Robust T Cell Immunity in Convalescent Individuals with Asymptomatic or Mild COVID-19

SARS-CoV-2-specific memory T cells will likely prove critical for long-term immune protection against COVID-19. Here, we systematically mapped the functional and phenotypic landscape of SARS-CoV-2-specific T cell responses in unexposed individuals, exposed family members, and individuals with acute or convalescent COVID-19. Acute-phase SARS-CoV-2-specific T cells displayed a highly activated cytotoxic phenotype that correlated with various clinical markers of disease severity, whereas convalescent-phase SARS-CoV-2-specific T cells were polyfunctional and displayed a stem-like memory phenotype. Importantly, SARS-CoV-2-specific T cells were detectable in antibody-seronegative exposed family members and convalescent individuals with a history of asymptomatic and mild COVID-19. Our collective dataset shows that SARS-CoV-2 elicits broadly directed and functionally replete memory T cell responses, suggesting that natural exposure or infection may prevent recurrent episodes of severe COVID-19.

Monumental Misunderstandings

A statue of stainless steel cast in China and placed at the entrance of the new National Stadium in Mozambique sparked controversy between Chinese donors and Mozambican recipients in the period leading up to the stadium’s 2011 inauguration. Based on ethnographic fieldwork among the Mozambican and Chinese nationals involved in the project, we explore the multiple misunderstandings surrounding the statue and show how they came to define Sino-Mozambican relations. Entextualized through materiality, the misunderstandings assumed a monumental form in the statue, and the message of mutual incomprehension continued to reverberate across the social terrain of Sino-Mozambican relations long after the statue itself had been removed. Misunderstandings, we argue, should not be dismissed as ephemeral communicative glitches, but seen as productive events that structure social relations.

A Systematic, Unbiased Mapping of CD8+ and CD4+ T Cell Epitopes in Yellow Fever Vaccinees

Examining CD8+ and CD4+ T cell responses after primary Yellow Fever vaccination in a cohort of 210 volunteers, we have identified and tetramer-validated 92 CD8+ and 50 CD4+ T cell epitopes, many inducing strong and prevalent (i.e., immunodominant) T cell responses. Restricted by 40 and 14 HLA-class I and II allotypes, respectively, these responses have wide population coverage and might be of considerable academic, diagnostic and therapeutic interest. The broad coverage of epitopes and HLA overcame the otherwise confounding effects of HLA diversity and non-HLA background providing the first evidence of T cell immunodomination in humans. Also, double-staining of CD4+ T cells with tetramers representing the same HLA-binding core, albeit with different flanking regions, demonstrated an extensive diversification of the specificities of many CD4+ T cell responses. We suggest that this could reduce the risk of pathogen escape, and that multi-tetramer staining is required to reveal the true magnitude and diversity of CD4+ T cell responses. Our T cell epitope discovery approach uses a combination of (1) overlapping peptides representing the entire Yellow Fever virus proteome to search for peptides containing CD4+ and/or CD8+ T cell epitopes, (2) predictors of peptide-HLA binding to suggest epitopes and their restricting HLA allotypes, (3) generation of peptide-HLA tetramers to identify T cell epitopes, and (4) analysis of ex vivo T cell responses to validate the same. This approach is systematic, exhaustive, and can be done in any individual of any HLA haplotype. It is all-inclusive in the sense that it includes all protein antigens and peptide epitopes, and encompasses both CD4+ and CD8+ T cell epitopes. It is efficient and, importantly, reduces the false discovery rate. The unbiased nature of the T cell epitope discovery approach presented here should support the refinement of future peptide-HLA class I and II predictors and tetramer technologies, which eventually should cover all HLA class I and II isotypes. We believe that future investigations of emerging pathogens (e.g., SARS-CoV-2) should include population-wide T cell epitope discovery using blood samples from patients, convalescents and/or long-term survivors, who might all hold important information on T cell epitopes and responses.

Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope Prediction

Human Leukocyte Antigen class II (HLA-II) molecules present peptides to T lymphocytes and play an important role in adaptive immune responses. Characterizing the binding specificity of single HLA-II molecules has profound impacts for understanding cellular immunity, identifying the cause of autoimmune diseases, for immunotherapeutics, and vaccine development. Here, novel high-density peptide microarray technology combined with machine learning techniques were used to address this task at an unprecedented level of high-throughput. Microarrays with over 200,000 defined peptides were assayed with four exemplary HLA-II molecules. Machine learning was applied to mine the signals. The comparison of identified binding motifs, and power for predicting eluted ligands and CD4+ epitope datasets to that obtained using NetMHCIIpan-3.2, confirmed a high quality of the chip readout. These results suggest that the proposed microarray technology offers a novel and unique platform for large-scale unbiased interrogation of peptide binding preferences of HLA-II molecules.

NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data

Major histocompatibility complex (MHC) molecules are expressed on the cell surface, where they present peptides to T cells, which gives them a key role in the development of T-cell immune responses. MHC molecules come in two main variants: MHC Class I (MHC-I) and MHC Class II (MHC-II). MHC-I predominantly present peptides derived from intracellular proteins, whereas MHC-II predominantly presents peptides from extracellular proteins. In both cases, the binding between MHC and antigenic peptides is the most selective step in the antigen presentation pathway. Therefore, the prediction of peptide binding to MHC is a powerful utility to predict the possible specificity of a T-cell immune response. Commonly MHC binding prediction tools are trained on binding affinity or mass spectrometry-eluted ligands. Recent studies have however demonstrated how the integration of both data types can boost predictive performances. Inspired by this, we here present NetMHCpan-4.1 and NetMHCIIpan-4.0, two web servers created to predict binding between peptides and MHC-I and MHC-II, respectively. Both methods exploit tailored machine learning strategies to integrate different training data types, resulting in state-of-the-art performance and outperforming their competitors. The servers are available at http://www.cbs.dtu.dk/services/NetMHCpan-4.1/ and http://www.cbs.dtu.dk/services/NetMHCIIpan-4.0/.

Immunoinformatics: Predicting Peptide-MHC Binding

Immunoinformatics is a discipline that applies methods of computer science to study and model the immune system. A fundamental question addressed by immunoinformatics is how to understand the rules of antigen presentation by MHC molecules to T cells, a process that is central to adaptive immune responses to infections and cancer. In the modern era of personalized medicine, the ability to model and predict which antigens can be presented by MHC is key to manipulating the immune system and designing strategies for therapeutic intervention. Since the MHC is both polygenic and extremely polymorphic, each individual possesses a personalized set of MHC molecules with different peptide-binding specificities, and collectively they present a unique individualized peptide imprint of the ongoing protein metabolism. Mapping all MHC allotypes is an enormous undertaking that cannot be achieved without a strong bioinformatics component. Computational tools for the prediction of peptide-MHC binding have thus become essential in most pipelines for T cell epitope discovery and an inescapable component of vaccine and cancer research. Here, we describe the development of several such tools, from pioneering efforts to the current state-of-the-art methods, that have allowed for accurate predictions of peptide binding of all MHC molecules, even including those that have not yet been characterized experimentally.

Immunopeptidomic Data Integration to Artificial Neural Networks Enhances Protein-Drug Immunogenicity Prediction

Recombinant DNA technology has, in the last decades, contributed to a vast expansion of the use of protein drugs as pharmaceutical agents. However, such biological drugs can lead to the formation of anti-drug antibodies (ADAs) that may result in adverse effects, including allergic reactions and compromised therapeutic efficacy. Production of ADAs is most often associated with activation of CD4 T cell responses resulting from proteolysis of the biotherapeutic and loading of drug-specific peptides into major histocompatibility complex (MHC) class II on professional antigen-presenting cells. Recently, readouts from MHC-associated peptide proteomics (MAPPs) assays have been shown to correlate with the presence of CD4 T cell epitopes. However, the limited sensitivity of MAPPs challenges its use as an immunogenicity biomarker. In this work, MAPPs data was used to construct an artificial neural network (ANN) model for MHC class II antigen presentation. Using Infliximab and Rituximab as showcase stories, the model demonstrated an unprecedented performance for predicting MAPPs and CD4 T cell epitopes in the context of protein-drug immunogenicity, complementing results from MAPPs assays and outperforming conventional prediction models trained on binding affinity data.

