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1.
J Immunol ; 206(10): 2489-2497, 2021 05 15.
Artículo en Inglés | MEDLINE | ID: mdl-33789985

RESUMEN

MHC peptide binding and presentation is the most selective event defining the landscape of T cell epitopes. Consequently, understanding the diversity of MHC alleles in a given population and the parameters that define the set of ligands that can be bound and presented by each of these alleles (the immunopeptidome) has an enormous impact on our capacity to predict and manipulate the potential of protein Ags to elicit functional T cell responses. Liquid chromatography-mass spectrometry analysis of MHC-eluted ligand data has proven to be a powerful technique for identifying such peptidomes, and methods integrating such data for prediction of Ag presentation have reached a high level of accuracy for both MHC class I and class II. In this study, we demonstrate how these techniques and prediction methods can be readily extended to the bovine leukocyte Ag class II DR locus (BoLA-DR). BoLA-DR binding motifs were characterized by eluted ligand data derived from bovine cell lines expressing a range of DRB3 alleles prevalent in Holstein-Friesian populations. The model generated (NetBoLAIIpan, available as a Web server at www.cbs.dtu.dk/services/NetBoLAIIpan) was shown to have unprecedented predictive power to identify known BoLA-DR-restricted CD4 epitopes. In summary, the results demonstrate the power of an integrated approach combining advanced mass spectrometry peptidomics with immunoinformatics for characterization of the BoLA-DR Ag presentation system and provide a prediction tool that can be used to assist in rational evaluation and selection of bovine CD4 T cell epitopes.


Asunto(s)
Presentación de Antígeno , Linfocitos T CD4-Positivos/inmunología , Biología Computacional/métodos , Epítopos de Linfocito T/inmunología , Antígenos de Histocompatibilidad Clase II/inmunología , Péptidos/inmunología , Alelos , Animales , Secuencia de Bases , Linfocitos T CD4-Positivos/parasitología , Bovinos , Células Cultivadas , Simulación por Computador , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Antígenos de Histocompatibilidad Clase II/genética , Ligandos , Espectrometría de Masas/métodos , Unión Proteica , Theileria annulata , Theileria parva , Theileriosis/inmunología , Theileriosis/parasitología
2.
Nucleic Acids Res ; 48(W1): W449-W454, 2020 07 02.
Artículo en Inglés | MEDLINE | ID: mdl-32406916

RESUMEN

Major histocompatibility complex (MHC) molecules are expressed on the cell surface, where they present peptides to T cells, which gives them a key role in the development of T-cell immune responses. MHC molecules come in two main variants: MHC Class I (MHC-I) and MHC Class II (MHC-II). MHC-I predominantly present peptides derived from intracellular proteins, whereas MHC-II predominantly presents peptides from extracellular proteins. In both cases, the binding between MHC and antigenic peptides is the most selective step in the antigen presentation pathway. Therefore, the prediction of peptide binding to MHC is a powerful utility to predict the possible specificity of a T-cell immune response. Commonly MHC binding prediction tools are trained on binding affinity or mass spectrometry-eluted ligands. Recent studies have however demonstrated how the integration of both data types can boost predictive performances. Inspired by this, we here present NetMHCpan-4.1 and NetMHCIIpan-4.0, two web servers created to predict binding between peptides and MHC-I and MHC-II, respectively. Both methods exploit tailored machine learning strategies to integrate different training data types, resulting in state-of-the-art performance and outperforming their competitors. The servers are available at http://www.cbs.dtu.dk/services/NetMHCpan-4.1/ and http://www.cbs.dtu.dk/services/NetMHCIIpan-4.0/.


