Next-generation IEDB tools: a platform for epitope prediction and analysis.

The Next-Generation (NG) IEDB Tools website (https://nextgen-tools.iedb.org) provides users with a redesigned interface to many of the algorithms for epitope prediction and analysis that were originally released on the legacy IEDB Tools website. The initial release focuses on consolidation of all tools related to HLA class I epitopes (MHC binding, elution, immunogenicity, and processing), making all of these predictions accessible from a single application and allowing for their simultaneous execution with minimal user inputs. Additionally, the PEPMatch tool for identifying highly similar epitopes in a set of curated proteomes, as well as a tool for epitope clustering, are available on the site. The NG Tools site allows users to build data pipelines by sending the output of one tool as input for the next. Over the next several years, all pre-existing IEDB Tools, and any newly developed tools, will be integrated into this new site. Here we describe the philosophy behind the redesign and demonstrate the utility and productivity enhancements that are enabled by the new interface.

IEDB-3D 2.0: Structural data analysis within the Immune Epitope Database.

The Immune Epitope Database (IEDB) catalogs T cell, B cell, and major histocompatibility complex ligand information in the context of infectious disease, allergy, autoimmunity, and transplantation. An important component of this information is three-dimensional structural data on T cell receptors, antibodies, and pairwise residue interactions between immune receptors and antigens, which we refer to as IEDB-3D. Such data is highly valuable for mechanically understanding receptor:ligand interactions. Here, we present IEDB-3D 2.0, which comprises a complete overhaul of how we obtain and present 3D structural data. A new 3D viewer experience that utilizes iCn3D has been implemented to replace outdated java-based technology. In addition, we have designed a new epitope mapping system that matches each epitope available in the IEDB with its antigen structural data. Finally, immunogenicity data retrieved from the IEDB's ImmunomeBrowser can now be used to highlight immunogenic regions of an antigen directly in iCn3D. Overall, the IEDB-3D 2.0 provides an updated tool platform to visualize epitope data cataloged in the IEDB.

Coronavirus Immunotherapeutic Consortium Database.

The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has seen multiple anti-SARS-CoV-2 antibodies being generated globally. It is difficult, however, to assemble a useful compendium of these biological properties if they are derived from experimental measurements performed at different sites under different experimental conditions. The Coronavirus Immunotherapeutic Consortium (COVIC) circumvents these issues by experimentally testing blinded antibodies side by side for several functional activities. To collect these data in a consistent fashion and make it publicly available, we established the COVIC database (COVIC-DB, https://covicdb.lji.org/). This database enables systematic analysis and interpretation of this large-scale dataset by providing a comprehensive view of various features such as affinity, neutralization, in vivo protection and effector functions for each antibody. Interactive graphs enable direct comparisons of antibodies based on select functional properties. We demonstrate how the COVIC-DB can be utilized to examine relationships among antibody features, thereby guiding the design of therapeutic antibody cocktails. Database URL  https://covicdb.lji.org/.

MatchTope: A tool to predict the cross reactivity of peptides complexed with Major Histocompatibility Complex I.

The therapeutic targeting of the immune system, for example in vaccinology and cancer treatment, is a challenging task and the subject of active research. Several in silico tools used for predicting immunogenicity are based on the analysis of peptide sequences binding to the Major Histocompatibility Complex (pMHC). However, few of these bioinformatics tools take into account the pMHC three-dimensional structure. Here, we describe a new bioinformatics tool, MatchTope, developed for predicting peptide similarity, which can trigger cross-reactivity events, by computing and analyzing the electrostatic potentials of pMHC complexes. We validated MatchTope by using previously published data from in vitro assays. We thereby demonstrate the strength of MatchTope for similarity prediction between targets derived from several pathogens as well as for indicating possible cross responses between self and tumor peptides. Our results suggest that MatchTope can enhance and speed up future studies in the fields of vaccinology and cancer immunotherapy.

Differential resistance to oxidants and production of hydrolytic enzymes in Candida albicans.

Resistance to the toxic effects of reactive oxygen species produced by phagocytes and production of hydrolytic enzymes are important aspects of Candida albicans virulence. In this report, we compared twelve C. albicans isolates for their in vitro capacity to resist oxidants-hydrogen peroxide, menadione and paraquat; and to produce hydrolytic enzymes-phospholipase and protease. Different C. albicans isolates showed different degrees of resistance to oxidants as well as differences in production of hydrolytic enzymes. Resistance to oxidative stress did not correlate with production of hydrolytic enzymes. This reinforces the view that C. albicans differentially regulates the expression of virulence factors in response to local environmental conditions.

High infiltration of B cells in tertiary lymphoid structures, TCR oligoclonality, and neoantigens are part of esophageal squamous cell carcinoma microenvironment.

