Structural in silico analysis of cross-genotype-reactivity among naturally occurring HCV NS3-1073-variants in the context of HLA-A*02:01 allele

CrossDome: an interactive R package to predict cross-reactivity risk using immunopeptidomics databases

T-cell-based immunotherapies hold tremendous potential in the fight against cancer, thanks to their capacity to specifically targeting diseased cells. Nevertheless, this potential has been tempered with safety concerns regarding the possible recognition of unknown off-targets displayed by healthy cells. In a notorious example, engineered T-cells specific to MAGEA3 (EVDPIGHLY) also recognized a TITIN-derived peptide (ESDPIVAQY) expressed by cardiac cells, inducing lethal damage in melanoma patients. Such off-target toxicity has been related to T-cell cross-reactivity induced by molecular mimicry. In this context, there is growing interest in developing the means to avoid off-target toxicity, and to provide safer immunotherapy products. To this end, we present CrossDome, a multi-omics suite to predict the off-target toxicity risk of T-cell-based immunotherapies. Our suite provides two alternative protocols, i) a peptide-centered prediction, or ii) a TCR-centered prediction. As proof-of-principle, we evaluate our approach using 16 well-known cross-reactivity cases involving cancer-associated antigens. With CrossDome, the TITIN-derived peptide was predicted at the 99+ percentile rank among 36,000 scored candidates (p-value < 0.001). In addition, off-targets for all the 16 known cases were predicted within the top ranges of relatedness score on a Monte Carlo simulation with over 5 million putative peptide pairs, allowing us to determine a cut-off p-value for off-target toxicity risk. We also implemented a penalty system based on TCR hotspots, named contact map (CM). This TCR-centered approach improved upon the peptide-centered prediction on the MAGEA3-TITIN screening (e.g., from 27th to 6th, out of 36,000 ranked peptides). Next, we used an extended dataset of experimentally-determined cross-reactive peptides to evaluate alternative CrossDome protocols. The level of enrichment of validated cases among top 50 best-scored peptides was 63% for the peptide-centered protocol, and up to 82% for the TCR-centered protocol. Finally, we performed functional characterization of top ranking candidates, by integrating expression data, HLA binding, and immunogenicity predictions. CrossDome was designed as an R package for easy integration with antigen discovery pipelines, and an interactive web interface for users without coding experience. CrossDome is under active development, and it is available at https://github.com/AntunesLab/crossdome.

PepSim: T-cell cross-reactivity prediction via comparison of peptide sequence and peptide-HLA structure

Introduction

Peptide-HLA class I (pHLA) complexes on the surface of tumor cells can be targeted by cytotoxic T-cells to eliminate tumors, and this is one of the bases for T-cell-based immunotherapies. However, there exist cases where therapeutic T-cells directed towards tumor pHLA complexes may also recognize pHLAs from healthy normal cells. The process where the same T-cell clone recognizes more than one pHLA is referred to as T-cell cross-reactivity and this process is driven mainly by features that make pHLAs similar to each other. T-cell cross-reactivity prediction is critical for designing T-cell-based cancer immunotherapies that are both effective and safe.

Methods

Here we present PepSim, a novel score to predict T-cell cross-reactivity based on the structural and biochemical similarity of pHLAs.

Results and discussion

We show our method can accurately separate cross-reactive from non-crossreactive pHLAs in a diverse set of datasets including cancer, viral, and self-peptides. PepSim can be generalized to work on any dataset of class I peptide-HLAs and is freely available as a web server at pepsim.kavrakilab.org.

