and cytotoxic T-lymphocyte (CTL) epitopes were also identified against SARS-CoV-2. In molecular docking analyses, -0.54 to -26.21 kcal/mol global energy was observed against the selected CTL epitopes, exhibiting binding solid energies of -3.33 to -26.36 kcal/mol. Based on optimization, eight epitopes (SEDMLNPNY, GSVGFNIDY, LLEDEFTPF, DYDCVSFCY, GTDLEGNFY, QTFSVLACY, TVNVLAWLY, and TANPKTPKY) showed reliable findings. The study calculated the associated HLA alleles with MHC-I and MHC-II and found that MHC-I epitopes had higher population coverage (0.9019% and 0.5639%) than MHC-II epitopes, which ranged from 58.49% to 34.71% in Italy and China, respectively. The CTL epitopes were docked with antigenic sites and analyzed with MHC-I HLA protein. In addition, virtual screening was conducted using the ZINC database library, which contained 3,447 compounds. The 10 top-ranked scrutinized molecules (ZINC222731806, ZINC077293241, ZINC014880001, ZINC003830427, ZINC030731133, ZINC003932831, ZINC003816514, ZINC004245650, ZINC000057255, and ZINC011592639) exhibited the least binding energy (-8.8 to -7.5 kcal/mol). The molecular dynamics (MD) and immune simulation data suggest that these epitopes could be used to design an effective SARS-CoV-2 vaccine in the form of a peptide-based vaccine. Our identified CTL epitopes have the potential to inhibit SARS-CoV-2 replication.

A robust deep learning workflow to predict CD8 + T-cell epitopes.

BACKGROUND: T-cells play a crucial role in the adaptive immune system by triggering responses against cancer cells and pathogens, while maintaining tolerance against self-antigens, which has sparked interest in the development of various T-cell-focused immunotherapies. However, the identification of antigens recognised by T-cells is low-throughput and laborious. To overcome some of these limitations, computational methods for predicting CD8 + T-cell epitopes have emerged. Despite recent developments, most immunogenicity algorithms struggle to learn features of peptide immunogenicity from small datasets, suffer from HLA bias and are unable to reliably predict pathology-specific CD8 + T-cell epitopes. METHODS: We developed TRAP (T-cell recognition potential of HLA-I presented peptides), a robust deep learning workflow for predicting CD8 + T-cell epitopes from MHC-I presented pathogenic and self-peptides. TRAP uses transfer learning, deep learning architecture and MHC binding information to make context-specific predictions of CD8 + T-cell epitopes. TRAP also detects low-confidence predictions for peptides that differ significantly from those in the training datasets to abstain from making incorrect predictions. To estimate the immunogenicity of pathogenic peptides with low-confidence predictions, we further developed a novel metric, RSAT (relative similarity to autoantigens and tumour-associated antigens), as a complementary to 'dissimilarity to self' from cancer studies. RESULTS: TRAP was used to identify epitopes from glioblastoma patients as well as SARS-CoV-2 peptides, and it outperformed other algorithms in both cancer and pathogenic settings. TRAP was especially effective at extracting immunogenicity-associated properties from restricted data of emerging pathogens and translating them onto related species, as well as minimising the loss of likely epitopes in imbalanced datasets. We also demonstrated that the novel metric termed RSAT was able to estimate immunogenic of pathogenic peptides of various lengths and species. TRAP implementation is available at: https://github.com/ChloeHJ/TRAP . CONCLUSIONS: This study presents a novel computational workflow for accurately predicting CD8 + T-cell epitopes to foster a better understanding of antigen-specific T-cell response and the development of effective clinical therapeutics.