Large-scale computational identification of HIV T-cell epitopes

Novel HLA-B7-restricted human metapneumovirus epitopes enhance viral clearance in mice and are recognized by human CD8+ T cells

Human metapneumovirus (HMPV) is a leading cause of acute lower respiratory tract illness in children and adults. Repeated infections are common and can be severe in young, elderly, and immunocompromised persons due to short-lived protective humoral immunity. In turn, few protective T cell epitopes have been identified in humans. Thus, we infected transgenic mice expressing the common human HLA MHC-I allele B*07:02 (HLA-B7) with HMPV and screened a robust library of overlapping and computationally predicted HLA-B7 binding peptides. Six HLA-B7-restricted CD8+ T cell epitopes were identified using ELISPOT screening in the F, M, and N proteins, with M195-203 (M195) eliciting the strongest responses. MHC-tetramer flow cytometric staining confirmed HLA-B7 epitope-specific CD8+ T cells migrated to lungs and spleen of HMPV-immune mice. Immunization with pooled HLA-B7-restricted peptides reduced viral titer and protected mice from virulent infection. Finally, we confirmed that CD8+ T cells from HLA-B7 positive humans also recognize the identified epitopes. These results enable identification of HMPV-specific CD8+ T cells in humans and help to inform future HMPV vaccine design.

Novel Identified HLA-A*0201-Restricted Hantaan Virus Glycoprotein Cytotoxic T-Cell Epitopes Could Effectively Induce Protective Responses in HLA-A2.1/Kb Transgenic Mice May Associate with the Severity of Hemorrhagic Fever with Renal Syndrome

Hantaan virus (HTNV) infections can cause severe hemorrhagic fever with renal syndrome (HFRS) in humans, which is associated with high fatality rates. Cytotoxic T cell (CTL) responses contribute to virus elimination; however, to date, HLA class I allele-restricted HTNV glycoprotein (GP) epitopes recognized by CTLs have not been reported, limiting our understanding of CTL responses against HTNV infection in humans. In this study, 34 HTNV GP nine-mer epitopes that may bind to HLA-A*0201 molecules were predicted using the BIMAS and SYFPEITHI database. Seven of the epitopes were demonstrated to bind to HLA-A*0201 molecules with high affinity via the T2 cell binding assay and were successfully used to synthesize peptide/HLA-A*0201 tetramers. The results of tetramer staining showed that the frequencies of each epitope-specific CTL were higher in patients with milder HFRS, which indicated that the epitopes may induce protective CTL responses after HTNV infection. IFN-γ-enzyme-linked immunospot analysis further confirmed the immunoreactivity of epitopes by eliciting epitope-specific IFN-γ-producing CTL responses. In an HTNV challenge trial, significant inhibition of HTNV replication characterized by lower levels of antigens and RNA loads was observed in major target organs (liver, spleen, and kidneys) of HLA-A2.1/K^b transgenic mice pre-vaccinated with nonapeptides VV9 (aa8–aa16, VMASLVWPV), SL9 (aa996–aa1004, SLTECPTFL) and LL9 (aa358–aa366, LIWTGMIDL). Importantly, LL9 exhibited the best ability to induce protective CTL responses and showed a prominent effect on the kidneys, potentially preventing kidney injury after HTNV infection. Taken together, our results highlight that HTNV GP-derived HLA-A*0201-restricted epitopes could elicit protective CTL responses against the virus, and that epitope LL9 functions as an immunodominant protective epitope that may advance the design of safe and effective CTL-based HTNV peptide vaccines for humans.

Novel HLA-A2-restricted human metapneumovirus epitopes reduce viral titers in mice and are recognized by human T cells

Human metapneumovirus (HMPV) is a major cause of morbidity and mortality from acute lower respiratory tract illness, with most individuals seropositive by age five. Despite the presence of neutralizing antibodies, secondary infections are common and can be severe in young, elderly, and immunocompromised persons. Preclinical vaccine studies for HMPV have suggested a need for a balanced antibody and T cell immune response to enhance protection and avoid lung immunopathology. We infected transgenic mice expressing human HLA-A*0201 with HMPV and used ELISPOT to screen overlapping and predicted epitope peptides. We identified six novel HLA-A2 restricted CD8(+) T cell (TCD8) epitopes, with M39-47 (M39) immunodominant. Tetramer staining detected M39-specific TCD8 in lungs and spleen of HMPV-immune mice. Immunization with adjuvant-formulated M39 peptide reduced lung virus titers upon challenge. Finally, we show that TCD8 from HLA-A*0201 positive humans recognize M39 by IFNγ ELISPOT and tetramer staining. These results will facilitate HMPV vaccine development and human studies.

