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LitCovid-PubTator

LitCovid-PMC-OGER-BB

LitCovid-PD-UBERON

{"project":"LitCovid-PD-UBERON","denotations":[{"id":"T1","span":{"begin":191,"end":195},"obj":"Body_part"}],"attributes":[{"id":"A1","pred":"uberon_id","subj":"T1","obj":"http://purl.obolibrary.org/obo/UBERON_0002415"}],"text":"Identification of 22 2019-nCoV peptides with high degree of similarity to previously reported immunogenic viral peptides\nIn addition to 28 identical hits against SARS CoV, we observed a long tail in distribution of normalized alignment scores between 10 2019-nCoV ORFs and 35,225 IEDB peptides ( Figure 1A, Methods). We therefore set out to further investigate potential vaccine targets among highly similar sequences.\nFigure 1. 2019-nCoV peptides with high sequence similarity to immunogenic peptides in IEDB.\nA. Comparison of normalized sequence alignment score for peptides with exact and non-exact matches. B. Number of target peptides grouped by their source organism. The peptides having an exact sequence alignment with epitopes in IEDB had normalized alignment scores ranging from 4 to 6. Taking the normalized alignment score of exact matches as a reference, we extracted 2019-nCoV peptides having score greater or equal to 4. As illustrated in Figure 1A, we observed 45 and 11 peptides having normalized alignment score ≥ 4 and ≥ 5 respectively ( Figure 1A inset). The target peptides were originated from 10 different sources ( Figure 1B) where a total 36 peptides were derived from strains associated to SARS CoV. Of interest, we also observed 7 hits having high sequence similarity to targets from Homo sapiens.\nIn order to investigate the extent to which the difference between the source (2019-nCoV) and target (IEDB) peptides influences the immunogenicity of the source peptides we used a recently published immunogenicity model 5 to predict and compare the immunogenicity between the source and target peptides (Data Table 2 4).\nWe could see a similar (close to identical) immunogenicity scores for a number of IEDB and 2019-nCov peptides especially for those with high immunogenicity scores ( Figure 2). While all 48 can be potential targets, of particular interest were those having higher immunogenicity score than IEDB peptides. Here, we list 22 out of 48 2019-nCoV peptides that scored higher compared to their targets that have been characterized to be immunogenic ( Table 2). In this list 15 (68%) 2019-nCov peptides have a score higher than 0.5 whereas only 11(50%) of IEDB get a score immunogenicity score greater than 0.5.\nTable 2. List of 22 2019-nCoV peptides having a higher predicted immunogenicity score than their target peptides.\nIEDB.peptide 2019-nCoV.pattern IEDB.prob nCol.prob\nWYMWLGARY WYIWLG 0.999249 0.999441\nGLMWLSYFV GLMWLSYFI 0.995073 0.998216\nGLVFLCLQY GIVFMCVEY 0.98123 0.984127\nTWLTYHGAIKLDDKDPQFKDNVILL TWLTYTGAIKLDDKDPNFKDQVILL 0.925862 0.975242\nIGMEVTPSGTWLTYH IGMEVTPSGTWLTY 0.903518 0.919184\nGETALALLLLDRLNQ GDAALALLLLDRLNQ 0.853114 0.900655\nTPSGTWLTYHGAIKL TPSGTWLTYTGAIKL 0.620894 0.662417\nSIVAYTMSL SIIAYTMSL 0.589694 0.693763\nRRPQGLPNNIASWFT RRPQGLPNNTASWFT 0.533253 0.584355\nYNLKWN YNL-WN 0.520244 0.765309\nAGCLIGAEHVDTSYECDI AGCLIGAEHVNNSYECDI 0.503905 0.56813\nGFMKQYGECLGDINARDL GFIKQYGDCLGDIAARDL 0.471939 0.506817\nANKEGIVWVATEGAL ANKDGIIWVATEGAL 0.367723 0.404796\nWNPDDY WNADLY 0.355018 0.584726\nPDDYGG PDDFTG 0.334887 0.527287\nTWLTYHGAIKLDDKDPQF TWLTYTGAIKLDDKDPNF 0.27017 0.529675\nDEVNQI DEVRQI 0.18504 0.187797\nSSKRFQPFQQFGRDV SNKKFLPFQQFGRDI 0.098384 0.119472\nNHDSPDAEL NHTSPDVDL 0.067808 0.17889\nTKQYNVTQAF TKAYNVTQAF 0.054818 0.171488\nVKQMYKTPTLKYFGGFNF VKQIYKTPPIKDFGGFNF 0.018685 0.135681\nQKRTATKQYNVTQAF QKRTATKAYNVTQAF 0.004891 0.037776\nFigure 2. Predicted immunogenicity for IEDB immunogenic vs. 2019-nCoV peptides.\n2019-nCoV peptides having a high sequence similarity to immunogenic peptides and their targets were analysed for their immunogenicity potential by iPred algorithm. It is worth noting that in general predicting immunogenicity of given a peptide is challenging and not a fully solved problem, and therefore current models for predicting immunogenicity are suboptimal. iPred is also not an exception. In fact, we could see that a substantial number of IEDB immunogenic peptides were scored \u003c 0.5 (the threshold score used to classify immunogenic vs non-immunogenic). This led us to ask whether we can gather any other evidence of either immunogenicity or cross-reactivity."}