Evaluation of T-Cell Responses Against Shared Melanoma Associated Antigens and Predicted Neoantigens in Cutaneous Melanoma Patients Treated With the CSF-470 Allogeneic Cell Vaccine Plus BCG and GM-CSF

The CSF-470 vaccine consists of lethally-irradiated allogeneic cells derived from four cutaneous melanoma cell lines administered plus BCG and GM-CSF as adjuvants. In an adjuvant phase II study vs. IFN-α2b, the vaccine significantly prolonged the distant metastasis-free survival (DMFS) of stages IIB-IIC-III melanoma patients with evidence of the induction of immune responses against vaccine cells. Purpose: The aim of this study was to analyze the antigens against which the immune response was induced, as well as the T-helper profile and lytic ability of immune cells after CSF-470 treatment. Methods: HLA-restricted peptides from tumor-associated antigens (TAAs) were selected from TANTIGEN database for 13 evaluable vaccinated patients. In addition, for patient #006 (pt#006), tumor somatic variants were identified by NGS and candidate neoAgs were selected by predicted HLA binding affinity and similarity between wild type (wt) and mutant peptides. The patient's PBMC reactivity against selected peptides was detected by IFNγ-ELISPOT. T-helper transcriptional profile was determined by quantifying GATA-3, T-bet, and FOXP3 mRNA by RT-PCR, and intracellular cytokines were analyzed by flow cytometry. Autologous tumor cell lysis by PBMC was assessed in an in vitro calcein release assay. Results: Vaccinated patient's PBMC reactivity against selected TAAs derived peptides showed a progressive increase in the number of IFNγ-producing cells throughout the 2-yr vaccination protocol. ELISPOT response correlated with delayed type hypersensitivity (DTH) reaction to CSF-470 vaccine cells. Early upregulation of GATA-3 and Foxp3 mRNA, as well as an increase in CD4+IL4+cells, was associated with a low DMFS. Also, IFNγ response against 9/73 predicted neoAgs was evidenced in the case of pt#006; 7/9 emerged after vaccination. We verified in pt# 006 that post-vaccination PBMC boosted in vitro with the vaccine lysate were able to lyse autologous tumor cells. Conclusions: A progressive increase in the immune response against TAAs expressed in the vaccine and in the patient's tumor was induced by CSF-470 vaccination. In pt#006, we demonstrated immune recognition of patient's specific neoAgs, which emerged after vaccination. These results suggest that an initial response against shared TAAs could further stimulate an immune response against autologous tumor neoAgs.

Adoptive cell therapy in combination with checkpoint inhibitors in ovarian cancer

Immune therapy is a promising field within oncology but has been unsuccessful in ovarian cancer (OC). Still, there is rationale and evidence supporting immune therapy in OC. We investigated the potential for adoptive cell therapy (ACT) from in vitro expanded tumor-infiltrating lymphocytes (TILs) in combination with checkpoint inhibitors (ICI) and conducted immunological testing of ex vivo expanded TILs (REP-TILs).

Six patients with late-stage metastatic high-grade serous OC were treated with immune therapy consisting of ipilimumab followed by surgery to obtain TILs and infusion of REP-TILs, low-dose IL-2 and nivolumab.

One patient achieved a partial response and 5 others experienced disease stabilization for up to 12 months. Analysis of the REP-TILs with flow- and mass-cytometry show primarily activated and differentiated effector memory T cells. REP-TILs showed in vitro reactivity and expression of inhibitory receptors, such as LAG-3 and PD-1. Furthermore, our data indicate that addition of ipilimumab therapy improves the T cell fold expansion during production, increase the level of CD8 T cell tumor reactivity, and favorably affect the T cell phenotype.

