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{"title"=>"Properties of MHC Class I Presented Peptides That Enhance Immunogenicity", "type"=>"journal", "authors"=>[{"first_name"=>"Jorg J.A.", "last_name"=>"Calis", "scopus_author_id"=>"36503557700"}, {"first_name"=>"Matt", "last_name"=>"Maybeno", "scopus_author_id"=>"35734659300"}, {"first_name"=>"Jason A.", "last_name"=>"Greenbaum", "scopus_author_id"=>"8588416000"}, {"first_name"=>"Daniela", "last_name"=>"Weiskopf", "scopus_author_id"=>"13807211700"}, {"first_name"=>"Aruna D.", "last_name"=>"De Silva", "scopus_author_id"=>"7101605567"}, {"first_name"=>"Alessandro", "last_name"=>"Sette", "scopus_author_id"=>"35418504700"}, {"first_name"=>"Can", "last_name"=>"Keşmir", "scopus_author_id"=>"6701789184"}, {"first_name"=>"Bjoern", "last_name"=>"Peters", "scopus_author_id"=>"8561294600"}], "year"=>2013, "source"=>"PLoS Computational Biology", "identifiers"=>{"scopus"=>"2-s2.0-84887277467", "sgr"=>"84887277467", "issn"=>"1553734X", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "pmid"=>"24204222", "doi"=>"10.1371/journal.pcbi.1003266", "pui"=>"370217852"}, "id"=>"8f9baacc-1188-3ead-93b3-fa1f903b940a", "abstract"=>"T-cells have to recognize peptides presented on MHC molecules to be activated and elicit their effector functions. Several studies demonstrate that some peptides are more immunogenic than others and therefore more likely to be T-cell epitopes. We set out to determine which properties cause such differences in immunogenicity. To this end, we collected and analyzed a large set of data describing the immunogenicity of peptides presented on various MHC-I molecules. Two main conclusions could be drawn from this analysis: First, in line with previous observations, we showed that positions P4-6 of a presented peptide are more important for immunogenicity. Second, some amino acids, especially those with large and aromatic side chains, are associated with immunogenicity. This information was combined into a simple model that was used to demonstrate that immunogenicity is, to a certain extent, predictable. This model (made available at http://tools.iedb.org/immunogenicity/) was validated with data from two independent epitope discovery studies. Interestingly, with this model we could show that T-cells are equipped to better recognize viral than human (self) peptides. After the past successful elucidation of different steps in the MHC-I presentation pathway, the identification of variables that influence immunogenicity will be an important next step in the investigation of T-cell epitopes and our understanding of cellular immune responses.", "link"=>"http://www.mendeley.com/research/properties-mhc-class-i-presented-peptides-enhance-immunogenicity", "reader_count"=>209, "reader_count_by_academic_status"=>{"Unspecified"=>5, "Professor > Associate Professor"=>7, "Student > Doctoral Student"=>14, "Researcher"=>59, "Student > Ph. D. Student"=>44, "Student > Postgraduate"=>6, "Student > Master"=>31, "Other"=>15, "Student > Bachelor"=>21, "Lecturer"=>2, "Professor"=>5}, "reader_count_by_user_role"=>{"Unspecified"=>5, "Professor > Associate Professor"=>7, "Student > Doctoral Student"=>14, "Researcher"=>59, "Student > Ph. D. Student"=>44, "Student > Postgraduate"=>6, "Student > Master"=>31, "Other"=>15, "Student > Bachelor"=>21, "Lecturer"=>2, "Professor"=>5}, "reader_count_by_subject_area"=>{"Unspecified"=>9, "Agricultural and Biological Sciences"=>96, "Veterinary Science and Veterinary Medicine"=>1, "Chemistry"=>8, "Computer Science"=>8, "Engineering"=>4, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>40, "Mathematics"=>1, "Medicine and Dentistry"=>20, "Pharmacology, Toxicology and Pharmaceutical Science"=>2, "Physics and Astronomy"=>4, "Immunology and Microbiology"=>15}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>20}, "Physics and Astronomy"=>{"Physics and Astronomy"=>4}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>9}, "Environmental Science"=>{"Environmental Science"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>2}, "Engineering"=>{"Engineering"=>4}, "Chemistry"=>{"Chemistry"=>8}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>15}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>96}, "Computer Science"=>{"Computer Science"=>8}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>40}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>1}}, "reader_count_by_country"=>{"Colombia"=>1, "Argentina"=>1, "United States"=>9, "United Kingdom"=>3, "Portugal"=>1, "India"=>1, "Russia"=>1, "Saudi Arabia"=>1, "Canada"=>2, "Belgium"=>1, "Denmark"=>3, "Brazil"=>1, "Mexico"=>1, "Australia"=>1, "Germany"=>2}, "group_count"=>19}

