Anne S. De Groot

Two novel T cell epitope prediction algorithms based on MHC-binding motifs; comparison of predicted and published epitopes from Mycobacterium tuberculosis and HIV protein sequences.

We have designed two computer-based algorithms for T cell epitope prediction, OptiMer and EpiMer, which incorporate current knowledge of MHC-binding motifs. OptiMer locates amphipathic segments of protein antigens with a high density of MHC-binding motifs. EpiMer identifies peptides with a high density of MHC-binding motifs alone. These algorithms exploit the striking tendency for MHC-binding motifs to cluster within short segments of each protein. Putative epitopes predicted by these algorithms contain motifs corresponding to many different MHC alleles, and may contain both class I and class II motifs, features thought to be ideal for the peptide components of synthetic subunit vaccines. In this study, we describe the use of OptiMer and EpiMer for the prediction of putative T cell epitopes from Mycobacterium tuberculosis and human immunodeficiency virus protein antigens, and demonstrate that these two algorithms may provide sensitive and efficient means for the prediction of promiscuous T cell epitopes that may be critical to the development of vaccines against these and other pathogens.

Reducing risk, improving outcomes: Bioengineering less immunogenic protein therapeutics

One of the great surprises of the biologics revolution has been the discovery that recombinant human proteins, including monoclonals of human origin, can cause immune responses when administered to immune-competent subjects. Preclinical and clinical evaluations of the potential immunogenicity of biologics have been primarily focused on humoral immune responses and as a result, the critical contribution of T cells to the development of anti-monoclonal antibodies (also known as anti-drug antibodies or ADA) has been somewhat overlooked. Recent publications have confirmed the role of effector T cells and begun to explore the role of regulatory T cells in the development of anti-drug antibodies. This review will focus on the role of T-cell-dependent (Td) immunogenicity assessment in the preclinical and clinical phases of drug development and summarize new data on regulatory T-cell epitopes contained in the Fc and CH1 domains of IgG. Recommendations for Td immunogenicity screening and assessment provided in this article may contribute to the development of safer protein-based drugs for human use.

Immunomics: discovering new targets for vaccines and therapeutics

T-cell-epitope mapping has emerged as one of the most powerful new drug discovery tools for a range of biomedical applications. Initially, T-cell-epitope discovery was applied to the development of vaccines for infectious diseases and cancer. T-cell-epitope-mapping applications have now expanded to include reengineering of protein therapeutics (a process now called deimmunization), as well as the fields of autoimmunity, endocrinology, allergy, transplantation and diagnostics. Research employing T-cell-epitope mapping falls within the realm of immunomics, a new field that addresses the interface between host and (pathogen) proteome, bridging informatics, genomics, proteomics, immunology and clinical medicine. This review highlights aspects of recent immunomics research that are related to the discovery of the T-cell immunome.

Immuno-informatics: Mining genomes for vaccine components.

The complete genome sequences of more than 60 microbes have been completed in the past decade. Concurrently, a series of new informatic stools, designed to harness this new wealth of information, have been developed. Some of these new tools allow researchers to select regions of microbial genomes that trigger immune responses. These regions, termed epitopes, are ideal components of vaccines. When the new tools are used to search for epitopes, this search is usually coupled with in vitro screening methods; an approach that has been termed computational immunology or immuno‐informatics. Researchers are now implementing these combined methods to scangenomic sequences for vaccine components. They are thereby expanding the number of different proteins that can be screened for vaccine development, while narrowing this search to those regions of the proteins that are extremely likely to induce an immune response. As the tools improve, it may soon be feasible to skip over many of the in vitro screening steps, moving directly from genome sequence to vaccine design. The present article reviews the work of several groups engaged in the development of immuno‐informatic stools and illustrates the application of these tools to the process of vaccine discovery.

T-cell dependent immunogenicity of protein therapeutics: Preclinical assessment and mitigation

Protein therapeutics hold a prominent and rapidly expanding place among medicinal products. Purified blood products, recombinant cytokines, growth factors, enzyme replacement factors, monoclonal antibodies, fusion proteins, and chimeric fusion proteins are all examples of therapeutic proteins that have been developed in the past few decades and approved for use in the treatment of human disease. Despite early belief that the fully human nature of these proteins would represent a significant advantage, adverse effects associated with immune responses to some biologic therapies have become a topic of some concern. As a result, drug developers are devising strategies to assess immune responses to protein therapeutics during both the preclinical and the clinical phases of development. While there are many factors that contribute to protein immunogenicity, T cell- (thymus-) dependent (Td) responses appear to play a critical role in the development of antibody responses to biologic therapeutics. A range of methodologies to predict and measure Td immune responses to protein drugs has been developed. This review will focus on the Td contribution to immunogenicity, summarizing current approaches for the prediction and measurement of T cell-dependent immune responses to protein biologics, discussing the advantages and limitations of these technologies, and suggesting a practical approach for assessing and mitigating Td immunogenicity.