Unix interfaces, Kleisli, bucandin structure, etc. -- the heroic beginning of bioinformatics in Singapore

Remarkably, Singapore as one of today's hotspots for bioinformatics and computational biology research appeared de novo out of pioneering efforts of engaged local individuals in the early 90-s that, supported with increasing public funds from 1996 on, morphed into the present vibrant research community. This article brings to mind the pioneers, their first successes and early institutional developments.

Information technologies for vaccine research

Vaccinology is a combinatorial science which studies the diversity of pathogens and the human immune system, and formulations that can modulate immune responses and prevent or cure disease. Huge amounts of data are produced by genomics and proteomics projects and large-scale screening of pathogen-host and antigen-host interactions. Current developments in computational vaccinology mainly support the analysis of antigen processing and presentation and the characterization of targets of immune response. Future development will also include systemic models of vaccine responses. Immunomics, the large-scale screening of immune processes which includes powerful immunoinformatic tools, offers great promise for future translation of basic immunology research advances into successful vaccines.

MHC Class II Binding Prediction by Molecular Docking

Proteins of the Major Histocompatibility Complex (MHC) bind self and nonself peptide antigens or epitopes within the cell and present them at the cell surface for recognition by T cells. All T-cell epitopes are MHC binders but not all MCH binders are T-cell epitopes. The MHC class II proteins are extremely polymorphic. Polymorphic residues cluster in the peptide-binding region and largely determine the MHC's peptide selectivity. The peptide binding site on MHC class II proteins consist of five binding pockets. Using molecular docking, we have modelled the interactions between peptide and MHC class II proteins from locus DRB1. A combinatorial peptide library was generated by mutation of residues at peptide positions which correspond to binding pockets (so called anchor positions). The binding affinities were assessed using different scoring functions. The normalized scoring functions for each amino acid at each anchor position were used to construct quantitative matrices (QM) for MHC class II binding prediction. Models were validated by external test sets comprising 4540 known binders. Eighty percent of the known binders are identified in the best predicted 15 % of all overlapping peptides, originating from one protein.

pDOCK: a new technique for rapid and accurate docking of peptide ligands to Major Histocompatibility Complexes

Background

Identification of antigenic peptide epitopes is an essential prerequisite in T cell-based molecular vaccine design. Computational (sequence-based and structure-based) methods are inexpensive and efficient compared to experimental approaches in screening numerous peptides against their cognate MHC alleles. In structure-based protocols, suited to alleles with limited epitope data, the first step is to identify high-binding peptides using docking techniques, which need improvement in speed and efficiency to be useful in large-scale screening studies. We present pDOCK: a new computational technique for rapid and accurate docking of flexible peptides to MHC receptors and primarily apply it on a non-redundant dataset of 186 pMHC (MHC-I and MHC-II) complexes with X-ray crystal structures.

Results

We have compared our docked structures with experimental crystallographic structures for the immunologically relevant nonameric core of the bound peptide for MHC-I and MHC-II complexes. Primary testing for re-docking of peptides into their respective MHC grooves generated 159 out of 186 peptides with Cα RMSD of less than 1.00 Å, with a mean of 0.56 Å. Amongst the 25 peptides used for single and variant template docking, the Cα RMSD values were below 1.00 Å for 23 peptides. Compared to our earlier docking methodology, pDOCK shows upto 2.5 fold improvement in the accuracy and is ~60% faster. Results of validation against previously published studies represent a seven-fold increase in pDOCK accuracy.

Conclusions

The limitations of our previous methodology have been addressed in the new docking protocol making it a rapid and accurate method to evaluate pMHC binding. pDOCK is a generic method and although benchmarks against experimental structures, it can be applied to alleles with no structural data using sequence information. Our outcomes establish the efficacy of our procedure to predict highly accurate peptide structures permitting conformational sampling of the peptide in MHC binding groove. Our results also support the applicability of pDOCK for in silico identification of promiscuous peptide epitopes that are relevant to higher proportions of human population with greater propensity to activate T cells making them key targets for the design of vaccines and immunotherapies.

