Determination of a predictive cleavage motif for eluted major histocompatibility complex class II ligands

CD4+ T cells have a major role in regulating immune responses. They are activated by recognition of peptides mostly generated from exogenous antigens through the major histocompatibility complex (MHC) class II pathway. Identification of epitopes is important and computational prediction of epitopes is used widely to save time and resources. Although there are algorithms to predict binding affinity of peptides to MHC II molecules, no accurate methods exist to predict which ligands are generated as a result of natural antigen processing. We utilized a dataset of around 14,000 naturally processed ligands identified by mass spectrometry of peptides eluted from MHC class II expressing cells to investigate the existence of sequence signatures potentially related to the cleavage mechanisms that liberate the presented peptides from their source antigens. This analysis revealed preferred amino acids surrounding both N- and C-terminuses of ligands, indicating sequence-specific cleavage preferences. We used these cleavage motifs to develop a method for predicting naturally processed MHC II ligands, and validated that it had predictive power to identify ligands from independent studies. We further confirmed that prediction of ligands based on cleavage motifs could be combined with predictions of MHC binding, and that the combined prediction had superior performance. However, when attempting to predict CD4+ T cell epitopes, either alone or in combination with MHC binding predictions, predictions based on the cleavage motifs did not show predictive power. Given that peptides identified as epitopes based on CD4+ T cell reactivity typically do not have well-defined termini, it is possible that motifs are present but outside of the mapped epitope. Our attempts to take that into account computationally did not show any sign of an increased presence of cleavage motifs around well-characterized CD4+ T cell epitopes. While it is possible that our attempts to translate the cleavage motifs in MHC II ligand elution data into T cell epitope predictions were suboptimal, other possible explanations are that the cleavage signal is too diluted to be detected, or that elution data are enriched for ligands generated through an antigen processing and presentation pathway that is less frequently utilized for T cell epitopes.Fil: Paul, Sinu. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Karosiene, Edita. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Dhanda, Sandeep Kumar. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Jurtz, Vanessa. Technical University of Denmark; DinamarcaFil: Edwards, Lindy. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: 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: Sette, Alessandro. University of California at San Diego; Estados Unidos. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados Unidos. University of California at San Diego; Estados Unido

Review of Immunoinformatic approaches to in-silico B-cell epitope prediction

In this paper, the current state of in-silico, B-cell epitope prediction is discussed. Recommendations for improving some of the approaches encountered are outlined, along with the presentation of an entirely novel technique, which uses molecular mechanics for epitope classification, evaluation and prediction

Definition of Naturally Processed Peptides Reveals Convergent Presentation of Autoantigenic Topoisomerase I Epitopes in Scleroderma.

ObjectiveAutoimmune responses to DNA topoisomerase I (topo I) are found in a subset of scleroderma patients who are at high risk for interstitial lung disease (ILD) and mortality. Anti-topo I antibodies (ATAs) are associated with specific HLA-DRB1 alleles, and the frequency of HLA-DR-restricted topo I-specific CD4+ T cells is associated with the presence, severity, and progression of ILD. Although this strongly implicates the presentation of topo I peptides by HLA-DR in scleroderma pathogenesis, the processing and presentation of topo I has not been studied.MethodsWe developed a natural antigen processing assay (NAPA) to identify putative CD4+ T cell epitopes of topo I presented by monocyte-derived dendritic cells (mo-DCs) from 6 ATA-positive patients with scleroderma. Mo-DCs were pulsed with topo I protein, HLA-DR-peptide complexes were isolated, and eluted peptides were analyzed by mass spectrometry. We then examined the ability of these naturally presented peptides to induce CD4+ T cell activation in 11 ATA-positive and 11 ATA-negative scleroderma patients.ResultsWe found that a common set of 10 topo I epitopes was presented by Mo-DCs from scleroderma patients with diverse HLA-DR variants. Sequence analysis revealed shared peptide-binding motifs within the HLA-DRβ chains of ATA-positive patients and a subset of topo I epitopes with distinct sets of anchor residues capable of binding to multiple different HLA-DR variants. The NAPA-derived epitopes elicited robust CD4+ T cell responses in 73% of ATA-positive patients (8 of 11), and the number of epitopes recognized correlated with ILD severity (P = 0.025).ConclusionThese findings mechanistically implicate the presentation of a convergent set of topo I epitopes in the development of scleroderma

ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins

Additional file 2: Table S2. Dipeptide composition distribution between proinflammatory and non proinflammatory data

Cell cycle-dependent phosphorylation of Theileria annulata schizont surface proteins