Structural analysis of cancer-relevant TCR-CD3 and peptide-MHC complexes by cryoEM

The recognition of antigenic peptide-MHC (pMHC) molecules by T-cell receptors (TCR) initiates the T-cell mediated immune response. Structural characterization is key for understanding the specificity of TCR-pMHC interactions and informing the development of therapeutics. Despite the rapid rise of single particle cryoelectron microscopy (cryoEM), x-ray crystallography has remained the preferred method for structure determination of TCR-pMHC complexes. Here, we report cryoEM structures of two distinct full-length α/β TCR-CD3 complexes bound to their pMHC ligand, the cancer-testis antigen HLA-A2/MAGEA4 (230-239). We also determined cryoEM structures of pMHCs containing MAGEA4 (230-239) peptide and the closely related MAGEA8 (232-241) peptide in the absence of TCR, which provided a structural explanation for the MAGEA4 preference displayed by the TCRs. These findings provide insights into the TCR recognition of a clinically relevant cancer antigen and demonstrate the utility of cryoEM for high-resolution structural analysis of TCR-pMHC interactions.

SARS-Arena: Sequence and Structure-Guided Selection of Conserved Peptides from SARS-related Coronaviruses for Novel Vaccine Development

The pandemic caused by the SARS-CoV-2 virus, the agent responsible for the COVID-19 disease, has affected millions of people worldwide. There is constant search for new therapies to either prevent or mitigate the disease. Fortunately, we have observed the successful development of multiple vaccines. Most of them are focused on one viral envelope protein, the spike protein. However, such focused approaches may contribute for the rise of new variants, fueled by the constant selection pressure on envelope proteins, and the widespread dispersion of coronaviruses in nature. Therefore, it is important to examine other proteins, preferentially those that are less susceptible to selection pressure, such as the nucleocapsid (N) protein. Even though the N protein is less accessible to humoral response, peptides from its conserved regions can be presented by class I Human Leukocyte Antigen (HLA) molecules, eliciting an immune response mediated by T-cells. Given the increased number of protein sequences deposited in biological databases daily and the N protein conservation among viral strains, computational methods can be leveraged to discover potential new targets for SARS-CoV-2 and SARS-CoV-related viruses. Here we developed SARS-Arena, a user-friendly computational pipeline that can be used by practitioners of different levels of expertise for novel vaccine development. SARS-Arena combines sequence-based methods and structure-based analyses to (i) perform multiple sequence alignment (MSA) of SARS-CoV-related N protein sequences, (ii) recover candidate peptides of different lengths from conserved protein regions, and (iii) model the 3D structure of the conserved peptides in the context of different HLAs. We present two main Jupyter Notebook workflows that can help in the identification of new T-cell targets against SARS-CoV viruses. In fact, in a cross-reactive case study, our workflows identified a conserved N protein peptide (SPRWYFYYL) recognized by CD8⁺ T-cells in the context of HLA-B7⁺. SARS-Arena is available at https://github.com/KavrakiLab/SARS-Arena.

Charge-based interactions through peptide position 4 drive diversity of antigen presentation by human leukocyte antigen class I molecules

Human leukocyte antigen class I (HLA-I) molecules bind and present peptides at the cell surface to facilitate the induction of appropriate CD8+ T cell-mediated immune responses to pathogen- and self-derived proteins. The HLA-I peptide-binding cleft contains dominant anchor sites in the B and F pockets that interact primarily with amino acids at peptide position 2 and the C-terminus, respectively. Nonpocket peptide-HLA interactions also contribute to peptide binding and stability, but these secondary interactions are thought to be unique to individual HLA allotypes or to specific peptide antigens. Here, we show that two positively charged residues located near the top of peptide-binding cleft facilitate interactions with negatively charged residues at position 4 of presented peptides, which occur at elevated frequencies across most HLA-I allotypes. Loss of these interactions was shown to impair HLA-I/peptide binding and complex stability, as demonstrated by both in vitro and in silico experiments. Furthermore, mutation of these Arginine-65 (R65) and/or Lysine-66 (K66) residues in HLA-A*02:01 and A*24:02 significantly reduced HLA-I cell surface expression while also reducing the diversity of the presented peptide repertoire by up to 5-fold. The impact of the R65 mutation demonstrates that nonpocket HLA-I/peptide interactions can constitute anchor motifs that exert an unexpectedly broad influence on HLA-I-mediated antigen presentation. These findings provide fundamental insights into peptide antigen binding that could broadly inform epitope discovery in the context of viral vaccine development and cancer immunotherapy.