Esophageal squamous cell carcinoma (ESCA) exhibits high intratumoral molecular heterogeneity posing a challenge to cancer therapy. Immune checkpoint blockade therapy has been approved for this disease, but with modest results. RNA-Seq data from paired tumor and surrounding nonmalignant tissue from 14 patients diagnosed with ESCA without previous treatment and from The Cancer Genome Atlas-ESCA cohort were analyzed. Herein, we investigated ESCA immune landscape including mutation-derived neoantigens and immune cell subpopulations. Tumor-associated antigen expression was determined by in silico analyses and confirmed by immunohistochemistry showing that PRAME, CEACAM4, and MAGEA11 proteins are expressed on tumors. Immune checkpoint molecules gene expression was higher in the tumor compared with surrounding nonmalignant tissue, but its expression varies greatly among patients. TCR repertoire and BCR transcripts analysis evidenced low clonal diversity with one TCR clone predicted to be specific for a MAGEA11-derived peptide. A high number of B-cell clones infiltrating the tumors and the abundance of these cells in tertiary lymphoid structures observed in ESCA tumors support B cells as a potential immune modulator in this tumor.

pMHC Structural Comparisons as a Pivotal Element to Detect and Validate T-Cell Targets for Vaccine Development and Immunotherapy-A New Methodological Proposal.

The search for epitopes that will effectively trigger an immune response remains the "El Dorado" for immunologists. The development of promising immunotherapeutic approaches requires the appropriate targets to elicit a proper immune response. Considering the high degree of HLA/TCR diversity, as well as the heterogeneity of viral and tumor proteins, this number will invariably be higher than ideal to test. It is known that the recognition of a peptide-MHC (pMHC) by the T-cell receptor is performed entirely in a structural fashion, where the atomic interactions of both structures, pMHC and TCR, dictate the fate of the process. However, epitopes with a similar composition of amino acids can produce dissimilar surfaces. Conversely, sequences with no conspicuous similarities can exhibit similar TCR interaction surfaces. In the last decade, our group developed a database and in silico structural methods to extract molecular fingerprints that trigger T-cell immune responses, mainly referring to physicochemical similarities, which could explain the immunogenic differences presented by different pMHC-I complexes. Here, we propose an immunoinformatic approach that considers a structural level of information, combined with an experimental technology that simulates the presentation of epitopes for a T cell, to improve vaccine production and immunotherapy efficacy.

Interpreting T-Cell Cross-reactivity through Structure: Implications for TCR-Based Cancer Immunotherapy.

Immunotherapy has become one of the most promising avenues for cancer treatment, making use of the patient's own immune system to eliminate cancer cells. Clinical trials with T-cell-based immunotherapies have shown dramatic tumor regressions, being effective in multiple cancer types and for many different patients. Unfortunately, this progress was tempered by reports of serious (even fatal) side effects. Such therapies rely on the use of cytotoxic T-cell lymphocytes, an essential part of the adaptive immune system. Cytotoxic T-cells are regularly involved in surveillance and are capable of both eliminating diseased cells and generating protective immunological memory. The specificity of a given T-cell is determined through the structural interaction between the T-cell receptor (TCR) and a peptide-loaded major histocompatibility complex (MHC); i.e., an intracellular peptide-ligand displayed at the cell surface by an MHC molecule. However, a given TCR can recognize different peptide-MHC (pMHC) complexes, which can sometimes trigger an unwanted response that is referred to as T-cell cross-reactivity. This has become a major safety issue in TCR-based immunotherapies, following reports of melanoma-specific T-cells causing cytotoxic damage to healthy tissues (e.g., heart and nervous system). T-cell cross-reactivity has been extensively studied in the context of viral immunology and tissue transplantation. Growing evidence suggests that it is largely driven by structural similarities of seemingly unrelated pMHC complexes. Here, we review recent reports about the existence of pMHC "hot-spots" for cross-reactivity and propose the existence of a TCR interaction profile (i.e., a refinement of a more general TCR footprint in which some amino acid residues are more important than others in triggering T-cell cross-reactivity). We also make use of available structural data and pMHC models to interpret previously reported cross-reactivity patterns among virus-derived peptides. Our study provides further evidence that structural analyses of pMHC complexes can be used to assess the intrinsic likelihood of cross-reactivity among peptide-targets. Furthermore, we hypothesize that some apparent inconsistencies in reported cross-reactivities, such as a preferential directionality, might also be driven by particular structural features of the targeted pMHC complex. Finally, we explain why TCR-based immunotherapy provides a special context in which meaningful T-cell cross-reactivity predictions can be made.

Improved structural method for T-cell cross-reactivity prediction.

