POPISK: T-cell reactivity prediction using support vector machines and string kernels

BACKGROUND: Accurate prediction of peptide immunogenicity and characterization of relation between peptide sequences and peptide immunogenicity will be greatly helpful for vaccine designs and understanding of the immune system. In contrast to the prediction of antigen processing and presentation pathway, the prediction of subsequent T-cell reactivity is a much harder topic. Previous studies of identifying T-cell receptor (TCR) recognition positions were based on small-scale analyses using only a few peptides and concluded different recognition positions such as positions 4, 6 and 8 of peptides with length 9. Large-scale analyses are necessary to better characterize the effect of peptide sequence variations on T-cell reactivity and design predictors of a peptide's T-cell reactivity (and thus immunogenicity). The identification and characterization of important positions influencing T-cell reactivity will provide insights into the underlying mechanism of immunogenicity. RESULTS: This work establishes a large dataset by collecting immunogenicity data from three major immunology databases. In order to consider the effect of MHC restriction, peptides are classified by their associated MHC alleles. Subsequently, a computational method (named POPISK) using support vector machine with a weighted degree string kernel is proposed to predict T-cell reactivity and identify important recognition positions. POPISK yields a mean 10-fold cross-validation accuracy of 68% in predicting T-cell reactivity of HLA-A2-binding peptides. POPISK is capable of predicting immunogenicity with scores that can also correctly predict the change in T-cell reactivity related to point mutations in epitopes reported in previous studies using crystal structures. Thorough analyses of the prediction results identify the important positions 4, 6, 8 and 9, and yield insights into the molecular basis for TCR recognition. Finally, we relate this finding to physicochemical properties and structural features of the MHC-peptide-TCR interaction. CONCLUSIONS: A computational method POPISK is proposed to predict immunogenicity with scores which are useful for predicting immunogenicity changes made by single-residue modifications. The web server of POPISK is freely available at http://iclab.life.nctu.edu.tw/POPISK

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

Rapid assessment of T-cell receptor specificity of the immune repertoire

Accurate assessment of T-cell-receptor (TCR)–antigen specificity across the whole immune repertoire lies at the heart of improved cancer immunotherapy, but predictive models capable of high-throughput assessment of TCR–peptide pairs are lacking. Recent advances in deep sequencing and crystallography have enriched the data available for studying TCR–peptide systems. Here, we introduce RACER, a pairwise energy model capable of rapid assessment of TCR–peptide affinity for entire immune repertoires. RACER applies supervised machine learning to efficiently and accurately resolve strong TCR–peptide binding pairs from weak ones. The trained parameters further enable a physical interpretation of interacting patterns encoded in each TCR–peptide system. When applied to simulate thymic selection of a major-histocompatibility-complex (MHC)-restricted T-cell repertoire, RACER accurately estimates recognition rates for tumor-associated neoantigens and foreign peptides, thus demonstrating its utility in helping address the computational challenge of reliably identifying properties of tumor antigen-specific T-cells at the level of an individual patient’s immune repertoire

The rule based classification models for MHC binding prediction and identification of the most relevant physicochemical properties for the individual allele

Binding of proteolyzed fragments of proteins to MHC molecules is essential and the most selective step that determines T-cell epitopes. Therefore, the prediction of MHC-peptide binding is principal for anticipating potential T cell epitopes and is of immense relevance in vaccine design. Despite numerous methods for predicting MHC binding ligands, there still exist limitations that affect the reliability of a prevailing number of methods. Certain important methods based on physicochemical properties have very low reported accuracy. The aim of this paper is to present a new approach of extracting the most important physicochemical properties that influence the classification of MHC-binding ligands. In this study, we have developed rule based classification models which take into account the physicochemical properties of amino acids and their frequencies. The models use k-means clustering technique for extracting the relevant physicochemical properties. The results of the study indicate that the physicochemical properties of amino acids contribute significantly to the peptide-binding and that the different alleles are characterized by a different set of the physicochemical properties

Quantitative Prediction of the Landscape of T Cell Epitope Immunogenicity in Sequence Space

Immunodominant T cell epitopes preferentially targeted in multiple individuals are the critical element of successful vaccines and targeted immunotherapies. However, the underlying principles of this “convergence” of adaptive immunity among different individuals remain poorly understood. To quantitatively describe epitope immunogenicity, here we propose a supervised machine learning framework generating probabilistic estimates of immunogenicity, termed “immunogenicity scores,” based on the numerical features computed through sequence-based simulation approximating the molecular scanning process of peptides presented onto major histocompatibility complex (MHC) by the human T cell receptor (TCR) repertoire. Notably, overlapping sets of intermolecular interaction parameters were commonly utilized in MHC-I and MHC-II prediction. Moreover, a similar simulation of individual TCR-peptide interaction using the same set of interaction parameters yielded correlates of TCR affinity. Pathogen-derived epitopes and tumor-associated epitopes with positive T cell reactivity generally had higher immunogenicity scores than non-immunogenic counterparts, whereas thymically expressed self-epitopes were assigned relatively low scores regardless of their immunogenicity annotation. Immunogenicity score dynamics among single amino acid mutants delineated the landscape of position- and residue-specific mutational impacts. Simulation of position-specific immunogenicity score dynamics detected residues with high escape potential in multiple epitopes, consistent with known escape mutations in the literature. This study indicates that targeting of epitopes by human adaptive immunity is to some extent directed by defined thermodynamic principles. The proposed framework also has a practical implication in that it may enable to more efficiently prioritize epitope candidates highly prone to T cell recognition in multiple individuals, warranting prospective validation across different cohorts

The most common Chinese rhesus macaque MHC class I molecule shares peptide binding repertoire with the HLA-B7 supertype

Of the two rhesus macaque subspecies used for AIDS studies, the Simian immunodeficiency virus-infected Indian rhesus macaque (Macaca mulatta) is the most established model of HIV infection, providing both insight into pathogenesis and a system for testing novel vaccines. Despite the Chinese rhesus macaque potentially being a more relevant model for AIDS outcomes than the Indian rhesus macaque, the Chinese-origin rhesus macaques have not been well-characterized for their major histocompatibility complex (MHC) composition and function, reducing their greater utilization. In this study, we characterized a total of 50 unique Chinese rhesus macaques from several varying origins for their entire MHC class I allele composition and identified a total of 58 unique complete MHC class I sequences. Only nine of the sequences had been associated with Indian rhesus macaques, and 28/58 (48.3%) of the sequences identified were novel. From all MHC alleles detected, we prioritized Mamu-A1*02201 for functional characterization based on its higher frequency of expression. Upon the development of MHC/peptide binding assays and definition of its associated motif, we revealed that this allele shares peptide binding characteristics with the HLA-B7 supertype, the most frequent supertype in human populations. These studies provide the first functional characterization of an MHC class I molecule in the context of Chinese rhesus macaques and the first instance of HLA-B7 analogy for rhesus macaques