Immunoinformatics comes of age

With the burgeoning immunological data in the scientific literature, scientists must increasingly rely on Internet resources to inform and enhance their work. Here we provide a brief overview of the adaptive immune response and summaries of immunoinformatics resources, emphasizing those with Web interfaces. These resources include searchable databases of epitopes and immune-related molecules, and analysis tools for T cell and B cell epitope prediction, vaccine design, and protein structure comparisons. There is an agreeable synergy between the growing collections in immune-related databases and the growing sophistication of analysis software; the databases provide the foundation for developing predictive computational tools, which in turn enable more rapid identification of immune responses to populate the databases. Collectively, these resources contribute to improved understanding of immune responses and escape, and evolution of pathogens under immune pressure. The public health implications are vast, including designing vaccines, understanding autoimmune diseases, and defining the correlates of immune protection.

Computational methods for prediction of T-cell epitopes--a framework for modelling, testing, and applications

Computational models complement laboratory experimentation for efficient identification of MHC-binding peptides and T-cell epitopes. Methods for prediction of MHC-binding peptides include binding motifs, quantitative matrices, artificial neural networks, hidden Markov models, and molecular modelling. Models derived by these methods have been successfully used for prediction of T-cell epitopes in cancer, autoimmunity, infectious disease, and allergy. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures and performed according to strict standards. This requires careful selection of data for model building, and adequate testing and validation. A range of web-based databases and MHC-binding prediction programs are available. Although some available prediction programs for particular MHC alleles have reasonable accuracy, there is no guarantee that all models produce good quality predictions. In this article, we present and discuss a framework for modelling, testing, and applications of computational methods used in predictions of T-cell epitopes.

Toward the quantitative prediction of T-cell epitopes: QSAR studies on peptides having affinity with the class I MHC molecular HLA-A*0201

It would be useful for vaccine development to develop a method of rapidly identifying peptide epitopes. In this paper, the empirical three-dimensional quantitative structure-affinity relationship (3D-QSAR) methods were used to study the relationship between the three dimensional structural parameters (the isotropic surface area, ISA, and the electronic charge index, ECI) of the HLA-A*0201 binding peptide and the HLA-A*0201/peptide binding affinities. A set of 102 peptides having affinity with the class I MHC HLA-A*0201 molecule was used as training set. A test set of 40 peptides was used to determine the predictive value of the models. The 3D-QSAR models yielded a q2 = 0.5724 and a high rpred2 = 0.6955. The standard regression coefficients indicated that the hydrophobic interactions played an important role in peptide-MHC molecule binding and predicted the specific amino acid residue essential at a certain position of the peptide. The approach tested in the current paper is highly complementary to many of the methods described in references and possesses good predictability. It is a rapid and convenient method to detect high affinity peptide epitopes.

Prediction of CTL epitopes using QM, SVM and ANN techniques

Cytotoxic T lymphocyte (CTL) epitopes are potential candidates for subunit vaccine design for various diseases. Most of the existing T cell epitope prediction methods are indirect methods that predict MHC class I binders instead of CTL epitopes. In this study, a systematic attempt has been made to develop a direct method for predicting CTL epitopes from an antigenic sequence. This method is based on quantitative matrix (QM) and machine learning techniques such as Support Vector Machine (SVM) and Artificial Neural Network (ANN). This method has been trained and tested on non-redundant dataset of T cell epitopes and non-epitopes that includes 1137 experimentally proven MHC class I restricted T cell epitopes. The accuracy of QM-, ANN- and SVM-based methods was 70.0, 72.2 and 75.2%, respectively. The performance of these methods has been evaluated through Leave One Out Cross-Validation (LOOCV) at a cutoff score where sensitivity and specificity was nearly equal. Finally, both machine-learning methods were used for consensus and combined prediction of CTL epitopes. The performances of these methods were evaluated on blind dataset where machine learning-based methods perform better than QM-based method. We also demonstrated through subgroup analysis that our methods can discriminate between T-cell epitopes and MHC binders (non-epitopes). In brief this method allows prediction of CTL epitopes using QM, SVM, ANN approaches. The method also facilitates prediction of MHC restriction in predicted T cell epitopes.

Towards in silico prediction of immunogenic epitopes

As torrents of new data now emerge from microbial genomics, bioinformatic prediction of immunogenic epitopes remains challenging but vital. In silico methods often produce paradoxically inconsistent results: good prediction rates on certain test sets but not others. The inherent complexity of immune presentation and recognition processes complicates epitope prediction. Two encouraging developments - data driven artificial intelligence sequence-based methods for epitope prediction and molecular modeling methods based on three-dimensional protein structures - offer hope for the future.

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