LitCovid-PD-MONDO

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T12","span":{"begin":162,"end":166},"obj":"Disease"},{"id":"T13","span":{"begin":1217,"end":1221},"obj":"Disease"}],"attributes":[{"id":"A12","pred":"mondo_id","subj":"T12","obj":"http://purl.obolibrary.org/obo/MONDO_0005091"},{"id":"A13","pred":"mondo_id","subj":"T13","obj":"http://purl.obolibrary.org/obo/MONDO_0005091"}],"text":"Identification of 22 2019-nCoV peptides with high degree of similarity to previously reported immunogenic viral peptides\nIn addition to 28 identical hits against SARS CoV, we observed a long tail in distribution of normalized alignment scores between 10 2019-nCoV ORFs and 35,225 IEDB peptides ( Figure 1A, Methods). We therefore set out to further investigate potential vaccine targets among highly similar sequences.\nFigure 1. 2019-nCoV peptides with high sequence similarity to immunogenic peptides in IEDB.\nA. Comparison of normalized sequence alignment score for peptides with exact and non-exact matches. B. Number of target peptides grouped by their source organism. The peptides having an exact sequence alignment with epitopes in IEDB had normalized alignment scores ranging from 4 to 6. Taking the normalized alignment score of exact matches as a reference, we extracted 2019-nCoV peptides having score greater or equal to 4. As illustrated in Figure 1A, we observed 45 and 11 peptides having normalized alignment score ≥ 4 and ≥ 5 respectively ( Figure 1A inset). The target peptides were originated from 10 different sources ( Figure 1B) where a total 36 peptides were derived from strains associated to SARS CoV. Of interest, we also observed 7 hits having high sequence similarity to targets from Homo sapiens.\nIn order to investigate the extent to which the difference between the source (2019-nCoV) and target (IEDB) peptides influences the immunogenicity of the source peptides we used a recently published immunogenicity model 5 to predict and compare the immunogenicity between the source and target peptides (Data Table 2 4).\nWe could see a similar (close to identical) immunogenicity scores for a number of IEDB and 2019-nCov peptides especially for those with high immunogenicity scores ( Figure 2). While all 48 can be potential targets, of particular interest were those having higher immunogenicity score than IEDB peptides. Here, we list 22 out of 48 2019-nCoV peptides that scored higher compared to their targets that have been characterized to be immunogenic ( Table 2). In this list 15 (68%) 2019-nCov peptides have a score higher than 0.5 whereas only 11(50%) of IEDB get a score immunogenicity score greater than 0.5.\nTable 2. List of 22 2019-nCoV peptides having a higher predicted immunogenicity score than their target peptides.\nIEDB.peptide 2019-nCoV.pattern IEDB.prob nCol.prob\nWYMWLGARY WYIWLG 0.999249 0.999441\nGLMWLSYFV GLMWLSYFI 0.995073 0.998216\nGLVFLCLQY GIVFMCVEY 0.98123 0.984127\nTWLTYHGAIKLDDKDPQFKDNVILL TWLTYTGAIKLDDKDPNFKDQVILL 0.925862 0.975242\nIGMEVTPSGTWLTYH IGMEVTPSGTWLTY 0.903518 0.919184\nGETALALLLLDRLNQ GDAALALLLLDRLNQ 0.853114 0.900655\nTPSGTWLTYHGAIKL TPSGTWLTYTGAIKL 0.620894 0.662417\nSIVAYTMSL SIIAYTMSL 0.589694 0.693763\nRRPQGLPNNIASWFT RRPQGLPNNTASWFT 0.533253 0.584355\nYNLKWN YNL-WN 0.520244 0.765309\nAGCLIGAEHVDTSYECDI AGCLIGAEHVNNSYECDI 0.503905 0.56813\nGFMKQYGECLGDINARDL GFIKQYGDCLGDIAARDL 0.471939 0.506817\nANKEGIVWVATEGAL ANKDGIIWVATEGAL 0.367723 0.404796\nWNPDDY WNADLY 0.355018 0.584726\nPDDYGG PDDFTG 0.334887 0.527287\nTWLTYHGAIKLDDKDPQF TWLTYTGAIKLDDKDPNF 0.27017 0.529675\nDEVNQI DEVRQI 0.18504 0.187797\nSSKRFQPFQQFGRDV SNKKFLPFQQFGRDI 0.098384 0.119472\nNHDSPDAEL NHTSPDVDL 0.067808 0.17889\nTKQYNVTQAF TKAYNVTQAF 0.054818 0.171488\nVKQMYKTPTLKYFGGFNF VKQIYKTPPIKDFGGFNF 0.018685 0.135681\nQKRTATKQYNVTQAF QKRTATKAYNVTQAF 0.004891 0.037776\nFigure 2. Predicted immunogenicity for IEDB immunogenic vs. 2019-nCoV peptides.\n2019-nCoV peptides having a high sequence similarity to immunogenic peptides and their targets were analysed for their immunogenicity potential by iPred algorithm. It is worth noting that in general predicting immunogenicity of given a peptide is challenging and not a fully solved problem, and therefore current models for predicting immunogenicity are suboptimal. iPred is also not an exception. In fact, we could see that a substantial number of IEDB immunogenic peptides were scored \u003c 0.5 (the threshold score used to classify immunogenic vs non-immunogenic). This led us to ask whether we can gather any other evidence of either immunogenicity or cross-reactivity."}

