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Montemurro A, Schuster V, Povlsen HR, Bentzen AK, Jurtz V, Chronister WD, Crinklaw A, Hadrup SR, Winther O, Peters B, Jessen LE, Nielsen M. NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data. Commun Biol 2021; 4:1060. [PMID: 34508155 PMCID: PMC8433451 DOI: 10.1038/s42003-021-02610-3] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 08/27/2021] [Indexed: 12/17/2022] Open
Abstract
Prediction of T-cell receptor (TCR) interactions with MHC-peptide complexes remains highly challenging. This challenge is primarily due to three dominant factors: data accuracy, data scarceness, and problem complexity. Here, we showcase that "shallow" convolutional neural network (CNN) architectures are adequate to deal with the problem complexity imposed by the length variations of TCRs. We demonstrate that current public bulk CDR3β-pMHC binding data overall is of low quality and that the development of accurate prediction models is contingent on paired α/β TCR sequence data corresponding to at least 150 distinct pairs for each investigated pMHC. In comparison, models trained on CDR3α or CDR3β data alone demonstrated a variable and pMHC specific relative performance drop. Together these findings support that T-cell specificity is predictable given the availability of accurate and sufficient paired TCR sequence data. NetTCR-2.0 is publicly available at https://services.healthtech.dtu.dk/service.php?NetTCR-2.0 .
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Affiliation(s)
- Alessandro Montemurro
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs, Lyngby, Denmark
| | - Viktoria Schuster
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs, Lyngby, Denmark
| | - Helle Rus Povlsen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs, Lyngby, Denmark
| | - Amalie Kai Bentzen
- Department of Health Technology, Section for Experimental and Translational Immunology, Technical University of Denmark, DTU, 2800 Kgs, Lyngby, Denmark
| | - Vanessa Jurtz
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs, Lyngby, Denmark
| | - William D Chronister
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, 92037, USA
| | - Austin Crinklaw
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, 92037, USA
| | - Sine R Hadrup
- Department of Health Technology, Section for Experimental and Translational Immunology, Technical University of Denmark, DTU, 2800 Kgs, Lyngby, Denmark
| | - Ole Winther
- Department of Biology, Bioinformatics Centre, University of Copenhagen, 2200, Copenhagen, Denmark
- Section for Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs., Lyngby, 2800, Denmark
- Centre for Genomic Medicine, Rigshospitalet, Copenhagen University Hospital, København, Ø 2100, Denmark
| | - Bjoern Peters
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, 92037, USA
- Department of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego, La Jolla, CA, 92037, USA
| | - Leon Eyrich Jessen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs, Lyngby, Denmark
| | - Morten Nielsen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs, Lyngby, Denmark.
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina.
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2
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Dhanda SK, Mahajan S, Paul S, Yan Z, Kim H, Jespersen MC, Jurtz V, Andreatta M, Greenbaum JA, Marcatili P, Sette A, Nielsen M, Peters B. IEDB-AR: immune epitope database-analysis resource in 2019. Nucleic Acids Res 2020; 47:W502-W506. [PMID: 31114900 PMCID: PMC6602498 DOI: 10.1093/nar/gkz452] [Citation(s) in RCA: 202] [Impact Index Per Article: 50.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 05/01/2019] [Accepted: 05/10/2019] [Indexed: 11/13/2022] Open
Abstract
The Immune Epitope Database Analysis Resource (IEDB-AR, http://tools.iedb.org/) is a companion website to the IEDB that provides computational tools focused on the prediction and analysis of B and T cell epitopes. All of the tools are freely available through the public website and many are also available through a REST API and/or a downloadable command-line tool. A virtual machine image of the entire site is also freely available for non-commercial use and contains most of the tools on the public site. Here, we describe the tools and functionalities that are available in the IEDB-AR, focusing on the 10 new tools that have been added since the last report in the 2012 NAR webserver edition. In addition, many of the tools that were already hosted on the site in 2012 have received updates to newest versions, including NetMHC, NetMHCpan, BepiPred and DiscoTope. Overall, this IEDB-AR update provides a substantial set of updated and novel features for epitope prediction and analysis.