Immunoinformatic comparison of T-cell epitopes contained in novel swine-origin influenza A (H1N1) virus with epitopes in 2008–2009 conventional influenza vaccine

In March 2009 a novel swine-origin influenza A (H1N1) virus (S-OIV) emerged in Mexico and the Western United States. Vaccination with conventional influenza vaccine (CIV) does not result in cross-reactive antibodies, however, the disproportionate number of cases (37%) occurring among persons younger than 50 years old suggested that adaptive immune memory might be responsible for the relative lack of virulence in older, healthy adults. Using EpiMatrix, a T-cell epitope prediction and comparison tool, we compared the sequences of the three hemagglutinin (HA) and neuraminidase (NA) proteins contained in 2008-2009 CIV to their counterparts in A/California/04/2009 (H1N1) looking for cross-conserved T-cell epitope sequences. We found greater than 50% conservation of T helper and CTL epitopes between novel S-OIV and CIV HA for selected HLA. Conservation was lower among NA epitopes. Sixteen promiscuous helper T-cell epitopes are contained in the S-OIV H1N1 HA sequence, of which nine (56%) were 100% conserved in the 2008-2009 influenza vaccine strain; 81% were either identical or had one conservative amino acid substitution. Fifty percent of predicted CTL epitopes found in S-OIV H1N1 HA were also found in CIV HA sequences. Based on historical performance, we expect these epitope predictions to be 93-99% accurate. This in silico analysis supports the proposition that T-cell response to cross-reactive T-cell epitopes, due to vaccination or exposure, may have the capacity to attenuate the course of S-OIV H1N1 induced disease-in the absence of cross-reactive antibody response. The value of the CIV or live-attenuated influenza vaccine containing the 2008-2009 vaccine strains, as defense against H1N1, could be further tested by evaluating human immune responses to the conserved T-cell epitopes using PBMC from individuals infected with H1N1 and from CIV vaccinees.

Prediction of well-conserved HIV-1 ligands using a matrix-based algorithm, EpiMatrix

This preliminary study was undertaken to identify new human leucocyte antigens (HLA) ligands from human immunodeficiency virus type 1 (HIV-1) which are highly conserved across HIV-1 clades and which may serve to induce cross-reactive cytotoxic T lymphocytes (CTLs). EpiMatrix was used to predict putative ligands from HIV-1 for HLA-A2 and HLA-B27. Twenty-six peptides that were both likely to bind and also highly conserved across HIV-1 strains in the Los Alamos HIV sequence database were selected for binding assays using the T2 stabilization assay. Two peptides that were also highly likely to bind (for A2 and B27, as determined by EpiMatrix) and well conserved across HIV-1 strains, and had previously been described to bind in the published literature, were also selected to serve as positive controls for the assays. Ten new major histocompatibility complex (MHC) ligands were identified among the 26 study peptides. The control peptides bound, as expected. These data confirm that EpiMatrix can be used to screen HIV-1 protein sequences for highly conserved regions that are likely to bind to MHC and may prove to be highly conserved HIV-1 CTL epitopes.

Emerging Vaccine Informatics

Vaccine informatics is an emerging research area that focuses on development and applications of bioinformatics methods that can be used to facilitate every aspect of the preclinical, clinical, and postlicensure vaccine enterprises. Many immunoinformatics algorithms and resources have been developed to predict T- and B-cell immune epitopes for epitope vaccine development and protective immunity analysis. Vaccine protein candidates are predictable in silico from genome sequences using reverse vaccinology. Systematic transcriptomics and proteomics gene expression analyses facilitate rational vaccine design and identification of gene responses that are correlates of protection in vivo. Mathematical simulations have been used to model host-pathogen interactions and improve vaccine production and vaccination protocols. Computational methods have also been used for development of immunization registries or immunization information systems, assessment of vaccine safety and efficacy, and immunization modeling. Computational literature mining and databases effectively process, mine, and store large amounts of vaccine literature and data. Vaccine Ontology (VO) has been initiated to integrate various vaccine data and support automated reasoning.

Prediction of immunogenicity: in silico paradigms, ex vivo and in vivo correlates

Immunogenicity can be a major obstacle to successful protein drug therapy. Antidrug antibodies may neutralize therapeutic function, influence pharmacokinetics and, in some cases, lead to severe adverse effects. These effects depend on factors including dose, regimen, delivery route and contaminants, among others. Importantly, immunogenicity is a consideration that is better addressed during preclinical development before complications arise in clinical trials or following licensure. This article will address the development and application of computational tools for immunogenicity assessment of protein therapeutics, and validation of those predictions using peripheral blood from exposed subjects or alternative in vivo methods.

Low immunogenicity predicted for emerging avian-origin H7N9: implication for influenza vaccine design.

A new avian-origin influenza virus emerged near Shanghai in February 2013, and by the beginning of May it had caused over 130 human infections and 36 deaths. Human-to-human transmission of avian-origin H7N9 influenza A has been limited to a few family clusters, but the high mortality rate (27%) associated with human infection has raised concern about the potential for this virus to become a significant human pathogen. European, American, and Asian vaccine companies have already initiated the process of cloning H7 antigens such as hemagglutinin (HA) into standardized vaccine production vehicles. Unfortunately, previous H7 HA-containing vaccines have been poorly immunogenic. We used well-established immunoinformatics tools to analyze the H7N9 protein sequences and compare their T cell epitope content to other circulating influenza A strains as a means of estimating the immunogenic potential of the new influenza antigen. We found that the HA proteins derived from closely related human-derived H7N9 strains contain fewer T cell epitopes than other recently circulating strains of influenza, and that conservation of T cell epitopes with other strains of influenza was very limited. Here, we provide a detailed accounting of the type and location of T cell epitopes contained in H7N9 and their conservation in other H7 and circulating (A/California/07/2009, A/Victoria/361/2011, and A/Texas/50/2012) influenza A strains. Based on this analysis, avian-origin H7N9 2013 appears to be a “stealth” virus, capable of evading human cellular and humoral immune response. Should H7N9 develop pandemic potential, this analysis predicts that novel strategies for improving vaccine immunogenicity for this unique low-immunogenicity strain of avian-origin influenza will be urgently needed.