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• {"month"=>"10", "year"=>"2019", "pdf_views"=>"42", "xml_views"=>"0", "html_views"=>"85"}
• {"month"=>"11", "year"=>"2019", "pdf_views"=>"35", "xml_views"=>"0", "html_views"=>"79"}
• {"month"=>"12", "year"=>"2019", "pdf_views"=>"9", "xml_views"=>"0", "html_views"=>"13"}

Figshare

• {"files"=>["https://ndownloader.figshare.com/files/1256997"], "description"=>"<p>The fraction of an amino acid in immunogenic (left bar, filled) and non-immunogenic (right bar, unfilled) peptides presented on HLA class I molecules is shown. Significantly different distributions are indicated with a star (Permutation test, see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003266#s4\" target=\"_blank\">Methods</a>: p<0.05; False discovery rate (FDR) for multiple testing determined as in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003266#pcbi.1003266-Storey1\" target=\"_blank\">[46]</a>: q<0.05). The background frequency for each amino acid in the protein sequences that were a source of the immunogenic or non-immunogenic peptides is shown by a grey line.</p>", "links"=>[], "tags"=>["preferences", "amino", "acids", "hla", "presented"], "article_id"=>831661, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Jorg J. A. Calis", "Matt Maybeno", "Jason A. Greenbaum", "Daniela Weiskopf", "Aruna D. De Silva", "Alessandro Sette", "Can Keşmir", "Bjoern Peters"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003266.g002", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_T_cell_preferences_for_different_amino_acids_in_HLA_class_I_presented_peptides_/831661", "title"=>"T-cell preferences for different amino acids in HLA class I presented peptides.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-24 02:56:37"}
• {"files"=>["https://ndownloader.figshare.com/files/1257005"], "description"=>"<p>For peptides presented on HLA class I molecules in HLA transgenic mice that were either known to be immunogenic or non-immunogenic (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003266#s4\" target=\"_blank\">Methods</a>), amino acids were counted per position. The 20 counts for immunogenic and non-immunogenic pMHCs were compared per position using the Kullback-Leibler divergence. A Fisher's test (<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003266#s4\" target=\"_blank\">Methods</a>) was done to determine if the distributions were significantly different (* p<0.05; **p<0.01).</p><p>†Position 1 is not an anchor in every HLA molecule, nonetheless it is an anchor in most pMHCs wherefore the divergence cannot be estimated at this position.</p>", "links"=>[], "tags"=>["differences", "immunogenic", "non-immunogenic"], "article_id"=>831669, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Jorg J. A. Calis", "Matt Maybeno", "Jason A. Greenbaum", "Daniela Weiskopf", "Aruna D. De Silva", "Alessandro Sette", "Can Keşmir", "Bjoern Peters"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003266.t002", "stats"=>{"downloads"=>4, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Position_dependent_differences_between_immunogenic_and_non_immunogenic_peptides_/831669", "title"=>"Position dependent differences between immunogenic and non-immunogenic peptides.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-10-24 02:56:37"}
• {"files"=>["https://ndownloader.figshare.com/files/1257004"], "description"=>"<p>For common HLA molecules (13 HLA-A and 15 HLA-B), viral and human ligands were predicted using MHC binding and precursor protein processing predictors (as in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003266#pcbi.1003266-Calis1\" target=\"_blank\">[40]</a>). The fraction of viral pMHCs (y-axis) and human pMHCs (x-axis) with a positive score in our immunogenicity model is shown. The diagonal denotes the line y = x, HLA molecules with a larger fraction of positively scoring viral pMHCs fall above this line, which was the case for 27 of the 28 HLA molecules (sign-test: p<0.001). Three HLA molecules where the difference between the (predicted) viral and human ligands was largest (B*0702, A*3001, B*4501), and one HLA molecule where the difference between viral and human ligands was smallest (A*2301), and HLA-A*0201 are indicated in the figure.