Virus-Induced T Cell-Mediated Heterologous Immunity and Vaccine Development

Heterologous immunity (H.I.) is a consequence of an encounter with a specific antigen, which can alter the subsequent immune response to a different antigen. This can happen at the innate immune system level—often called trained immunity or innate immune memory—and/or at the adaptive immune system level involving T memory cells and antibodies. Viruses may also induce T cell-mediated H.I., which can confer protection or drive immunopathology against other virus subtypes, related or unrelated viruses, other pathogens, auto- or allo-antigens. It is important to understand the underlying mechanisms for the development of antiviral “universal” vaccines and broader T cell responses rather than just subtype-specific antibody responses as in the case of influenza. Furthermore, knowledge about determinants of vaccine-mediated H.I. may inform public health policies and provide suggestions for repurposing existing vaccines. Here, we introduce H.I. and provide an overview of evidence on virus- and antiviral vaccine-induced T cell-mediated cross-reactive responses. We also discuss the factors influencing final clinical outcome of virus-mediated H.I. as well as non-specific beneficial effects of live attenuated antiviral vaccines such as measles and vaccinia. Available epidemiological and mechanistic data have implications both for the development of new vaccines and for personalized vaccinology, which are presented. Finally, we formulate future research priorities and opportunities.

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.

A new in silico approach to investigate molecular aspects of factor IX missense causative mutations and their impact on the hemophilia B severity

Factor IX (encoded by F9) is a protein in the coagulation process, where its lack or deficiency leads to hemophilia B. This condition has been much less studied than hemophilia A, especially in Latin America. We analyzed the structural and functional impact of 54 missense mutations (18 reported by us previously, and 36 other mutations from the Factor IX database) through molecular modeling approaches. To accomplish this task, we examine the electrostatic patterns, hydrophobicity/hydrophilicity, disulfide, and H-bond differences of the Factor IX structures harboring the missense mutations found, correlating them with their clinical effects. The 54 mutated sequences were modeled and their physicochemical features were determined and used as input in clusterization tools. The electrostatic pattern seems to influence in disease severity, especially for mutations investigated in epidermal growth factors 1 and 2 (EGF1/2) domains. The combined use of all physicochemical information improved the clustering of structures associated to similar phenotypes, especially for mutations from GLA and EGF1-2 domains. The effect of mutations in the disease phenotype severity seems to be a complex interplay of molecular features, each one contributing to different impacts. This highlights that previous studies and tools analyzing individually single features for single mutations are missing elements that fulfill the whole picture.

APE-Gen: A Fast Method for Generating Ensembles of Bound Peptide-MHC Conformations

The Class I Major Histocompatibility Complex (MHC) is a central protein in immunology as it binds to intracellular peptides and displays them at the cell surface for recognition by T-cells. The structural analysis of bound peptide-MHC complexes (pMHCs) holds the promise of interpretable and general binding prediction (i.e., testing whether a given peptide binds to a given MHC). However, structural analysis is limited in part by the difficulty in modelling pMHCs given the size and flexibility of the peptides that can be presented by MHCs. This article describes APE-Gen (Anchored Peptide-MHC Ensemble Generator), a fast method for generating ensembles of bound pMHC conformations. APE-Gen generates an ensemble of bound conformations by iterated rounds of (i) anchoring the ends of a given peptide near known pockets in the binding site of the MHC, (ii) sampling peptide backbone conformations with loop modelling, and then (iii) performing energy minimization to fix steric clashes, accumulating conformations at each round. APE-Gen takes only minutes on a standard desktop to generate tens of bound conformations, and we show the ability of APE-Gen to sample conformations found in X-ray crystallography even when only sequence information is used as input. APE-Gen has the potential to be useful for its scalability (i.e., modelling thousands of pMHCs or even non-canonical longer peptides) and for its use as a flexible search tool. We demonstrate an example for studying cross-reactivity.

