PMC:7111504 / 12361-16550 JSON TXT

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LitCovid-PubTator

LitCovid-PMC-OGER-BB

LitCovid-PD-FMA-UBERON

LitCovid-PD-MONDO

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T19","span":{"begin":406,"end":411},"obj":"Disease"}],"attributes":[{"id":"A19","pred":"mondo_id","subj":"T19","obj":"http://purl.obolibrary.org/obo/MONDO_0005070"}],"text":"Immunogenicity of 2019-nCoV peptides to 1G4 CD8+ TCR molecule\nWhile our de novo candidates are appealing shortlisted targets for experimental validation, it does not provide information about target T cell receptors (TCRs). We therefore set out to interrogate the possibility of cross reactivity with one well-studied TCR.\nT cell cross-reactivity has been instrumental for the T cell immunity against both tumor antigens and external pathogens. In that regard, a number of T cells have been extensively characterized including 1G4 CD8+ TCR, which is known to recognize the ‘SLLMWITQC’ peptide presented by HLA-A*02:01. We therefore set out to leverage the data from a recently published study 7 and exploit the possibility of cross reactivity of this TCR to any 2019-nCoV peptide.\nHere, we scanned all 9-mers from the 2019-nCoV proteome (9613 peptides) with Binding, Activating and Killing Position Weight Matrices (PWM, see the method section) and associated each peptide with the geometric mean of these three assays as a measure of immunogenicity (Data Table 4 4). The distributions of binding, activation and killing scores along with their multiplicative score and geometric mean are illustrated in Figure 4. Based on geometric mean, we observed 20 2019-nCoV peptides with a score \u003e 0.8 and 516 peptides \u003e 0.7. The 9-mer peptides with geometric mean \u003e 0.7 and positive HLA-A*02:01 binding prediction by NetMHCpan 4.0 are listed in Table 4.\nFigure 4. Distribution of 1G4 TCR positional weight matrix scores for 2019-nCoV peptides.\nThe positional weight matrices were obtained from 7 and 9613 9-mers generated from 10 2019-nCoV ORFs were computed for their TCR recognition potential.\nTable 4. 2019-nCoV 9-mer peptides with geometric mean ≥ 0.7 by 1G4 TCR positional weight matrix and predicted positive to bind HLA-A*02:01 by NetMHCpan 4.0 (Rank = NetMHCpan rank).\nPeptide Binding score Activation score Killing score geoMean Rank Binder\nRIMTWLDMV 0.866377428 0.853995 0.776303 0.831249 0.3481 SB\nALNTLVKQL 0.802453741 0.75073 0.785957 0.779413 0.6159 WB\nLLLDRLNQL 0.809895414 0.7752 0.741096 0.774888 0.0423 SB\nMIAQYTSAL 0.766262499 0.789511 0.749477 0.768242 0.9238 WB\nVLSTFISAA 0.799672451 0.756117 0.687278 0.746239 0.536 WB\nNVLAWLYAA 0.761297552 0.686117 0.739944 0.728423 1.4457 WB\nRLANECAQV 0.783161706 0.719705 0.680504 0.726572 0.2049 SB\nKLLKSIAAT 0.748896679 0.708996 0.697463 0.718118 1.0923 WB\nQLSLPVLQV 0.70128376 0.715259 0.708405 0.708293 0.4768 SB\nVQMAPISAM 0.729320768 0.698514 0.689612 0.705612 1.4677 WB\nLLLTILTSL 0.7131709 0.715194 0.680064 0.702623 0.2712 SB\nSVLLFLAFV 0.736972762 0.690855 0.679534 0.70202 1.1449 WB\nLMWLIINLV 0.727847374 0.681119 0.694007 0.700717 1.304 WB We further analysed the MHC binding propensities and gathered peptides not only predicted positive by NetMHCpan but also to have leucine (L) and valine (V) in anchor positions 2 (P2) and 9 (P9) respectively. Previous studies have shown that for MHC-I HLA-A02:01 specific peptides, 9-mer peptides with leucine at P2 and valine at P9 are preferably presented on the surface of HLA-A02:01 8. Looking at the LV peptide, we identified 44 2019-nCoV peptides of which 2 peptides had immunogenicity score \u003e 0.7 and 12 peptides \u003e 0.6 ( Table 5). Thus, here we provide the list of peptides that are potential targets for 1G4 TCR recognition for subjects with HLA-A02:01 haplotype.\nTable 5. 2019-nCoV 9-mer peptides having leucine-valine in anchor positions.\nPeptides have geometric mean ≥ 0.6 and ≤ 0.7 (for those ≥ 0.7, refer to Table 4) by 1G4 TCR positional weight matrix and predicted positive for HLA-A*02:01 binding by NetMHCpan 4.0 (Rank = NetMHCpan rank).\nPeptide Binding score Activation score Killing score geoMean Rank Binder\nTLMNVLTLV 0.723687 0.658986 0.652178 0.677534 0.0444 SB\nQLEMELTPV 0.711291 0.651003 0.608605 0.655625 1.6769 WB\nMLAKALRKV 0.668756 0.610664 0.65968 0.645854 0.3524 SB\nGLFKDCSKV 0.675952 0.632375 0.594753 0.633494 0.2677 SB\nALSKGVHFV 0.652549 0.604952 0.586236 0.613954 0.0425 SB\nYLNTLTLAV 0.624147 0.610826 0.575445 0.603119 0.0453 SB"}