The invasion of Theileria sporozoites into bovine leukocytes is rapidly followed by the destruction of the surrounding host cell membrane, allowing the parasite to establish its niche within the host cell cytoplasm. Theileria infection induces host cell transformation, characterised by increased host cell proliferation and invasiveness, and the activation of anti-apoptotic genes. This process is strictly dependent on the presence of a viable parasite. Several host cell kinases, including PI3-K, JNK, CK2 and Src-family kinases, are constitutively activated in Theileria-infected cells and contribute to the transformed phenotype. Although a number of host cell molecules, including IkB kinase and polo-like kinase 1 (Plk1), are recruited to the schizont surface, very little is known about the schizont molecules involved in host-parasite interactions. In this study we used immunofluorescence to detect phosphorylated threonine (p-Thr), serine (p-Ser) and threonine-proline (p-Thr-Pro) epitopes on the schizont during host cell cycle progression, revealing extensive schizont phosphorylation during host cell interphase. Furthermore, we established a quick protocol to isolate schizonts from infected macrophages following synchronisation in S-phase or mitosis, and used mass spectrometry to detect phosphorylated schizont proteins. In total, 65 phosphorylated Theileria proteins were detected, 15 of which are potentially secreted or expressed on the surface of the schizont and thus may be targets for host cell kinases. In particular, we describe the cell cycle-dependent phosphorylation of two T. annulata surface proteins, TaSP and p104, both of which are highly phosphorylated during host cell S-phase. TaSP and p104 are involved in mediating interactions between the parasite and the host cell cytoskeleton, which is crucial for the persistence of the parasite within the dividing host cell and the maintenance of the transformed state

Applying bioinformatics for antibody epitope prediction using affinity-selected mimotopes – relevance for vaccine design

To properly characterize protective polyclonal antibody responses, it is necessary to examine epitope specificity. Most antibody epitopes are conformational in nature and, thus, cannot be identified using synthetic linear peptides. Cyclic peptides can function as mimetics of conformational epitopes (termed mimotopes), thereby providing targets, which can be selected by immunoaffinity purification. However, the management of large collections of random cyclic peptides is cumbersome. Filamentous bacteriophage provides a useful scaffold for the expression of random peptides (termed phage display) facilitating both the production and manipulation of complex peptide libraries. Immunoaffinity selection of phage displaying random cyclic peptides is an effective strategy for isolating mimotopes with specificity for a given antiserum. Further epitope prediction based on mimotope sequence is not trivial since mimotopes generally display only small homologies with the target protein. Large numbers of unique mimotopes are required to provide sufficient sequence coverage to elucidate the target epitope. We have developed a method based on pattern recognition theory to deal with the complexity of large collections of conformational mimotopes. The analysis consists of two phases: 1) The learning phase where a large collection of epitope-specific mimotopes is analyzed to identify epitope specific “signs” and 2) The identification phase where immunoaffinity-selected mimotopes are interrogated for the presence of the epitope specific “signs” and assigned to specific epitopes. We are currently using computational methods to define epitope “signs” without the need for prior knowledge of specific mimotopes. This technology provides an important tool for characterizing the breadth of antibody specificities within polyclonal antisera

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/

Immunoinformatic identification of CD8+ T-cell epitopes

Antigen-specific T-cells play a crucial role in the adaptive immune response by providing a defence mechanism against pathogens and maintaining tolerance against self-antigens. This sparked interest in the development of epitope-based vaccines and immunotherapies that elicit antigen-specific T-cell responses. However, screening the antigens driving the response is currently labour-intensive, low-throughput and costly. Due to the limitations of experimental approaches, computational methods for predicting CD8+ T-cells have started to emerge. However, predicting the T-cell recognition potential of MHC-presented peptides has shown to be more challenging than predicting MHC ligands, and the full spectrum of features underlying peptide immunogenicity remains to be explored. Hence, this thesis presents a systems biology approach to study features of peptide immunogenicity and accurately predict CD8+ T-cell epitopes from HLA-I presented pathogenic or cancer peptides. The thesis begins with an immunoinformatic analysis of antigen-specific T-cell profiles in the contexts of autoinflammatory and infectious diseases. In autoinflammatory disease, the multi-modal single-cell sequencing of ulcerative colitis and checkpoint treatment-induced colitis revealed pathology-specific differential expressions of cytotoxic T-cells. The current technologies, however, were unable to identify the source antigen, emphasising the importance of predicting T-cell targets to better understand disease pathology. Moreover, in infectious diseases, CD8+ T-cell epitope prediction algorithms facilitated the understanding of disease heterogeneity and vaccine design during the COVID-19 pandemic, but many existing algorithms were found to be ill-suited for predicting epitopes from emerging pathogens. Therefore, a novel computational workflow was developed for an accurate and robust prediction of source antigens driving the cellular immune response. First, an unbiased evaluation of state-of-the-art algorithms revealed that they perform poorly on both cancer neoepitopes (e.g. glioblastoma) and pathogenic (e.g. SARS-CoV-2) epitopes. After investigating the reasons for low performance, TRAP, a deep learning workflow for context-specific prediction of CD8+ T-cell epitopes, was developed to effectively capture T-cell recognition motifs. The application of TRAP was demonstrated by using it to investigate the immune escape potential of all theoretical SARS-CoV-2 mutants. Thus, this thesis presents a novel computational platform for accurately predicting CD8+ T-cell epitopes to foster a better understanding of TCR:pMHC interaction and the development of effective clinical therapeutics