Asunto(s)
Presentación de Antígeno , Antígenos de Histocompatibilidad Clase II/metabolismo , Antígenos de Histocompatibilidad Clase I/metabolismo , Programas Informáticos , Secuencias de Aminoácidos , Antígenos de Histocompatibilidad Clase I/química , Antígenos de Histocompatibilidad Clase II/química , Ligandos , Aprendizaje Automático , Péptidos/metabolismo
3.
Immunology ; 162(2): 208-219, 2021 02.
Artículo en Inglés | MEDLINE | ID: mdl-33010039

RESUMEN

Immunogenicity risk assessment is a critical element in protein drug development. Currently, the risk assessment is most often performed using MHC-associated peptide proteomics (MAPPs) and/or T-cell activation assays. However, this is a highly costly procedure that encompasses limited sensitivity imposed by sample sizes, the MHC repertoire of the tested donor cohort and the experimental procedures applied. Recent work has suggested that these techniques could be complemented by accurate, high-throughput and cost-effective prediction of in silico models. However, this work covered a very limited set of therapeutic proteins and eluted ligand (EL) data. Here, we resolved these limitations by showcasing, in a broader setting, the versatility of in silico models for assessment of protein drug immunogenicity. A method for prediction of MHC class II antigen presentation was developed on the hereto largest available mass spectrometry (MS) HLA-DR EL data set. Using independent test sets, the performance of the method for prediction of HLA-DR antigen presentation hotspots was benchmarked. In particular, the method was showcased on a set of protein sequences including four therapeutic proteins and demonstrated to accurately predict the experimental MS hotspot regions at a significantly lower false-positive rate compared with other methods. This gain in performance was particularly pronounced when compared to the NetMHCIIpan-3.2 method trained on binding affinity data. These results suggest that in silico methods trained on MS HLA EL data can effectively and accurately be used to complement MAPPs assays for the risk assessment of protein drugs.


Asunto(s)
Presentación de Antígeno/inmunología , Antígenos HLA-DR/inmunología , Proteínas/inmunología , Epítopos de Linfocito T/inmunología , Antígenos de Histocompatibilidad Clase II/inmunología , Humanos , Ligandos , Activación de Linfocitos/inmunología , Unión Proteica/inmunología , Proteómica/métodos , Medición de Riesgo
4.
Mol Cell Proteomics ; 18(12): 2459-2477, 2019 12.
Artículo en Inglés | MEDLINE | ID: mdl-31578220

RESUMEN

The set of peptides presented on a cell's surface by MHC molecules is known as the immunopeptidome. Current mass spectrometry technologies allow for identification of large peptidomes, and studies have proven these data to be a rich source of information for learning the rules of MHC-mediated antigen presentation. Immunopeptidomes are usually poly-specific, containing multiple sequence motifs matching the MHC molecules expressed in the system under investigation. Motif deconvolution -the process of associating each ligand to its presenting MHC molecule(s)- is therefore a critical and challenging step in the analysis of MS-eluted MHC ligand data. Here, we describe NNAlign_MA, a computational method designed to address this challenge and fully benefit from large, poly-specific data sets of MS-eluted ligands. NNAlign_MA simultaneously performs the tasks of (1) clustering peptides into individual specificities; (2) automatic annotation of each cluster to an MHC molecule; and (3) training of a prediction model covering all MHCs present in the training set. NNAlign_MA was benchmarked on large and diverse data sets, covering class I and class II data. In all cases, the method was demonstrated to outperform state-of-the-art methods, effectively expanding the coverage of alleles for which accurate predictions can be made, resulting in improved identification of both eluted ligands and T-cell epitopes. Given its high flexibility and ease of use, we expect NNAlign_MA to serve as an effective tool to increase our understanding of the rules of MHC antigen presentation and guide the development of novel T-cell-based therapeutics.


Asunto(s)
Algoritmos , Biología Computacional/métodos , Epítopos de Linfocito T/metabolismo , Antígenos de Histocompatibilidad Clase II/metabolismo , Antígenos de Histocompatibilidad Clase I/metabolismo , Secuencias de Aminoácidos , Animales , Benchmarking , Bovinos , Línea Celular , Bases de Datos de Proteínas , Conjuntos de Datos como Asunto , Humanos , Ligandos , Aprendizaje Automático , Espectrometría de Masas , Péptidos/metabolismo , Unión Proteica
5.
J Proteome Res ; 19(6): 2304-2315, 2020 06 05.
Artículo en Inglés | MEDLINE | ID: mdl-32308001

