SILVI, an open-source pipeline for T-cell epitope selection

High-throughput screening of available genomic data and identification of potential antigenic candidates have promoted the development of epitope-based vaccines and therapeutics. Several immunoinformatic tools are available to predict potential epitopes and other immunogenicity-related features, yet...

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Veröffentlicht in:	PloS one Jg. 17; H. 9; S. e0273494
Hauptverfasser:	Pissarra, Joana, Dorkeld, Franck, Loire, Etienne, Bonhomme, Vincent, Sereno, Denis, Lemesre, Jean-Loup, Holzmuller, Philippe
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	San Francisco Public Library of Science 07.09.2022 Public Library of Science (PLoS)
Schlagworte:	Algorithms Analysis Antigenic determinants Antigens Binding Biochemistry, Molecular Biology Bioinformatics Biology and Life Sciences Chemical properties Computer Science Epitopes Evaluation Fluid filters Genomes Genomics Health aspects Hepatitis C High-throughput screening Histocompatibility antigen HLA Human papillomavirus Hydrophobicity Immunogenicity Immunotherapy Leukocytes Life Sciences Localization Lymphocytes T Medicine and Health Sciences Microorganisms Modeling and Simulation Pathogens Peptides Predictions Proteins Proteomes Public software Research and Analysis Methods T cells Tuberculosis Vaccines Workflow France
ISSN:	1932-6203, 1932-6203
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Abstract	High-throughput screening of available genomic data and identification of potential antigenic candidates have promoted the development of epitope-based vaccines and therapeutics. Several immunoinformatic tools are available to predict potential epitopes and other immunogenicity-related features, yet it is still challenging and time-consuming to compare and integrate results from different algorithms. We developed the R script SILVI (short for: from in silico to in vivo ), to assist in the selection of the potentially most immunogenic T-cell epitopes from Human Leukocyte Antigen (HLA)-binding prediction data. SILVI merges and compares data from available HLA-binding prediction servers, and integrates additional relevant information of predicted epitopes, namely BLASTp alignments with host proteins and physical-chemical properties. The two default criteria applied by SILVI and additional filtering allow the fast selection of the most conserved, promiscuous, strong binding T-cell epitopes. Users may adapt the script at their discretion as it is written in open-source R language. To demonstrate the workflow and present selection options, SILVI was used to integrate HLA-binding prediction results of three example proteins, from viral, bacterial and parasitic microorganisms, containing validated epitopes included in the Immune Epitope Database (IEDB), plus the Human Papillomavirus (HPV) proteome. Applying different filters on predicted IC50, hydrophobicity and mismatches with host proteins allows to significantly reduce the epitope lists with favourable sensitivity and specificity to select immunogenic epitopes. We contemplate SILVI will assist T-cell epitope selections and can be continuously refined in a community-driven manner, helping the improvement and design of peptide-based vaccines or immunotherapies. SILVI development version is available at: github.com/JoanaPissarra/SILVI2020 and https://doi.org/10.5281/zenodo.6865909 .