General Prediction of Peptide-MHC Binding Modes Using Incremental Docking: A Proof of Concept

The class I major histocompatibility complex (MHC) is capable of binding peptides derived from intracellular proteins and displaying them at the cell surface. The recognition of these peptide-MHC (pMHC) complexes by T-cells is the cornerstone of cellular immunity, enabling the elimination of infected or tumoral cells. T-cell-based immunotherapies against cancer, which leverage this mechanism, can greatly benefit from structural analyses of pMHC complexes. Several attempts have been made to use molecular docking for such analyses, but pMHC structure remains too challenging for even state-of-the-art docking tools. To overcome these limitations, we describe the use of an incremental meta-docking approach for structural prediction of pMHC complexes. Previous methods applied in this context used specific constraints to reduce the complexity of this prediction problem, at the expense of generality. Our strategy makes no assumption and can potentially be used to predict binding modes for any pMHC complex. Our method has been tested in a re-docking experiment, reproducing the binding modes of 25 pMHC complexes whose crystal structures are available. This study is a proof of concept that incremental docking strategies can lead to general geometry prediction of pMHC complexes, with potential applications for immunotherapy against cancer or infectious diseases.

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.

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.

Increased Immune Response Variability during Simultaneous Viral Coinfection Leads to Unpredictability in CD8 T Cell Immunity and Pathogenesis

T cell memory is usually studied in the context of infection with a single pathogen in naive mice, but how memory develops during a coinfection with two pathogens, as frequently occurs in nature or after vaccination, is far less studied. Here, we questioned how the competition between immune responses to two viruses in the same naive host would influence the development of CD8 T cell memory and subsequent disease outcome upon challenge. Using two different models of coinfection, including the well-studied lymphocytic choriomeningitis (LCMV) and Pichinde (PICV) viruses, several differences were observed within the CD8 T cell responses to either virus. Compared to single-virus infection, coinfection resulted in substantial variation among mice in the size of epitope-specific T cell responses to each virus. Some mice had an overall reduced number of virus-specific cells to either one of the viruses, and other mice developed an immunodominant response to a normally subdominant, cross-reactive epitope (nucleoprotein residues 205 to 212, or NP205). These changes led to decreased protective immunity and enhanced pathology in some mice upon challenge with either of the original coinfecting viruses. In mice with PICV-dominant responses, during a high-dose challenge with LCMV clone 13, increased immunopathology was associated with a reduced number of LCMV-specific effector memory CD8 T cells. In mice with dominant cross-reactive memory responses, during challenge with PICV increased immunopathology was directly associated with these cross-reactive NP205-specific CD8 memory cells. In conclusion, the inherent competition between two simultaneous immune responses results in significant alterations in T cell immunity and subsequent disease outcome upon reexposure.

IMPORTANCE

Combination vaccines and simultaneous administration of vaccines are necessary to accommodate required immunizations and maintain vaccination rates. Antibody responses generally correlate with protection and vaccine efficacy. However, live attenuated vaccines also induce strong CD8 T cell responses, and the impact of these cells on subsequent immunity, whether beneficial or detrimental, has seldom been studied, in part due to the lack of known T cell epitopes to vaccine viruses. We questioned if the inherent increased competition and stochasticity between two immune responses during a simultaneous coinfection would significantly alter CD8 T cell memory in a mouse model where CD8 T cell epitopes are clearly defined. We show that some of the coinfected mice have sufficiently altered memory T cell responses that they have decreased protection and enhanced immunopathology when reexposed to one of the two viruses. These data suggest that a better understanding of human T cell responses to vaccines is needed to optimize immunization strategies.