Cytotoxic T-lymphocytes (CTLs) are the key players of adaptive cellular immunity, being able to identify and eliminate infected cells through the interaction with peptide-loaded major histocompatibility complexes class I (pMHC-I). Despite the high specificity of this interaction, a given lymphocyte is actually able to recognize more than just one pMHC-I complex, a phenomenon referred as cross-reactivity. In the present work we describe the use of pMHC-I structural features as input for multivariate statistical methods, to perform standardized structure-based predictions of cross-reactivity among viral epitopes. Our improved approach was able to successfully identify cross-reactive targets among 28 naturally occurring hepatitis C virus (HCV) variants and among eight epitopes from the four dengue virus serotypes. In both cases, our results were supported by multiscale bootstrap resampling and by data from previously published in vitro experiments. The combined use of data from charges and accessible surface area (ASA) of selected residues over the pMHC-I surface provided a powerful way of assessing the structural features involved in triggering cross-reactive responses. Moreover, the use of an R package (pvclust) for assessing the uncertainty in the hierarchical cluster analysis provided a statistical support for the interpretation of results. Taken together, these methods can be applied to vaccine design, both for the selection of candidates capable of inducing immunity against different targets, or to identify epitopes that could trigger undesired immunological responses.

DockTope: a Web-based tool for automated pMHC-I modelling.

The immune system is constantly challenged, being required to protect the organism against a wide variety of infectious pathogens and, at the same time, to avoid autoimmune disorders. One of the most important molecules involved in these events is the Major Histocompatibility Complex class I (MHC-I), responsible for binding and presenting small peptides from the intracellular environment to CD8(+) T cells. The study of peptide:MHC-I (pMHC-I) molecules at a structural level is crucial to understand the molecular mechanisms underlying immunologic responses. Unfortunately, there are few pMHC-I structures in the Protein Data Bank (PDB) (especially considering the total number of complexes that could be formed combining different peptides), and pMHC-I modelling tools are scarce. Here, we present DockTope, a free and reliable web-based tool for pMHC-I modelling, based on crystal structures from the PDB. DockTope is fully automated and allows any researcher to construct a pMHC-I complex in an efficient way. We have reproduced a dataset of 135 non-redundant pMHC-I structures from the PDB (Cα RMSD below 1 Å). Modelling of pMHC-I complexes is remarkably important, contributing to the knowledge of important events such as cross-reactivity, autoimmunity, cancer therapy, transplantation and rational vaccine design.

Lessons from molecular modeling human α-L-iduronidase.

Human α-L-iduronidase (IDUA) is a member of glycoside hydrolase family and is involved in the catabolism of glycosaminoglycans (GAGs), heparan sulfate (HS) and dermatan sulfate (DS). Mutations in this enzyme are responsible for mucopolysaccharidosis I (MPS I), an inherited lysosomal storage disorder. Despite great interest in determining and studying this enzyme structure, the lack of a high identity to templates and other technical issues have challenged both bioinformaticians and crystallographers, until the recent publication of an IDUA crystal structure (PDB: 4JXP). In the present work, four alternative IDUA models, generated and evaluated prior to crystallographic determination, were compared to the 4JXP structure. A combined analysis using several viability assessment tools and molecular dynamics simulations highlights the strengths and limitations of different comparative modeling protocols, all of which are based on the same low identity template (only 22%). Incorrect alignment between the target and template was confirmed to be a major bottleneck in homology modeling, regardless of the modeling software used. Moreover, secondary structure analysis during a 50ns simulation seems to be useful for indicating alignment errors and structural instabilities. The best model was achieved through the combined use of Phyre 2 and Modeller, suggesting the use of this protocol for the modeling of other proteins that still lack high identity templates.

Oxidative stress in the brain of reproductive male rats during aging.

Reproduction alters the male physiology. We performed a comprehensive study to examine oxidative stress in the brains of male rats with (experienced) or without (naïve) reproductive activity during aging. Oxidative stress was assessed by measuring the activity of catalase, glutathione peroxidase, superoxide dismutase, glutathione S-transferase, aconitase, and aconitase reactivated, and by measuring lipid peroxidation, protein carbonylation, nitrite and nitrate levels, vitamin C levels, and glutathione (total, reduced, oxidized forms) levels in brain tissue, as well as testosterone and estradiol levels in serum. Reproductively active animals exhibited increased testosterone levels and aconitase activity, suggesting an increased metabolism. Increased antioxidant enzyme activities and increased levels of antioxidant compounds were observed, yet damage to biomolecules was also observed in experienced rats. During aging changes in oxidative stress were observed. We found higher activities of antioxidant enzymes, higher amounts of antioxidants, and more damage at six months of age among experienced animals than among naïve animals. Similar antioxidant activities and levels, and damage were found between the groups at twenty-four months of age. These results add comprehensive data regarding changes in oxidative stress during aging, and suggest an explanation for the costs of reproduction.