AbstractList	High-throughput screening of available genomic data and identification of potential antigenic candidates have promoted the development of epitope-based vaccines and therapeutics. Several immunoinformatic tools are available to predict potential epitopes and other immunogenicity-related features, yet it is still challenging and time-consuming to compare and integrate results from different algorithms. We developed the R script SILVI (short for: from in silico to in vivo), to assist in the selection of the potentially most immunogenic T-cell epitopes from Human Leukocyte Antigen (HLA)-binding prediction data. SILVI merges and compares data from available HLA-binding prediction servers, and integrates additional relevant information of predicted epitopes, namely BLASTp alignments with host proteins and physical-chemical properties. The two default criteria applied by SILVI and additional filtering allow the fast selection of the most conserved, promiscuous, strong binding T-cell epitopes. Users may adapt the script at their discretion as it is written in open-source R language. To demonstrate the workflow and present selection options, SILVI was used to integrate HLA-binding prediction results of three example proteins, from viral, bacterial and parasitic microorganisms, containing validated epitopes included in the Immune Epitope Database (IEDB), plus the Human Papillomavirus (HPV) proteome. Applying different filters on predicted IC50, hydrophobicity and mismatches with host proteins allows to significantly reduce the epitope lists with favourable sensitivity and specificity to select immunogenic epitopes. We contemplate SILVI will assist T-cell epitope selections and can be continuously refined in a community-driven manner, helping the improvement and design of peptide-based vaccines or immunotherapies. SILVI development version is available at: github.com/JoanaPissarra/SILVI2020 and High-throughput screening of available genomic data and identification of potential antigenic candidates have promoted the development of epitope-based vaccines and therapeutics. Several immunoinformatic tools are available to predict potential epitopes and other immunogenicity-related features, yet it is still challenging and time-consuming to compare and integrate results from different algorithms. We developed the R script SILVI (short for: from in silico to in vivo), to assist in the selection of the potentially most immunogenic T-cell epitopes from Human Leukocyte Antigen (HLA)-binding prediction data. SILVI merges and compares data from available HLA-binding prediction servers, and integrates additional relevant information of predicted epitopes, namely BLASTp alignments with host proteins and physical-chemical properties. The two default criteria applied by SILVI and additional filtering allow the fast selection of the most conserved, promiscuous, strong binding T-cell epitopes. Users may adapt the script at their discretion as it is written in open-source R language. To demonstrate the workflow and present selection options, SILVI was used to integrate HLA-binding prediction results of three example proteins, from viral, bacterial and parasitic microorganisms, containing validated epitopes included in the Immune Epitope Database (IEDB), plus the Human Papillomavirus (HPV) proteome. Applying different filters on predicted IC50, hydrophobicity and mismatches with host proteins allows to significantly reduce the epitope lists with favourable sensitivity and specificity to select immunogenic epitopes. We contemplate SILVI will assist T-cell epitope selections and can be continuously refined in a community-driven manner, helping the improvement and design of peptide-based vaccines or immunotherapies. SILVI development version is available at: github.com/JoanaPissarra/SILVI2020 and https://doi.org/10.5281/zenodo.6865909. High-throughput screening of available genomic data and identification of potential antigenic candidates have promoted the development of epitope-based vaccines and therapeutics. Several immunoinformatic tools are available to predict potential epitopes and other immunogenicity-related features, yet it is still challenging and time-consuming to compare and integrate results from different algorithms. We developed the R script SILVI (short for: from in silico to in vivo ), to assist in the selection of the potentially most immunogenic T-cell epitopes from Human Leukocyte Antigen (HLA)-binding prediction data. SILVI merges and compares data from available HLA-binding prediction servers, and integrates additional relevant information of predicted epitopes, namely BLASTp alignments with host proteins and physical-chemical properties. The two default criteria applied by SILVI and additional filtering allow the fast selection of the most conserved, promiscuous, strong binding T-cell epitopes. Users may adapt the script at their discretion as it is written in open-source R language. To demonstrate the workflow and present selection options, SILVI was used to integrate HLA-binding prediction results of three example proteins, from viral, bacterial and parasitic microorganisms, containing validated epitopes included in the Immune Epitope Database (IEDB), plus the Human Papillomavirus (HPV) proteome. Applying different filters on predicted IC50, hydrophobicity and mismatches with host proteins allows to significantly reduce the epitope lists with favourable sensitivity and specificity to select immunogenic epitopes. We contemplate SILVI will assist T-cell epitope selections and can be continuously refined in a community-driven manner, helping the improvement and design of peptide-based vaccines or immunotherapies. SILVI development version is available at: https://github.com/JoanaPissarra/SILVI2020 and https://doi.org/10.5281/zenodo.6865909 . High-throughput