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Affiliation(s)
- Sandeep Kumar Dhanda
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Swapnil Mahajan
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Sinu Paul
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Zhen Yan
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Haeuk Kim
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | | | - Vanessa Jurtz
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Massimo Andreatta
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Argentina
| | - Jason A Greenbaum
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Paolo Marcatili
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Alessandro Sette
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA.,Department of Medicine, University of California, San Diego, CA 92122, USA
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Argentina
| | - Bjoern Peters
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA.,Department of Medicine, University of California, San Diego, CA 92122, USA
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3
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Jensen KK, Rantos V, Jappe EC, Olsen TH, Jespersen MC, Jurtz V, Jessen LE, Lanzarotti E, Mahajan S, Peters B, Nielsen M, Marcatili P. TCRpMHCmodels: Structural modelling of TCR-pMHC class I complexes. Sci Rep 2019; 9:14530. [PMID: 31601838 PMCID: PMC6787230 DOI: 10.1038/s41598-019-50932-4] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Accepted: 09/09/2019] [Indexed: 01/30/2023] Open
Abstract
The interaction between the class I major histocompatibility complex (MHC), the peptide presented by the MHC and the T-cell receptor (TCR) is a key determinant of the cellular immune response. Here, we present TCRpMHCmodels, a method for accurate structural modelling of the TCR-peptide-MHC (TCR-pMHC) complex. This TCR-pMHC modelling pipeline takes as input the amino acid sequence and generates models of the TCR-pMHC complex, with a median Cα RMSD of 2.31 Å. TCRpMHCmodels significantly outperforms TCRFlexDock, a specialised method for docking pMHC and TCR structures. TCRpMHCmodels is simple to use and the modelling pipeline takes, on average, only two minutes. Thanks to its ease of use and high modelling accuracy, we expect TCRpMHCmodels to provide insights into the underlying mechanisms of TCR and pMHC interactions and aid in the development of advanced T-cell-based immunotherapies and rational design of vaccines. The TCRpMHCmodels tool is available at http://www.cbs.dtu.dk/services/TCRpMHCmodels/.
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Affiliation(s)
| | - Vasileios Rantos
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark.,Centre for Structural Systems Biology (CSSB), DESY and European Molecular Biology Laboratory, Notkestrasse 85, 22607, Hamburg, Germany
| | - Emma Christine Jappe
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark.,Evaxion Biotech, Bredgade 34E, 1260, Copenhagen, Denmark
| | - Tobias Hegelund Olsen
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark
| | | | - Vanessa Jurtz
- Department of Bioinformatics and Data Mining, Novo Nordisk A/S, 2760, Måløv, Denmark
| | - Leon Eyrich Jessen
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Esteban Lanzarotti
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Swapnil Mahajan
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Bjoern Peters
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA.,University of California San Diego, Department of Medicine, La Jolla, CA 92037, USA
| | - Morten Nielsen
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Paolo Marcatili
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark.
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Fenoy E, Izarzugaza JMG, Jurtz V, Brunak S, Nielsen M. A generic deep convolutional neural network framework for prediction of receptor–ligand interactions—NetPhosPan: application to kinase phosphorylation prediction. Bioinformatics 2018; 35:1098-1107. [DOI: 10.1093/bioinformatics/bty715] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Revised: 08/09/2018] [Accepted: 08/16/2018] [Indexed: 11/15/2022] Open
Affiliation(s)
- Emilio Fenoy
- Laboratorio de Bioinformatica, Instituto de Investigaciones Biotecnologicas, Universidad Nacional de San Martin, San Martin, B, HMP Buenos Aires, Argentina
| | - Jose M G Izarzugaza
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Vanessa Jurtz
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Søren Brunak
- Translational Disease Systems Biology Group, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Morten Nielsen
- Laboratorio de Bioinformatica, Instituto de Investigaciones Biotecnologicas, Universidad Nacional de San Martin, San Martin, B, HMP Buenos Aires, Argentina
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
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5
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Paul S, Karosiene E, Dhanda SK, Jurtz V, Edwards L, Nielsen M, Sette A, Peters B. Determination of a Predictive Cleavage Motif for Eluted Major Histocompatibility Complex Class II Ligands. Front Immunol 2018; 9:1795. [PMID: 30127785 PMCID: PMC6087742 DOI: 10.3389/fimmu.2018.01795] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Accepted: 07/20/2018] [Indexed: 01/13/2023] Open
Abstract
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.