We show that the combination of ICI and ACT is feasible and safe. With one partial response and one long-lasting SD, we demonstrated the potential of ACT in OC.

HLA Class II Specificity Assessed by High-Density Peptide Microarray Interactions

The ability to predict and/or identify MHC binding peptides is an essential component of T cell epitope discovery, something that ultimately should benefit the development of vaccines and immunotherapies. In particular, MHC class I prediction tools have matured to a point where accurate selection of optimal peptide epitopes is possible for virtually all MHC class I allotypes; in comparison, current MHC class II (MHC-II) predictors are less mature. Because MHC-II restricted CD4⁺ T cells control and orchestrated most immune responses, this shortcoming severely hampers the development of effective immunotherapies. The ability to generate large panels of peptides and subsequently large bodies of peptide-MHC-II interaction data are key to the solution of this problem, a solution that also will support the improvement of bioinformatics predictors, which critically relies on the availability of large amounts of accurate, diverse, and representative data. In this study, we have used rHLA-DRB1*01:01 and HLA-DRB1*03:01 molecules to interrogate high-density peptide arrays, in casu containing 70,000 random peptides in triplicates. We demonstrate that the binding data acquired contains systematic and interpretable information reflecting the specificity of the HLA-DR molecules investigated, suitable of training predictors able to predict T cell epitopes and peptides eluted from human EBV-transformed B cells. Collectively, with a cost per peptide reduced to a few cents, combined with the flexibility of rHLA technology, this poses an attractive strategy to generate vast bodies of MHC-II binding data at an unprecedented speed and for the benefit of generating peptide-MHC-II binding data as well as improving MHC-II prediction tools.

In vitro 4-1BB stimulation promotes expansion of CD8+ tumor-infiltrating lymphocytes from various sarcoma subtypes

Tumor-specific tumor-infiltrating lymphocytes (TILs) can be in vitro expanded and have the ability to induce complete and durable tumor regression in some patients with melanoma following adoptive cell therapy (ACT). In this preclinical study, we investigated the feasibility of expanding TIL from sarcomas, as well as performing functional in vitro analyses on these. TILs were expanded in vitro by the use of IL2 stimulation with or without the addition of 4-1BB and CD3 antibodies. Phenotypical and functional analyses were mainly performed by flow cytometry. TILs were expanded from 25 of 28 (89%) tumor samples from patients with 9 different sarcoma subtypes. TILs were predominantly αβ T-cells of effector memory subtype with CD4⁺  dominance. In particular, CD8⁺ TIL highly expressed LAG3 and to a lesser degree PD-1 and BTLA. In total, 10 of 20 TIL cultures demonstrated in vitro recognition of autologous tumor. In some cases, the fraction of tumor-reactive T cells was more than 20%. 4-1BB stimulation augmented expansion kinetics and favored CD8⁺ occurrence. In conclusion, TIL expansion from sarcoma is feasible and expanded TILs highly express LAG3 and comprise multifunctional tumor-reactive T-cells.

Benchmarking predictions of MHC class I restricted T cell epitopes in a comprehensively studied model system