RESUMEN

Major histocompatibility complex II (MHC II) molecules play a vital role in the onset and control of cellular immunity. In a highly selective process, MHC II presents peptides derived from exogenous antigens on the surface of antigen-presenting cells for T cell scrutiny. Understanding the rules defining this presentation holds critical insights into the regulation and potential manipulation of the cellular immune system. Here, we apply the NNAlign_MA machine learning framework to analyze and integrate large-scale eluted MHC II ligand mass spectrometry (MS) data sets to advance prediction of CD4+ epitopes. NNAlign_MA allows integration of mixed data types, handling ligands with multiple potential allele annotations, encoding of ligand context, leveraging information between data sets, and has pan-specific power allowing accurate predictions outside the set of molecules included in the training data. Applying this framework, we identified accurate binding motifs of more than 50 MHC class II molecules described by MS data, particularly expanding coverage for DP and DQ beyond that obtained using current MS motif deconvolution techniques. Furthermore, in large-scale benchmarking, the final model termed NetMHCIIpan-4.0 demonstrated improved performance beyond current state-of-the-art predictors for ligand and CD4+ T cell epitope prediction. These results suggest that NNAlign_MA and NetMHCIIpan-4.0 are powerful tools for analysis of immunopeptidome MS data, prediction of T cell epitopes, and development of personalized immunotherapies.


Asunto(s)
Presentación de Antígeno , Antígenos de Histocompatibilidad Clase I , Antígenos de Histocompatibilidad Clase I/metabolismo , Antígenos de Histocompatibilidad Clase II/genética , Antígenos de Histocompatibilidad Clase II/metabolismo , Ligandos , Complejo Mayor de Histocompatibilidad , Espectrometría de Masas , Unión Proteica
6.
J Leukoc Biol ; 115(5): 913-925, 2024 04 29.
Artículo en Inglés | MEDLINE | ID: mdl-38214568

RESUMEN

The set of peptides processed and presented by major histocompatibility complex class II molecules defines the immunopeptidome, and its characterization holds keys to understanding essential properties of the immune system. High-throughput mass spectrometry (MS) techniques enable interrogation of the diversity and complexity of the immunopeptidome at an unprecedented scale. Here, we analyzed a large set of MS immunopeptidomics data from 40 donors, 221 samples, covering 30 unique HLA-DR molecules. We identified likely co-immunoprecipitated HLA-DR irrelevant contaminants using state-of-the-art prediction methods and unveiled novel light on the properties of HLA antigen processing and presentation. The ligandome (HLA binders) was enriched in 15-mer peptides, and the contaminome (nonbinders) in longer peptides. Classification of singletons and nested sets showed that the first were enriched in contaminants. Investigating the source protein location of ligands revealed that only contaminants shared a positional bias. Regarding subcellular localization, nested peptides were found to be predominantly of endolysosomal origin, whereas singletons shared an equal distribution between the cytosolic and endolysosomal origin. According to antigen-processing signatures, no significant differences were observed between the cytosolic and endolysosomal ligands. Further, the sensitivity of MS immunopeptidomics was investigated by analyzing overlap and saturation between biological MS replicas, concluding that at least 5 replicas are needed to identify 80% of the immunopeptidome. Moreover, the overlap in immunopeptidome between donors was found to be very low both in terms of peptides and source proteins, the latter indicating a critical HLA bias in the antigen sampling in the HLA antigen presentation. Finally, the complementarity between MS and in silico approaches for comprehensively sampling the immunopeptidome was demonstrated.


Asunto(s)
Presentación de Antígeno , Antígenos HLA-DR , Péptidos , Humanos , Antígenos HLA-DR/inmunología , Antígenos HLA-DR/metabolismo , Péptidos/inmunología , Presentación de Antígeno/inmunología , Ligandos , Espectrometría de Masas , Proteómica/métodos
7.
Front Immunol ; 15: 1360281, 2024.
Artículo en Inglés | MEDLINE | ID: mdl-38633261