Vaccination and heterologous immunity: educating the immune system

This review discusses three inter-related topics: (1) the immaturity of the neonatal and infant immune response; (2) heterologous immunity, where prior infection history with unrelated pathogens alters disease outcome resulting in either enhanced protective immunity or increased immunopathology to new infections, and (3) epidemiological human vaccine studies that demonstrate vaccines can have beneficial or detrimental effects on subsequent unrelated infections. The results from the epidemiological and heterologous immunity studies suggest that the immune system has tremendous plasticity and that each new infection or vaccine that an individual is exposed to during a lifetime will potentially alter the dynamics of their immune system. It also suggests that each new infection or vaccine that an infant receives is not only perturbing the immune system but is educating the immune system and laying down the foundation for all subsequent responses. This leads to the question, is there an optimum way to educate the immune system? Should this be taken into consideration in our vaccination protocols?

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.

Frequency, Private Specificity, and Cross-Reactivity of Preexisting Hepatitis C Virus (HCV)-Specific CD8+ T Cells in HCV-Seronegative Individuals: Implications for Vaccine Responses

T cell responses play a critical role in controlling or clearing viruses. Therefore, strategies to prevent or treat infections include boosting T cell responses. T cells specific for various pathogens have been reported in unexposed individuals and an influence of such cells on the response toward vaccines is conceivable. However, little is known about their frequency, repertoire, and impact on vaccination. We performed a detailed characterization of CD8(+) T cells specific to a hepatitis C virus (HCV) epitope (NS3-1073) in 121 HCV-seronegative individuals. We show that in vitro HCV NS3-1073-specific CD8(+) T cell responses were rather abundantly detectable in one-third of HCV-seronegative individuals irrespective of risk factors for HCV exposure. Ex vivo, these NS3-1073-specific CD8(+) T cells were found to be both naive and memory cells. Importantly, recognition of various peptides derived from unrelated viruses by NS3-1073-specific CD8(+) T cells showed a considerable degree of T cell cross-reactivity, suggesting that they might in part originate from previous heterologous infections. Finally, we further provide evidence that preexisting NS3-1073-specific CD8(+) T cells can impact the T cell response toward peptide vaccination. Healthy, vaccinated individuals who showed an in vitro response toward NS3-1073 already before vaccination displayed a more vigorous and earlier response toward the vaccine.

IMPORTANCE

Preventive and therapeutic vaccines are being developed for many viral infections and often aim on inducing T cell responses. Despite effective antiviral drugs against HCV, there is still a need for a preventive vaccine. However, the responses to vaccines can be highly variable among different individuals. Preexisting T cells in unexposed individuals could be one reason that helps to explain the variable T cell responses to vaccines. Based on our findings, we suggest that HCV CD8(+) T cells are abundant in HCV-seronegative individuals but that their repertoire is highly diverse due to the involvement of both naive precursors and cross-reactive memory cells of different specificities, which can influence the response to vaccines. The data may emphasize the need to personalize immune-based therapies based on the individual's T cell repertoire that is present before the immune intervention.

Databases for T-cell epitopes

Modem immunology and vaccinology incorporate immunoinformatics techniques to give insights into immune systems and accelerate vaccine design. Databases managing epitope data in a structured form with immune-related annotations including sequences, alleles, source organisms, structures, and diseases could be the most crucial part of immunoinformatics offering data sources for the analysis of immune systems and development of prediction methods. This chapter provides an overview of publicly available databases of T-cell epitopes including general databases, pathogen- and tumor-specific databases, and 3D structure databases.

CrossTope: a curate repository of 3D structures of immunogenic peptide: MHC complexes

The CrossTope is a highly curate repository of three-dimensional structures of peptide:major histocompatibility complex (MHC) class I complexes (pMHC-I). The complexes hosted by this databank were obtained in protein databases and by large-scale in silico construction of pMHC-I structures, using a new approach developed by our group. At this moment, the database contains 182 ‘non-redundant’ pMHC-I complexes from two human and two murine alleles. A web server provides interface for database query. The user can download (i) structure coordinate files and (ii) topological and charges distribution maps images from the T-cell receptor-interacting surface of pMHC-I complexes. The retrieved structures and maps can be used to cluster similar epitopes in cross-reactivity approaches, to analyse viral escape mutations in a structural level or even to improve the immunogenicity of tumour antigens.

Database URL: http://www.crosstope.com.br