LitCovid-PD-CLO

LitCovid-PD-CHEBI

LitCovid-PD-HP

{"project":"LitCovid-PD-HP","denotations":[{"id":"T1","span":{"begin":406,"end":411},"obj":"Phenotype"}],"attributes":[{"id":"A1","pred":"hp_id","subj":"T1","obj":"http://purl.obolibrary.org/obo/HP_0002664"}],"text":"Immunogenicity of 2019-nCoV peptides to 1G4 CD8+ TCR molecule\nWhile our de novo candidates are appealing shortlisted targets for experimental validation, it does not provide information about target T cell receptors (TCRs). We therefore set out to interrogate the possibility of cross reactivity with one well-studied TCR.\nT cell cross-reactivity has been instrumental for the T cell immunity against both tumor antigens and external pathogens. In that regard, a number of T cells have been extensively characterized including 1G4 CD8+ TCR, which is known to recognize the ‘SLLMWITQC’ peptide presented by HLA-A*02:01. We therefore set out to leverage the data from a recently published study 7 and exploit the possibility of cross reactivity of this TCR to any 2019-nCoV peptide.\nHere, we scanned all 9-mers from the 2019-nCoV proteome (9613 peptides) with Binding, Activating and Killing Position Weight Matrices (PWM, see the method section) and associated each peptide with the geometric mean of these three assays as a measure of immunogenicity (Data Table 4 4). The distributions of binding, activation and killing scores along with their multiplicative score and geometric mean are illustrated in Figure 4. Based on geometric mean, we observed 20 2019-nCoV peptides with a score \u003e 0.8 and 516 peptides \u003e 0.7. The 9-mer peptides with geometric mean \u003e 0.7 and positive HLA-A*02:01 binding prediction by NetMHCpan 4.0 are listed in Table 4.\nFigure 4. Distribution of 1G4 TCR positional weight matrix scores for 2019-nCoV peptides.\nThe positional weight matrices were obtained from 7 and 9613 9-mers generated from 10 2019-nCoV ORFs were computed for their TCR recognition potential.\nTable 4. 2019-nCoV 9-mer peptides with geometric mean ≥ 0.7 by 1G4 TCR positional weight matrix and predicted positive to bind HLA-A*02:01 by NetMHCpan 4.0 (Rank = NetMHCpan rank).\nPeptide Binding score Activation score Killing score geoMean Rank Binder\nRIMTWLDMV 0.866377428 0.853995 0.776303 0.831249 0.3481 SB\nALNTLVKQL 0.802453741 0.75073 0.785957 0.779413 0.6159 WB\nLLLDRLNQL 0.809895414 0.7752 0.741096 0.774888 0.0423 SB\nMIAQYTSAL 0.766262499 0.789511 0.749477 0.768242 0.9238 WB\nVLSTFISAA 0.799672451 0.756117 0.687278 0.746239 0.536 WB\nNVLAWLYAA 0.761297552 0.686117 0.739944 0.728423 1.4457 WB\nRLANECAQV 0.783161706 0.719705 0.680504 0.726572 0.2049 SB\nKLLKSIAAT 0.748896679 0.708996 0.697463 0.718118 1.0923 WB\nQLSLPVLQV 0.70128376 0.715259 0.708405 0.708293 0.4768 SB\nVQMAPISAM 0.729320768 0.698514 0.689612 0.705612 1.4677 WB\nLLLTILTSL 0.7131709 0.715194 0.680064 0.702623 0.2712 SB\nSVLLFLAFV 0.736972762 0.690855 0.679534 0.70202 1.1449 WB\nLMWLIINLV 0.727847374 0.681119 0.694007 0.700717 1.304 WB We further analysed the MHC binding propensities and gathered peptides not only predicted positive by NetMHCpan but also to have leucine (L) and valine (V) in anchor positions 2 (P2) and 9 (P9) respectively. Previous studies have shown that for MHC-I HLA-A02:01 specific peptides, 9-mer peptides with leucine at P2 and valine at P9 are preferably presented on the surface of HLA-A02:01 8. Looking at the LV peptide, we identified 44 2019-nCoV peptides of which 2 peptides had immunogenicity score \u003e 0.7 and 12 peptides \u003e 0.6 ( Table 5). Thus, here we provide the list of peptides that are potential targets for 1G4 TCR recognition for subjects with HLA-A02:01 haplotype.\nTable 5. 2019-nCoV 9-mer peptides having leucine-valine in anchor positions.\nPeptides have geometric mean ≥ 0.6 and ≤ 0.7 (for those ≥ 0.7, refer to Table 4) by 1G4 TCR positional weight matrix and predicted positive for HLA-A*02:01 binding by NetMHCpan 4.0 (Rank = NetMHCpan rank).\nPeptide Binding score Activation score Killing score geoMean Rank Binder\nTLMNVLTLV 0.723687 0.658986 0.652178 0.677534 0.0444 SB\nQLEMELTPV 0.711291 0.651003 0.608605 0.655625 1.6769 WB\nMLAKALRKV 0.668756 0.610664 0.65968 0.645854 0.3524 SB\nGLFKDCSKV 0.675952 0.632375 0.594753 0.633494 0.2677 SB\nALSKGVHFV 0.652549 0.604952 0.586236 0.613954 0.0425 SB\nYLNTLTLAV 0.624147 0.610826 0.575445 0.603119 0.0453 SB"}