</p>", "links"=>[], "tags"=>["pmhcs", "recognized"], "article_id"=>831668, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Jorg J. A. Calis", "Matt Maybeno", "Jason A. Greenbaum", "Daniela Weiskopf", "Aruna D. De Silva", "Alessandro Sette", "Can Keşmir", "Bjoern Peters"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003266.g005", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Viral_pMHCs_are_better_recognized_by_T_cells_/831668", "title"=>"Viral pMHCs are better recognized by T-cells.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-24 02:56:37"}
• {"files"=>["https://ndownloader.figshare.com/files/1256998"], "description"=>"<p>Two-thirds of the data were used for making the immunogenicity model (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003266#s4\" target=\"_blank\">methods</a>) and one-third for cross-validation. The average ROC (thick grey line) of 25 of such cross-validations (thin lines) are plotted. The average AUC was 0.65.</p>", "links"=>[], "tags"=>["immunogenicity"], "article_id"=>831662, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Jorg J. A. Calis", "Matt Maybeno", "Jason A. Greenbaum", "Daniela Weiskopf", "Aruna D. De Silva", "Alessandro Sette", "Can Keşmir", "Bjoern Peters"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003266.g003", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cross_validation_of_the_immunogenicity_model_/831662", "title"=>"Cross-validation of the immunogenicity model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-24 02:56:37"}
• {"files"=>["https://ndownloader.figshare.com/files/1256995"], "description"=>"<p>Data was collected from four different sources (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003266#s4\" target=\"_blank\">Methods</a>). The first panel shows how many pMHCs were derived from each data set and their respective MHC restrictions and immunogenicity status. Data from all sets was combined, the number of non-redundant 9mers with respect to the host in which the data was obtained is shown in the second panel.</p>", "links"=>[], "tags"=>["handling"], "article_id"=>831659, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Jorg J. A. Calis", "Matt Maybeno", "Jason A. Greenbaum", "Daniela Weiskopf", "Aruna D. De Silva", "Alessandro Sette", "Can Keşmir", "Bjoern Peters"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003266.g001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Data_acquisition_and_handling_oversight_/831659", "title"=>"Data acquisition and handling oversight.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-24 02:56:37"}
• {"files"=>["https://ndownloader.figshare.com/files/1257007", "https://ndownloader.figshare.com/files/1257008", "https://ndownloader.figshare.com/files/1257009", "https://ndownloader.figshare.com/files/1257010", "https://ndownloader.figshare.com/files/1257011", "https://ndownloader.figshare.com/files/1257012"], "description"=>"<div><p>T-cells have to recognize peptides presented on MHC molecules to be activated and elicit their effector functions. Several studies demonstrate that some peptides are more immunogenic than others and therefore more likely to be T-cell epitopes. We set out to determine which properties cause such differences in immunogenicity. To this end, we collected and analyzed a large set of data describing the immunogenicity of peptides presented on various MHC-I molecules. Two main conclusions could be drawn from this analysis: First, in line with previous observations, we showed that positions P4–6 of a presented peptide are more important for immunogenicity. Second, some amino acids, especially those with large and aromatic side chains, are associated with immunogenicity. This information was combined into a simple model that was used to demonstrate that immunogenicity is, to a certain extent, predictable. This model (made available at <a href=\"http://tools.iedb.org/immunogenicity/\" target=\"_blank\">http://tools.iedb.org/immunogenicity/</a>) was validated with data from two independent epitope discovery studies. Interestingly, with this model we could show that T-cells are equipped to better recognize viral than human (self) peptides. After the past successful elucidation of different steps in the MHC-I presentation pathway, the identification of variables that influence immunogenicity will be an important next step in the investigation of T-cell epitopes and our understanding of cellular immune responses.