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Affiliation(s)
- Sinu Paul
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States
| | - Edita Karosiene
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States
| | - Sandeep Kumar Dhanda
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States
| | - Vanessa Jurtz
- Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark
| | - Lindy Edwards
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States
| | - Morten Nielsen
- Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Argentina
| | - Alessandro Sette
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States.,Department of Medicine, University of California San Diego, La Jolla, CA, United States
| | - Bjoern Peters
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, United States.,Department of Medicine, University of California San Diego, La Jolla, CA, United States
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6
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Bjerregaard AM, Nielsen M, Jurtz V, Barra CM, Hadrup SR, Szallasi Z, Eklund AC. Corrigendum: An Analysis of Natural T Cell Responses to Predicted Tumor Neoepitopes. Front Immunol 2018; 9:1007. [PMID: 29795801 PMCID: PMC5961322 DOI: 10.3389/fimmu.2018.01007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Accepted: 04/23/2018] [Indexed: 11/13/2022] Open
Abstract
[This corrects the article on p. 1566 in vol. 8, PMID: 29187854.].
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Affiliation(s)
- Anne-Mette Bjerregaard
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Morten Nielsen
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Vanessa Jurtz
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Carolina M Barra
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Sine Reker Hadrup
- Section for Immunology and Vaccinology, National Veterinary Institute, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Zoltan Szallasi
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark.,Computational Health Informatics Program (CHIP), Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
| | - Aron Charles Eklund
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
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7
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Bjerregaard AM, Nielsen M, Jurtz V, Barra CM, Hadrup SR, Szallasi Z, Eklund AC. An Analysis of Natural T Cell Responses to Predicted Tumor Neoepitopes. Front Immunol 2017; 8:1566. [PMID: 29187854 PMCID: PMC5694748 DOI: 10.3389/fimmu.2017.01566] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Accepted: 11/01/2017] [Indexed: 12/30/2022] Open
Abstract
Personalization of cancer immunotherapies such as therapeutic vaccines and adoptive T-cell therapy may benefit from efficient identification and targeting of patient-specific neoepitopes. However, current neoepitope prediction methods based on sequencing and predictions of epitope processing and presentation result in a low rate of validation, suggesting that the determinants of peptide immunogenicity are not well understood. We gathered published data on human neopeptides originating from single amino acid substitutions for which T cell reactivity had been experimentally tested, including both immunogenic and non-immunogenic neopeptides. Out of 1,948 neopeptide-HLA (human leukocyte antigen) combinations from 13 publications, 53 were reported to elicit a T cell response. From these data, we found an enrichment for responses among peptides of length 9. Even though the peptides had been pre-selected based on presumed likelihood of being immunogenic, we found using NetMHCpan-4.0 that immunogenic neopeptides were predicted to bind significantly more strongly to HLA compared to non-immunogenic peptides. Investigation of the HLA binding strength of the immunogenic peptides revealed that the vast majority (96%) shared very strong predicted binding to HLA and that the binding strength was comparable to that observed for pathogen-derived epitopes. Finally, we found that neopeptide dissimilarity to self is a predictor of immunogenicity in situations where neo- and normal peptides share comparable predicted binding strength. In conclusion, these results suggest new strategies for prioritization of mutated peptides, but new data will be needed to confirm their value.
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Affiliation(s)
- Anne-Mette Bjerregaard
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Morten Nielsen
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Vanessa Jurtz
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Carolina M Barra
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Sine Reker Hadrup
- Section for Immunology and Vaccinology, National Veterinary Institute, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Zoltan Szallasi
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark.,Computational Health Informatics Program (CHIP), Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
| | - Aron Charles Eklund
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
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8
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Jurtz V, Paul S, Andreatta M, Marcatili P, Peters B, Nielsen M. NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data. J Immunol 2017; 199:3360-3368. [PMID: 28978689 DOI: 10.4049/jimmunol.1700893] [Citation(s) in RCA: 847] [Impact Index Per Article: 121.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Accepted: 09/06/2017] [Indexed: 12/12/2022]
Abstract
Cytotoxic T cells are of central importance in the immune system's response to disease. They recognize defective cells by binding to peptides presented on the cell surface by MHC class I molecules. Peptide binding to MHC molecules is the single most selective step in the Ag-presentation pathway. Therefore, in the quest for T cell epitopes, the prediction of peptide binding to MHC molecules has attracted widespread attention. In the past, predictors of peptide-MHC interactions have primarily been trained on binding affinity data. Recently, an increasing number of MHC-presented peptides identified by mass spectrometry have been reported containing information about peptide-processing steps in the presentation pathway and the length distribution of naturally presented peptides. In this article, we present NetMHCpan-4.0, a method trained on binding affinity and eluted ligand data leveraging the information from both data types. Large-scale benchmarking of the method demonstrates an increase in predictive performance compared with state-of-the-art methods when it comes to identification of naturally processed ligands, cancer neoantigens, and T cell epitopes.