LitCovid-sentences

MyTest

{"project":"MyTest","denotations":[{"id":"32269766-31695703-30420973","span":{"begin":693,"end":695},"obj":"31695703"},{"id":"32269766-31695703-30420974","span":{"begin":1586,"end":1588},"obj":"31695703"},{"id":"32269766-1709722-30420975","span":{"begin":3171,"end":3172},"obj":"1709722"}],"namespaces":[{"prefix":"_base","uri":"https://www.uniprot.org/uniprot/testbase"},{"prefix":"UniProtKB","uri":"https://www.uniprot.org/uniprot/"},{"prefix":"uniprot","uri":"https://www.uniprot.org/uniprotkb/"}],"text":"Immunogenicity of 2019-nCoV peptides to 1G4 CD8+ TCR molecule\nWhile our de novo candidates are appealing shortlisted targets for experimental validation, it does not provide information about target T cell receptors (TCRs). We therefore set out to interrogate the possibility of cross reactivity with one well-studied TCR.\nT cell cross-reactivity has been instrumental for the T cell immunity against both tumor antigens and external pathogens. In that regard, a number of T cells have been extensively characterized including 1G4 CD8+ TCR, which is known to recognize the ‘SLLMWITQC’ peptide presented by HLA-A*02:01. We therefore set out to leverage the data from a recently published study 7 and exploit the possibility of cross reactivity of this TCR to any 2019-nCoV peptide.\nHere, we scanned all 9-mers from the 2019-nCoV proteome (9613 peptides) with Binding, Activating and Killing Position Weight Matrices (PWM, see the method section) and associated each peptide with the geometric mean of these three assays as a measure of immunogenicity (Data Table 4 4). The distributions of binding, activation and killing scores along with their multiplicative score and geometric mean are illustrated in Figure 4. Based on geometric mean, we observed 20 2019-nCoV peptides with a score \u003e 0.8 and 516 peptides \u003e 0.7. The 9-mer peptides with geometric mean \u003e 0.7 and positive HLA-A*02:01 binding prediction by NetMHCpan 4.0 are listed in Table 4.\nFigure 4. Distribution of 1G4 TCR positional weight matrix scores for 2019-nCoV peptides.\nThe positional weight matrices were obtained from 7 and 9613 9-mers generated from 10 2019-nCoV ORFs were computed for their TCR recognition potential.\nTable 4. 