</p></div>", "links"=>[], "tags"=>["mhc", "presented", "peptides"], "article_id"=>831671, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Jorg J. A. Calis", "Matt Maybeno", "Jason A. Greenbaum", "Daniela Weiskopf", "Aruna D. De Silva", "Alessandro Sette", "Can Keşmir", "Bjoern Peters"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003266.s001", "https://dx.doi.org/10.1371/journal.pcbi.1003266.s002", "https://dx.doi.org/10.1371/journal.pcbi.1003266.s003", "https://dx.doi.org/10.1371/journal.pcbi.1003266.s004", "https://dx.doi.org/10.1371/journal.pcbi.1003266.s005", "https://dx.doi.org/10.1371/journal.pcbi.1003266.s006"], "stats"=>{"downloads"=>10, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Properties_of_MHC_Class_I_Presented_Peptides_That_Enhance_Immunogenicity_/831671", "title"=>"Properties of MHC Class I Presented Peptides That Enhance Immunogenicity", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-10-24 02:56:37"}
• {"files"=>["https://ndownloader.figshare.com/files/1257006"], "description"=>"<p>Sets of amino acids were counted in immunogenic and non-immunogenic peptides based on size, aromaticity, acidity and charge, and enrichments were determined (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003266#s4\" target=\"_blank\">Methods</a>). The association of these characteristics (e.g. size) with immunogenicity was tested by comparing the distributions in one extreme of a characteristic with the distribution in the other extreme of that characteristic (e.g. large versus small) using Fisher's exact test. This way, one test is performed per characteristic.</p>", "links"=>[], "tags"=>["immunogenic", "peptides", "presented", "hla"], "article_id"=>831670, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Jorg J. A. Calis", "Matt Maybeno", "Jason A. Greenbaum", "Daniela Weiskopf", "Aruna D. De Silva", "Alessandro Sette", "Can Keşmir", "Bjoern Peters"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003266.t001", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Amino_acid_characteristics_of_immunogenic_peptides_presented_on_HLA_class_I_molecules_/831670", "title"=>"Amino acid characteristics of immunogenic peptides presented on HLA class I molecules.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-10-24 02:56:37"}
• {"files"=>["https://ndownloader.figshare.com/files/1257002"], "description"=>"<p>Immunogenicity scores were determined for non-redundant epitopes (n = 22) and non-epitopes (n = 110) identified in mice by Weiskopf et al. <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003266#pcbi.1003266-Weiskopf1\" target=\"_blank\">[55]</a> (A), and for non-redundant epitopes (n = 42) and non-epitopes (n = 477) identified by Weiskopf et al. in humans <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003266#pcbi.1003266-Weiskopf2\" target=\"_blank\">[56]</a> (B). Average and variation of the average are shown as thick lines with error bars, individual scores are shown as dots. Both in mice and in men, the epitopes had a significantly higher immunogenicity score than the non-epitopes (Murine data (A): p<0.01 (Wilcoxon rank-sum test); AUC = 0.69. Human data (B): p = 0.014 (Wilcoxon rank-sum test); AUC = 0.61).</p>", "links"=>[], "tags"=>["dengue-derived", "ctl", "epitopes", "immunogenicity"], "article_id"=>831666, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Jorg J. A. Calis", "Matt Maybeno", "Jason A. Greenbaum", "Daniela Weiskopf", "Aruna D. De Silva", "Alessandro Sette", "Can Keşmir", "Bjoern Peters"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003266.g004", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predicting_Dengue_derived_CTL_epitopes_with_the_immunogenicity_model_/831666", "title"=>"Predicting Dengue-derived CTL epitopes with the immunogenicity model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-24 02:56:37"}

PMC Usage Stats | Further Information

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Relative Metric

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F1000Prime