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Affiliation(s)
- Vanessa Jurtz
- Department of Bio and Health Informatics, Technical University of Denmark, DK-2800 Lyngby, Denmark
| | - Sinu Paul
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037; and
| | - Massimo Andreatta
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, CP1650 San Martín, Argentina
| | - Paolo Marcatili
- Department of Bio and Health Informatics, Technical University of Denmark, DK-2800 Lyngby, Denmark
| | - Bjoern Peters
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037; and
| | - Morten Nielsen
- Department of Bio and Health Informatics, Technical University of Denmark, DK-2800 Lyngby, Denmark; .,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, CP1650 San Martín, Argentina
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9
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Thomsen MCF, Ahrenfeldt J, Cisneros JLB, Jurtz V, Larsen MV, Hasman H, Aarestrup FM, Lund O. A Bacterial Analysis Platform: An Integrated System for Analysing Bacterial Whole Genome Sequencing Data for Clinical Diagnostics and Surveillance. PLoS One 2016; 11:e0157718. [PMID: 27327771 PMCID: PMC4915688 DOI: 10.1371/journal.pone.0157718] [Citation(s) in RCA: 119] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Accepted: 06/05/2016] [Indexed: 11/18/2022] Open
Abstract
Recent advances in whole genome sequencing have made the technology available for routine use in microbiological laboratories. However, a major obstacle for using this technology is the availability of simple and automatic bioinformatics tools. Based on previously published and already available web-based tools we developed a single pipeline for batch uploading of whole genome sequencing data from multiple bacterial isolates. The pipeline will automatically identify the bacterial species and, if applicable, assemble the genome, identify the multilocus sequence type, plasmids, virulence genes and antimicrobial resistance genes. A short printable report for each sample will be provided and an Excel spreadsheet containing all the metadata and a summary of the results for all submitted samples can be downloaded. The pipeline was benchmarked using datasets previously used to test the individual services. The reported results enable a rapid overview of the major results, and comparing that to the previously found results showed that the platform is reliable and able to correctly predict the species and find most of the expected genes automatically. In conclusion, a combined bioinformatics platform was developed and made publicly available, providing easy-to-use automated analysis of bacterial whole genome sequencing data. The platform may be of immediate relevance as a guide for investigators using whole genome sequencing for clinical diagnostics and surveillance. The platform is freely available at: https://cge.cbs.dtu.dk/services/CGEpipeline-1.1 and it is the intention that it will continue to be expanded with new features as these become available.
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Affiliation(s)
| | - Johanne Ahrenfeldt
- Department of Systems Biology, Technical University of Denmark, Kemitorvet Building 208, 2800 Kgs. Lyngby, Denmark
| | - Jose Luis Bellod Cisneros
- Department of Systems Biology, Technical University of Denmark, Kemitorvet Building 208, 2800 Kgs. Lyngby, Denmark
| | - Vanessa Jurtz
- Department of Systems Biology, Technical University of Denmark, Kemitorvet Building 208, 2800 Kgs. Lyngby, Denmark
| | - Mette Voldby Larsen
- Department of Systems Biology, Technical University of Denmark, Kemitorvet Building 208, 2800 Kgs. Lyngby, Denmark
| | - Henrik Hasman
- National Food Institute, Technical University of Denmark, Søltofts Plads Building 221, 2800 Kgs. Lyngby, Denmark
| | - Frank Møller Aarestrup
- National Food Institute, Technical University of Denmark, Søltofts Plads Building 221, 2800 Kgs. Lyngby, Denmark
| | - Ole Lund
- Department of Systems Biology, Technical University of Denmark, Kemitorvet Building 208, 2800 Kgs. Lyngby, Denmark
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