screening of available genomic data and identification of potential antigenic candidates have promoted the development of epitope-based vaccines and therapeutics. Several immunoinformatic tools are available to predict potential epitopes and other immunogenicity-related features, yet it is still challenging and time-consuming to compare and integrate results from different algorithms. We developed the R script SILVI (short for: from in silico to in vivo ), to assist in the selection of the potentially most immunogenic T-cell epitopes from Human Leukocyte Antigen (HLA)-binding prediction data. SILVI merges and compares data from available HLA-binding prediction servers, and integrates additional relevant information of predicted epitopes, namely BLASTp alignments with host proteins and physical-chemical properties. The two default criteria applied by SILVI and additional filtering allow the fast selection of the most conserved, promiscuous, strong binding T-cell epitopes. Users may adapt the script at their discretion as it is written in open-source R language. To demonstrate the workflow and present selection options, SILVI was used to integrate HLA-binding prediction results of three example proteins, from viral, bacterial and parasitic microorganisms, containing validated epitopes included in the Immune Epitope Database (IEDB), plus the Human Papillomavirus (HPV) proteome. Applying different filters on predicted IC50, hydrophobicity and mismatches with host proteins allows to significantly reduce the epitope lists with favourable sensitivity and specificity to select immunogenic epitopes. We contemplate SILVI will assist T-cell epitope selections and can be continuously refined in a community-driven manner, helping the improvement and design of peptide-based vaccines or immunotherapies. SILVI development version is available at: github.com/JoanaPissarra/SILVI2020 and https://doi.org/10.5281/zenodo.6865909 . High-throughput screening of available genomic data and identification of potential antigenic candidates have promoted the development of epitope-based vaccines and therapeutics. Several immunoinformatic tools are available to predict potential epitopes and other immunogenicity-related features, yet it is still challenging and time-consuming to compare and integrate results from different algorithms. We developed the R script SILVI (short for: from in silico to in vivo), to assist in the selection of the potentially most immunogenic T-cell epitopes from Human Leukocyte Antigen (HLA)-binding prediction data. SILVI merges and compares data from available HLA-binding prediction servers, and integrates additional relevant information of predicted epitopes, namely BLASTp alignments with host proteins and physical-chemical properties. The two default criteria applied by SILVI and additional filtering allow the fast selection of the most conserved, promiscuous, strong binding T-cell epitopes. Users may adapt the script at their discretion as it is written in open-source R language. To demonstrate the workflow and present selection options, SILVI was used to integrate HLA-binding prediction results of three example proteins, from viral, bacterial and parasitic microorganisms, containing validated epitopes included in the Immune Epitope Database (IEDB), plus the Human Papillomavirus (HPV) proteome. Applying different filters on predicted IC50, hydrophobicity and mismatches with host proteins allows to significantly reduce the epitope lists with favourable sensitivity and specificity to select immunogenic epitopes. We contemplate SILVI will assist T-cell epitope selections and can be continuously refined in a community-driven manner, helping the improvement and design of peptide-based vaccines or immunotherapies. SILVI development version is available at: github.com/JoanaPissarra/SILVI2020 and https://doi.org/10.5281/zenodo.6865909.High-throughput screening of available genomic data and identification of potential antigenic candidates have promoted the development of epitope-based vaccines and therapeutics. Several immunoinformatic tools are available to predict potential epitopes and other immunogenicity-related features, yet it is still challenging and time-consuming to compare and integrate results from different algorithms. We developed the R script SILVI (short for: from in silico to in vivo), to assist in the selection of the potentially most immunogenic T-cell epitopes from Human Leukocyte Antigen (HLA)-binding prediction data. SILVI merges and compares data from available HLA-binding prediction servers, and integrates additional relevant information of predicted epitopes, namely BLASTp alignments with host proteins and physical-chemical properties. The two default criteria applied by SILVI and additional filtering allow the fast selection of the most conserved, promiscuous, strong binding T-cell epitopes. Users may adapt the script at their discretion as it is written in open-source R language. To demonstrate the workflow and present selection options, SILVI was used to integrate HLA-binding prediction results of three example proteins, from viral, bacterial and parasitic microorganisms, containing validated epitopes included in the Immune Epitope Database (IEDB), plus the Human Papillomavirus (HPV) proteome. Applying different filters on predicted IC50, hydrophobicity and mismatches with host proteins allows to significantly reduce the epitope lists with favourable sensitivity and specificity to select immunogenic epitopes. We contemplate SILVI will assist T-cell epitope selections and can be continuously refined in a community-driven manner, helping the improvement and design of peptide-based vaccines or immunotherapies. SILVI development version is available at: github.com/JoanaPissarra/SILVI2020 and https://doi.org/10.5281/zenodo.6865909.