2019-nCoV 9-mer peptides with geometric mean ≥ 0.7 by 1G4 TCR positional weight matrix and predicted positive to bind HLA-A*02:01 by NetMHCpan 4.0 (Rank = NetMHCpan rank).\nPeptide Binding score Activation score Killing score geoMean Rank Binder\nRIMTWLDMV 0.866377428 0.853995 0.776303 0.831249 0.3481 SB\nALNTLVKQL 0.802453741 0.75073 0.785957 0.779413 0.6159 WB\nLLLDRLNQL 0.809895414 0.7752 0.741096 0.774888 0.0423 SB\nMIAQYTSAL 0.766262499 0.789511 0.749477 0.768242 0.9238 WB\nVLSTFISAA 0.799672451 0.756117 0.687278 0.746239 0.536 WB\nNVLAWLYAA 0.761297552 0.686117 0.739944 0.728423 1.4457 WB\nRLANECAQV 0.783161706 0.719705 0.680504 0.726572 0.2049 SB\nKLLKSIAAT 0.748896679 0.708996 0.697463 0.718118 1.0923 WB\nQLSLPVLQV 0.70128376 0.715259 0.708405 0.708293 0.4768 SB\nVQMAPISAM 0.729320768 0.698514 0.689612 0.705612 1.4677 WB\nLLLTILTSL 0.7131709 0.715194 0.680064 0.702623 0.2712 SB\nSVLLFLAFV 0.736972762 0.690855 0.679534 0.70202 1.1449 WB\nLMWLIINLV 0.727847374 0.681119 0.694007 0.700717 1.304 WB We further analysed the MHC binding propensities and gathered peptides not only predicted positive by NetMHCpan but also to have leucine (L) and valine (V) in anchor positions 2 (P2) and 9 (P9) respectively. Previous studies have shown that for MHC-I HLA-A02:01 specific peptides, 9-mer peptides with leucine at P2 and valine at P9 are preferably presented on the surface of HLA-A02:01 8. Looking at the LV peptide, we identified 44 2019-nCoV peptides of which 2 peptides had immunogenicity score \u003e 0.7 and 12 peptides \u003e 0.6 ( Table 5). Thus, here we provide the list of peptides that are potential targets for 1G4 TCR recognition for subjects with HLA-A02:01 haplotype.\nTable 5. 2019-nCoV 9-mer peptides having leucine-valine in anchor positions.\nPeptides have geometric mean ≥ 0.6 and ≤ 0.7 (for those ≥ 0.7, refer to Table 4) by 1G4 TCR positional weight matrix and predicted positive for HLA-A*02:01 binding by NetMHCpan 4.0 (Rank = NetMHCpan rank).\nPeptide Binding score Activation score Killing score geoMean Rank Binder\nTLMNVLTLV 0.723687 0.658986 0.652178 0.677534 0.0444 SB\nQLEMELTPV 0.711291 0.651003 0.608605 0.655625 1.6769 WB\nMLAKALRKV 0.668756 0.610664 0.65968 0.645854 0.3524 SB\nGLFKDCSKV 0.675952 0.632375 0.594753 0.633494 0.2677 SB\nALSKGVHFV 0.652549 0.604952 0.586236 0.613954 0.0425 SB\nYLNTLTLAV 0.624147 0.610826 0.575445 0.603119 0.0453 SB"}