T cell epitope candidates are commonly identified using computational prediction tools in order to enable applications such as vaccine design, cancer neoantigen identification, development of diagnostics and removal of unwanted immune responses against protein therapeutics. Most T cell epitope prediction tools are based on machine learning algorithms trained on MHC binding or naturally processed MHC ligand elution data. The ability of currently available tools to predict T cell epitopes has not been comprehensively evaluated. In this study, we used a recently published dataset that systematically defined T cell epitopes recognized in vaccinia virus (VACV) infected C57BL/6 mice (expressing H-2D^b and H-2K^b), considering both peptides predicted to bind MHC or experimentally eluted from infected cells, making this the most comprehensive dataset of T cell epitopes mapped in a complex pathogen. We evaluated the performance of all currently publicly available computational T cell epitope prediction tools to identify these major epitopes from all peptides encoded in the VACV proteome. We found that all methods were able to improve epitope identification above random, with the best performance achieved by neural network-based predictions trained on both MHC binding and MHC ligand elution data (NetMHCPan-4.0 and MHCFlurry). Impressively, these methods were able to capture more than half of the major epitopes in the top N = 277 predictions within the N = 767,788 predictions made for distinct peptides of relevant lengths that can theoretically be encoded in the VACV proteome. These performance metrics provide guidance for immunologists as to which prediction methods to use, and what success rates are possible for epitope predictions when considering a highly controlled system of administered immunizations to inbred mice. In addition, this benchmark was implemented in an open and easy to reproduce format, providing developers with a framework for future comparisons against new tools.

Computational prediction tools are used to screen peptides to identify potential T cell epitope candidates. These tools, developed using machine learning methods, save time and resources in many immunological studies including vaccine discovery and cancer neoantigen identification. In addition to the already existing methods several epitope prediction tools are being developed these days but they lack a comprehensive and uniform evaluation to see which method performs best. In this study we did a comprehensive evaluation of publicly accessible MHC I restricted T cell epitope prediction tools using a recently published dataset of Vaccinia virus epitopes identified in the context of H-2D^b and H-2K^b. We found that methods based on artificial neural network architecture and trained on both MHC binding and ligand elution data showed very high performance (NetMHCPan-4.0 and MHCFlurry). This benchmark analysis will help immunologists to choose the right prediction method for their desired work and will also serve as a framework for tool developers to evaluate new prediction methods.

Interrogation of neoantigen-specific CD8 T cells in peripheral blood following PD-L1 blockade in patients with metastatic urothelial carcinoma (mUC)

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Background: Proliferation of CD8 T cells can be detected in the blood of cancer patients (pts) following a single dose of immune checkpoint blockade (ICB) and tends to be more robust in responding pts. Furthermore, tumor mutational burden (TMB) is seen to predict outcome to ICB across cancers. Mutation-derived neoepitopes presented on the tumor cell surface is believed to be recognized by T cells and are thus critical for tumor clearance. However, the capacity to mount a neoantigen T cell response and the kinetics in relation to ICB remain poorly understood. Methods: 24 pts with mUC were treated with atezolizumab (anti-PD-L1) 1200mg q3w on IMVigor 210 at MSKCC and included in here. Pt-specific neoepitopes were predicted based on whole-exome and RNA sequencing of pre-treatment archival tumors using the MuPeXI platform. Using DNA-barcode labelled pMHC multimers, we investigated CD8 T cell recognition of mutation-derived neoepitopes by screening pt PBMC samples pre- and post-treatment with atezolizumab (n = 85 PBMC samples). The kinetics of neoepitope-specific CD8 T cells were assessed for association with durable clinical benefit (DCB; defined as progression free survival > 6 mo). Results: Neoepitope peptide libraries of between 200-587 peptides were generated per pt (mean = 260 peptides per pt). 31 out of a combined 56 possible pt HLA types across the cohort were utilized for T cell analyses (mean four HLAs per pt). MHC multimer-based screening of pt PBMCs revealed detection of neoepitope-specific CD8 T cells in 22 of 24 pts pre-treatment (range one to 14 neoepitope responses) and 21 of 22 pts post-treatment (up to 273 weeks after trial start; one to 19 neoepitope responses). There were large inter- and intra-patient variations of neoepitope-specific CD8 T cell responses during treatment with the largest increases occurring at the 3-wk, post-treatment initiation timepoint. We observed that pts with DCB tend to raise a broader neoantigen T cell response than patients without DCB. 38% of pts without DCB and 67% of pts with DCB exhibited an increase in neoepitope-specific CD8 T cell responses within 3 wks of treatment initiation. Conclusions: Using high-throughput screening, pt-specific neoepitope reactive CD8 T cells could be detected pre- and post-treatment in pts with mUC treated with atezolizumab. Phenotypic characterization of neoepitope reactive CD8 T cells is ongoing. These data may help elucidate the dynamics and characteristics of the T cells of highest relevance to the ICB-induced, anti-tumor immune response.

