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.Fil: Nielsen, Morten. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina. Technical University of Denmark; DinamarcaFil: Andreatta, Massimo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Buus, Søren. Universidad de Copenhagen; Dinamarc

Evaluating the predictive performance of cytotoxic T lymphocyte epitope prediction tools using Elispot assay data

Computational T-cell epitope prediction tools have been previously devised to predict potential human leukocyte antigen (HLA) binding peptides from protein sequences. These tools are complements of Enzyme-linked immunosorbent spot (ELISpot) assays - a very commonly applied immunological technique that is used both to identify regions of pathogen genomes that trigger an immune response and to characterize the relationships between an individual's complement of HLA alleles and the degree of immunity that they display. If computational tools could accurately predict HLA-peptide binding, then these tools might be useable as a cheap and reliable alternative to ELISpot assays. A web-based IFN γ ELISpot assay dataset sharing resource, called IMMUNO-SHARE, was developed to enable the simple and straightforward storage and dissemination amongst researchers of large volumes of IFN γ ELISpot assay data. Such experimental data was next used to make HLA-peptide binding predictions with four frequently used T-cell epitope prediction tools - netMHC 3.2, IEDB_ANN, IEDB_ARB Matrix and IEDB_SMM. The predictive performances of all four tools individually and collectively was statistically assessed using non-parametric Spearman rank-order correlation tests. It was found that none of the four tested tools yielded binding affinity predictions that were detectably correlated with the observed ELISpot data. High false positive rates, where high predicted binding affinities between peptides and patient HLAs corresponded in these patients with no appreciable immune responses, were apparent for all four of the tested methods. The low degree of correlation between ELISpot data and HLA-peptide binding predictions and in particular, high false positive rates and relatively low true positive and true negative rates, indicate that the four tested tools would require substantial improvement before they could be seen as a viable alternative to ELISpot assays. Given that the accuracy of predictions of each of the four methods tested is largely dependent on both the quantity and quality of known true binder and true non-binder datasets that were used to train the HLA-peptide binding prediction methods implemented by the tools, it is plausible that the accuracy of these tools could be increased with larger training datasets. Retraining either the current methods or the next generation of prediction tools would therefore be greatly facilitated by the availability of large quantities of publically available HLA-peptide binding interaction information. It is hoped that IMMUNO-SHARE or some other ELISpot data sharing resource could eventually meet this need

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. The method is available at http://www.imtech.res.in/raghava/ctlpred/

The influence of HLA genotype on the development of metal hypersensitivity following joint replacement

We thank Innovate UK Edge for providing funding to allow this research to be carried out.Background  Over five million joint replacements are performed across the world each year. Cobalt chrome (CoCr) components are used in most of these procedures. Some patients develop delayed type hypersensitivity (DTH) responses to CoCr implants, resulting in tissue damage and revision surgery. DTH is unpredictable and genetic links have yet to be definitively established. Methods At a single site, we carried out an initial investigation to identify HLA alleles associated with development of DTH following metal-on-metal hip arthroplasty. We then recruited patients from other centres to train and validate an algorithm incorporating patient age, gender, HLA genotype44 and blood metal concentrations to predict the development of DTH. Accuracy of the modelling was assessed using performance metrics including time dependent receiver operator curves. Results Using next generation sequencing, here we determine the HLA genotypes of 606 patients. 176 of these patients had experienced failure of their prostheses; the remaining 430 remain asymptomatic at a mean follow up of twelve years. We demonstrate that the development of DTH is associated with patient age, gender, the magnitude of metal exposure and the presence of certain HLA class II alleles. We show that the predictive algorithm developed from this investigation performs to an accuracy suitable for clinical use, with weighted mean survival probability errors of 1.8% and 3.1%53 for pre-operative and post-operative models respectively. Conclusions The development of DTH following joint replacement appears to be determined by the interaction between implant wear and a patient’s genotype. The algorithm described in this paper may improve implant selection and help direct patient surveillance following surgery. Further consideration should be given towards understanding patient specific responses to different biomaterials.Publisher PDFPeer reviewe

NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets

Allele-specific length preference for 24 MHC molecules characterized by 20 or more ligand data points for the allmer and 9mer prediction methods compared to the length preference in the SYFPEITHI data. Length profiles for the allmer and 9mer methods were estimated as described in the text. (XLSX 50 kb

Evaluation of MHC class I peptide binding prediction servers: Applications for vaccine research

<b>Background</b> Protein antigens and their specific epitopes are formulation targets for epitope-based vaccines. A number of prediction servers are available for identification of peptides that bind major histocompatibility complex class I (MHC-I) molecules. The lack of standardized methodology and large number of human MHC-I molecules make the selection of appropriate prediction servers difficult. This study reports a comparative evaluation of thirty prediction servers for seven human MHC-I molecules.<p></p> <b>Results</b> Of 147 individual predictors 39 have shown excellent, 47 good, 33 marginal, and 28 poor ability to classify binders from non-binders. The classifiers for HLA-A*0201, A*0301, A*1101, B*0702, B*0801, and B*1501 have excellent, and for A*2402 moderate classification accuracy. Sixteen prediction servers predict peptide binding affinity to MHC-I molecules with high accuracy; correlation coefficients ranging from r = 0.55 (B*0801) to r = 0.87 (A*0201).<p></p> <b>Conclusion</b> Non-linear predictors outperform matrix-based predictors. Most predictors can be improved by non-linear transformations of their raw prediction scores. The best predictors of peptide binding are also best in prediction of T-cell epitopes. We propose a new standard for MHC-I binding prediction – a common scale for normalization of prediction scores, applicable to both experimental and predicted data. The results of this study provide assistance to researchers in selection of most adequate prediction tools and selection criteria that suit the needs of their projects