2_test

{"project":"2_test","denotations":[{"id":"32269766-31695703-30420973","span":{"begin":693,"end":694},"obj":"31695703"},{"id":"32269766-31695703-30420974","span":{"begin":1586,"end":1587},"obj":"31695703"},{"id":"32269766-1709722-30420975","span":{"begin":3171,"end":3172},"obj":"1709722"}],"text":"Immunogenicity of 2019-nCoV peptides to 1G4 CD8+ TCR molecule\nWhile our de novo candidates are appealing shortlisted targets for experimental validation, it does not provide information about target T cell receptors (TCRs). We therefore set out to interrogate the possibility of cross reactivity with one well-studied TCR.\nT cell cross-reactivity has been instrumental for the T cell immunity against both tumor antigens and external pathogens. In that regard, a number of T cells have been extensively characterized including 1G4 CD8+ TCR, which is known to recognize the ‘SLLMWITQC’ peptide presented by HLA-A*02:01. We therefore set out to leverage the data from a recently published study 7 and exploit the possibility of cross reactivity of this TCR to any 2019-nCoV peptide.\nHere, we scanned all 9-mers from the 2019-nCoV proteome (9613 peptides) with Binding, Activating and Killing Position Weight Matrices (PWM, see the method section) and associated each peptide with the geometric mean of these three assays as a measure of immunogenicity (Data Table 4 4). The distributions of binding, activation and killing scores along with their multiplicative score and geometric mean are illustrated in Figure 4. Based on geometric mean, we observed 20 2019-nCoV peptides with a score \u003e 0.8 and 516 peptides \u003e 0.7. The 9-mer peptides with geometric mean \u003e 0.7 and positive HLA-A*02:01 binding prediction by NetMHCpan 4.0 are listed in Table 4.\nFigure 4. Distribution of 1G4 TCR positional weight matrix scores for 2019-nCoV peptides.\nThe positional weight matrices were obtained from 7 and 9613 9-mers generated from 10 2019-nCoV ORFs were computed for their TCR recognition potential.\nTable 4. 2019-nCoV 9-mer peptides with geometric mean ≥ 0.7 by 1G4 TCR positional weight matrix and predicted positive to bind HLA-A*02:01 by NetMHCpan 4.0 (Rank = NetMHCpan rank).\nPeptide Binding score Activation score Killing score geoMean Rank Binder\nRIMTWLDMV 0.866377428 0.853995 0.776303 0.831249 0.3481 SB\nALNTLVKQL 0.802453741 0.75073 0.785957 0.779413 0.6159 WB\nLLLDRLNQL 0.809895414 0.7752 0.741096 0.774888 0.0423 SB\nMIAQYTSAL 0.766262499 0.789511 0.749477 0.768242 0.9238 WB\nVLSTFISAA 0.799672451 0.756117 0.687278 0.746239 0.536 WB\nNVLAWLYAA 0.761297552 0.686117 0.739944 0.728423 1.4457 WB\nRLANECAQV 0.783161706 0.719705 0.680504 0.726572 0.2049 SB\nKLLKSIAAT 0.748896679 0.708996 0.697463 0.718118 1.0923 WB\nQLSLPVLQV 0.70128376 0.715259 0.708405 0.708293 0.4768 SB\nVQMAPISAM 0.729320768 0.698514 0.689612 0.705612 1.4677 WB\nLLLTILTSL 0.7131709 0.715194 0.680064 0.702623 0.2712 SB\nSVLLFLAFV 0.736972762 0.690855 0.679534 0.70202 1.1449 WB\nLMWLIINLV 0.727847374 0.681119 0.694007 0.700717 1.304 WB We further analysed the MHC binding propensities and gathered peptides not only predicted positive by NetMHCpan but also to have leucine (L) and valine (V) in anchor positions 2 (P2) and 9 (P9) respectively. Previous studies have shown that for MHC-I HLA-A02:01 specific peptides, 9-mer peptides with leucine at P2 and valine at P9 are preferably presented on the surface of HLA-A02:01 8. Looking at the LV peptide, we identified 44 2019-nCoV peptides of which 2 peptides had immunogenicity score \u003e 0.7 and 12 peptides \u003e 0.6 ( Table 5). Thus, here we provide the list of peptides that are potential targets for 1G4 TCR recognition for subjects with HLA-A02:01 haplotype.\nTable 5. 2019-nCoV 9-mer peptides having leucine-valine in anchor positions.\nPeptides have geometric mean ≥ 0.6 and ≤ 0.7 (for those ≥ 0.7, refer to Table 4) by 1G4 TCR positional weight matrix and predicted positive for HLA-A*02:01 binding by NetMHCpan 4.0 (Rank = NetMHCpan rank).\nPeptide Binding score Activation score Killing score geoMean Rank Binder\nTLMNVLTLV 0.723687 0.658986 0.652178 0.677534 0.0444 SB\nQLEMELTPV 0.711291 0.651003 0.608605 0.655625 1.6769 WB\nMLAKALRKV 0.668756 0.610664 0.65968 0.645854 0.3524 SB\nGLFKDCSKV 0.675952 0.632375 0.594753 0.633494 0.2677 SB\nALSKGVHFV 0.652549 0.604952 0.586236 0.613954 0.0425 SB\nYLNTLTLAV 0.624147 0.610826 0.575445 0.603119 0.0453 SB"}

PMC:7111504 / 12361-16550 JSONTXT

Annnotations TAB JSON ListView MergeView

PMC:7111504 / 12361-16550 JSON TXT