IEDB-AR: immune epitope database-analysis resource in 2019

The Immune Epitope Database Analysis Resource (IEDB-AR, http://tools.iedb.org/) is a companion website to the IEDB that provides computational tools focused on the prediction and analysis of B and T cell epitopes. All of the tools are freely available through the public website and many are also available through a REST API and/or a downloadable command-line tool. A virtual machine image of the entire site is also freely available for non-commercial use and contains most of the tools on the public site. Here, we describe the tools and functionalities that are available in the IEDB-AR, focusing on the 10 new tools that have been added since the last report in the 2012 NAR webserver edition. In addition, many of the tools that were already hosted on the site in 2012 have received updates to newest versions, including NetMHC, NetMHCpan, BepiPred and DiscoTope. Overall, this IEDB-AR update provides a substantial set of updated and novel features for epitope prediction and analysis.

Improved Prediction of MHC II Antigen Presentation through Integration and Motif Deconvolution of Mass Spectrometry MHC Eluted Ligand Data

Major histocompatibility complex II (MHC II) molecules play a vital role in the onset and control of cellular immunity. In a highly selective process, MHC II presents peptides derived from exogenous antigens on the surface of antigen-presenting cells for T cell scrutiny. Understanding the rules defining this presentation holds critical insights into the regulation and potential manipulation of the cellular immune system. Here, we apply the NNAlign_MA machine learning framework to analyze and integrate large-scale eluted MHC II ligand mass spectrometry (MS) data sets to advance prediction of CD4+ epitopes. NNAlign_MA allows integration of mixed data types, handling ligands with multiple potential allele annotations, encoding of ligand context, leveraging information between data sets, and has pan-specific power allowing accurate predictions outside the set of molecules included in the training data. Applying this framework, we identified accurate binding motifs of more than 50 MHC class II molecules described by MS data, particularly expanding coverage for DP and DQ beyond that obtained using current MS motif deconvolution techniques. Furthermore, in large-scale benchmarking, the final model termed NetMHCIIpan-4.0 demonstrated improved performance beyond current state-of-the-art predictors for ligand and CD4+ T cell epitope prediction. These results suggest that NNAlign_MA and NetMHCIIpan-4.0 are powerful tools for analysis of immunopeptidome MS data, prediction of T cell epitopes, and development of personalized immunotherapies.

T Cell Epitope Predictions

Throughout the body, T cells monitor MHC-bound ligands expressed on the surface of essentially all cell types. MHC ligands that trigger a T cell immune response are referred to as T cell epitopes. Identifying such epitopes enables tracking, phenotyping, and stimulating T cells involved in immune responses in infectious disease, allergy, autoimmunity, transplantation, and cancer. The specific T cell epitopes recognized in an individual are determined by genetic factors such as the MHC molecules the individual expresses, in parallel to the individual's environmental exposure history. The complexity and importance of T cell epitope mapping have motivated the development of computational approaches that predict what T cell epitopes are likely to be recognized in a given individual or in a broader population. Such predictions guide experimental epitope mapping studies and enable computational analysis of the immunogenic potential of a given protein sequence region.