Audience	Academic
Author	Dorkeld, Franck Bonhomme, Vincent Holzmuller, Philippe Lemesre, Jean-Loup Loire, Etienne Sereno, Denis Pissarra, Joana
AuthorAffiliation	3 UMR ASTRE, CIRAD, INRAE, University of Montpellier (I-MUSE), Montpellier, France 1 UMR INTERTRYP, IRD, CIRAD, University of Montpellier (I-MUSE), Montpellier, France 4 ISEM, CNRS, EPHE, IRD, University of Montpellier (I-MUSE), Montpellier, France National Cancer Institute at Frederick, UNITED STATES 2 UMR CBGP, INRAE, CIRAD, IRD, Montpellier SupAgro, University of Montpellier (I-MUSE), Montpellier, France
AuthorAffiliation_xml	– name: 3 UMR ASTRE, CIRAD, INRAE, University of Montpellier (I-MUSE), Montpellier, France – name: 2 UMR CBGP, INRAE, CIRAD, IRD, Montpellier SupAgro, University of Montpellier (I-MUSE), Montpellier, France – name: 1 UMR INTERTRYP, IRD, CIRAD, University of Montpellier (I-MUSE), Montpellier, France – name: 4 ISEM, CNRS, EPHE, IRD, University of Montpellier (I-MUSE), Montpellier, France – name: National Cancer Institute at Frederick, UNITED STATES
Author_xml	– sequence: 1 givenname: Joana orcidid: 0000-0002-0506-8042 surname: Pissarra fullname: Pissarra, Joana – sequence: 2 givenname: Franck surname: Dorkeld fullname: Dorkeld, Franck – sequence: 3 givenname: Etienne surname: Loire fullname: Loire, Etienne – sequence: 4 givenname: Vincent surname: Bonhomme fullname: Bonhomme, Vincent – sequence: 5 givenname: Denis orcidid: 0000-0002-0034-9291 surname: Sereno fullname: Sereno, Denis – sequence: 6 givenname: Jean-Loup surname: Lemesre fullname: Lemesre, Jean-Loup – sequence: 7 givenname: Philippe surname: Holzmuller fullname: Holzmuller, Philippe
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PublicationDate_xml	– month: 09 year: 2022 text: 2022-09-07 day: 07
PublicationDecade	2020
PublicationPlace	San Francisco
PublicationPlace_xml	– name: San Francisco – name: San Francisco, CA USA
PublicationTitle	PloS one
PublicationYear	2022
Publisher	Public Library of Science Public Library of Science (PLoS)
Publisher_xml	– name: Public Library of Science – name: Public Library of Science (PLoS)
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SubjectTerms	Algorithms Analysis Antigenic determinants Antigens Binding Biochemistry, Molecular Biology Bioinformatics Biology and Life Sciences Chemical properties Computer Science Epitopes Evaluation Fluid filters Genomes Genomics Health aspects Hepatitis C High-throughput screening Histocompatibility antigen HLA Human papillomavirus Hydrophobicity Immunogenicity Immunotherapy Leukocytes Life Sciences Localization Lymphocytes T Medicine and Health Sciences Microorganisms Modeling and Simulation Pathogens Peptides Predictions Proteins Proteomes Public software Research and Analysis Methods T cells Tuberculosis Vaccines Workflow
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Title	SILVI, an open-source pipeline for T-cell epitope selection
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