RESUMEN

Background: Mutation-derived neoantigens are critical targets for tumor rejection in cancer immunotherapy, and better tools for neoepitope identification and prediction are needed to improve neoepitope targeting strategies. Computational tools have enabled the identification of patient-specific neoantigen candidates from sequencing data, but limited data availability has hindered their capacity to predict which of the many neoepitopes will most likely give rise to T cell recognition. Method: To address this, we make use of experimentally validated T cell recognition towards 17,500 neoepitope candidates, with 467 being T cell recognized, across 70 cancer patients undergoing immunotherapy. Results: We evaluated 27 neoepitope characteristics, and created a random forest model, IMPROVE, to predict neoepitope immunogenicity. The presence of hydrophobic and aromatic residues in the peptide binding core were the most important features for predicting neoepitope immunogenicity. Conclusion: Overall, IMPROVE was found to significantly advance the identification of neoepitopes compared to other current methods.


Asunto(s)
Neoplasias , Linfocitos T , Humanos , Inmunoterapia/métodos
8.
Vaccines (Basel) ; 10(11)2022 Nov 11.
Artículo en Inglés | MEDLINE | ID: mdl-36423003

RESUMEN

The apicomplexan parasite Theileria parva is the causative agent of East Coast fever, usually a fatal disease for cattle, which is prevalent in large areas of eastern, central, and southern Africa. Protective immunity against T. parva is mediated by CD8+ T cells, with CD4+ T-cells thought to be important in facilitating the full maturation and development of the CD8+ T-cell response. T. parva has a large proteome, with >4000 protein-coding genes, making T-cell antigen identification using conventional screening approaches laborious and expensive. To date, only a limited number of T-cell antigens have been described. Novel approaches for identifying candidate antigens for T. parva are required to replace and/or complement those currently employed. In this study, we report on the use of immunopeptidomics to study the repertoire of T. parva peptides presented by both BoLA-I and BoLA-DR molecules on infected cells. The study reports on peptides identified from the analysis of 13 BoLA-I and 6 BoLA-DR datasets covering a range of different BoLA genotypes. This represents the most comprehensive immunopeptidomic dataset available for any eukaryotic pathogen to date. Examination of the immunopeptidome data suggested the presence of a large number of coprecipitated and non-MHC-binding peptides. As part of the work, a pipeline to curate the datasets to remove these peptides was developed and used to generate a final list of 74 BoLA-I and 15 BoLA-DR-presented peptides. Together, the data demonstrated the utility of immunopeptidomics as a method to identify novel T-cell antigens for T. parva and the importance of careful curation and the application of high-quality immunoinformatics to parse the data generated.

9.
Front Immunol ; 11: 1304, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32655572

RESUMEN

Recombinant DNA technology has, in the last decades, contributed to a vast expansion of the use of protein drugs as pharmaceutical agents. However, such biological drugs can lead to the formation of anti-drug antibodies (ADAs) that may result in adverse effects, including allergic reactions and compromised therapeutic efficacy. Production of ADAs is most often associated with activation of CD4 T cell responses resulting from proteolysis of the biotherapeutic and loading of drug-specific peptides into major histocompatibility complex (MHC) class II on professional antigen-presenting cells. Recently, readouts from MHC-associated peptide proteomics (MAPPs) assays have been shown to correlate with the presence of CD4 T cell epitopes. However, the limited sensitivity of MAPPs challenges its use as an immunogenicity biomarker. In this work, MAPPs data was used to construct an artificial neural network (ANN) model for MHC class II antigen presentation. Using Infliximab and Rituximab as showcase stories, the model demonstrated an unprecedented performance for predicting MAPPs and CD4 T cell epitopes in the context of protein-drug immunogenicity, complementing results from MAPPs assays and outperforming conventional prediction models trained on binding affinity data.


Asunto(s)
Antirreumáticos/farmacología , Antígenos de Histocompatibilidad Clase II/inmunología , Infliximab/farmacología , Redes Neurales de la Computación , Rituximab/farmacología , Linfocitos T CD4-Positivos/efectos de los fármacos , Linfocitos T CD4-Positivos/inmunología , Células Dendríticas/efectos de los fármacos , Células Dendríticas/inmunología , Epítopos de Linfocito T/efectos de los fármacos , Epítopos de Linfocito T/inmunología , Humanos , Espectrometría de Masas , Péptidos/inmunología , Unión Proteica , Proteómica
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