In Silico Guided Discovery of Novel Class I and II Trypanosoma cruzi Epitopes Recognized by T Cells from Chagas' Disease Patients

T cell-mediated immune response plays a crucial role in controlling Trypanosoma cruzi infection and parasite burden, but it is also involved in the clinical onset and progression of chronic Chagas' disease. Therefore, the study of T cells is central to the understanding of the immune response against the parasite and its implications for the infected organism. The complexity of the parasite-host interactions hampers the identification and characterization of T cell-activating epitopes. We approached this issue by combining in silico and in vitro methods to interrogate patients' T cells specificity. Fifty T. cruzi peptides predicted to bind a broad range of class I and II HLA molecules were selected for in vitro screening against PBMC samples from a cohort of chronic Chagas' disease patients, using IFN-γ secretion as a readout. Seven of these peptides were shown to activate this type of T cell response, and four out of these contain class I and II epitopes that, to our knowledge, are first described in this study. The remaining three contain sequences that had been previously demonstrated to induce CD8⁺ T cell response in Chagas' disease patients, or bind HLA-A*02:01, but are, in this study, demonstrated to engage CD4⁺ T cells. We also assessed the degree of differentiation of activated T cells and looked into the HLA variants that might restrict the recognition of these peptides in the context of human T. cruzi infection.

NNAlign_MA; MHC Peptidome Deconvolution for Accurate MHC Binding Motif Characterization and Improved T-cell Epitope Predictions

The set of peptides presented on a cell's surface by MHC molecules is known as the immunopeptidome. Current mass spectrometry technologies allow for identification of large peptidomes, and studies have proven these data to be a rich source of information for learning the rules of MHC-mediated antigen presentation. Immunopeptidomes are usually poly-specific, containing multiple sequence motifs matching the MHC molecules expressed in the system under investigation. Motif deconvolution -the process of associating each ligand to its presenting MHC molecule(s)- is therefore a critical and challenging step in the analysis of MS-eluted MHC ligand data. Here, we describe NNAlign_MA, a computational method designed to address this challenge and fully benefit from large, poly-specific data sets of MS-eluted ligands. NNAlign_MA simultaneously performs the tasks of (1) clustering peptides into individual specificities; (2) automatic annotation of each cluster to an MHC molecule; and (3) training of a prediction model covering all MHCs present in the training set. NNAlign_MA was benchmarked on large and diverse data sets, covering class I and class II data. In all cases, the method was demonstrated to outperform state-of-the-art methods, effectively expanding the coverage of alleles for which accurate predictions can be made, resulting in improved identification of both eluted ligands and T-cell epitopes. Given its high flexibility and ease of use, we expect NNAlign_MA to serve as an effective tool to increase our understanding of the rules of MHC antigen presentation and guide the development of novel T-cell-based therapeutics.

The Immune Epitope Database and Analysis Resource Program 2003-2018: reflections and outlook

The Immune Epitope Database and Analysis Resource (IEDB) contains information related to antibodies and T cells across an expansive scope of research fields (infectious diseases, allergy, autoimmunity, and transplantation). Capture and representation of the data to reflect growing scientific standards and techniques have required continual refinement of our rigorous curation and query and reporting processes beginning with the automated classification of over 28 million PubMed abstracts, and resulting in easily searchable data from over 20,000 published manuscripts. Data related to MHC binding and elution, nonpeptidics, natural processing, receptors, and 3D structure is first captured through manual curation and subsequently maintained through recuration to reflect evolving scientific standards. Upon promotion to the free, public database, users can query and export records of specific relevance via the online web portal which undergoes iterative development to best enable efficient data access. In parallel, the companion Analysis Resource site hosts a variety of tools that assist in the bioinformatic analyses of epitopes and related structures, which can be applied to IEDB-derived and independent datasets alike. Available tools are classified into two categories: analysis and prediction. Analysis tools include epitope clustering, sequence conservancy, and more, while prediction tools cover T and B cell epitope binding, immunogenicity, and TCR/BCR structures. In addition to these tools, benchmarking servers which allow for unbiased performance comparison are also offered. In order to expand and support the user-base of both the database and Analysis Resource, the research team actively engages in community outreach through publication of ongoing work, conference attendance and presentations, hosting of user workshops, and the provision of online help. This review provides a description of the IEDB database infrastructure, curation and recuration processes, query and reporting capabilities, the Analysis Resource, and our Community Outreach efforts, including assessment of the impact of the IEDB across the research community.