LitCovid-PD-CLO

LitCovid-PD-CHEBI

LitCovid-sentences

MyTest

{"project":"MyTest","denotations":[{"id":"32269766-30144804-30420971","span":{"begin":1546,"end":1548},"obj":"30144804"}],"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":"Identification of 22 2019-nCoV peptides with high degree of similarity to previously reported immunogenic viral peptides\nIn addition to 28 identical hits against SARS CoV, we observed a long tail in distribution of normalized alignment scores between 10 2019-nCoV ORFs and 35,225 IEDB peptides ( Figure 1A, Methods). We therefore set out to further investigate potential vaccine targets among highly similar sequences.\nFigure 1. 2019-nCoV peptides with high sequence similarity to immunogenic peptides in IEDB.\nA. Comparison of normalized sequence alignment score for peptides with exact and non-exact matches. B. Number of target peptides grouped by their source organism. The peptides having an exact sequence alignment with epitopes in IEDB had normalized alignment scores ranging from 4 to 6. Taking the normalized alignment score of exact matches as a reference, we extracted 2019-nCoV peptides having score greater or equal to 4. As illustrated in Figure 1A, we observed 45 and 11 peptides having normalized alignment score ≥ 4 and ≥ 5 respectively ( Figure 1A inset). The target peptides were originated from 10 different sources ( Figure 1B) where a total 36 peptides were derived from strains associated to SARS CoV. Of interest, we also observed 7 hits having high sequence similarity to targets from Homo sapiens.\nIn order to investigate the extent to which the difference between the source (2019-nCoV) and target (IEDB) peptides influences the immunogenicity of the source peptides we used a recently published immunogenicity model 5 to predict and compare the immunogenicity between the source and target peptides (Data Table 2 4).\nWe could see a similar (close to identical) immunogenicity scores for a number of IEDB and 2019-nCov peptides especially for those with high immunogenicity scores ( Figure 2). While all 48 can be potential targets, of particular interest were those having higher immunogenicity score than IEDB peptides. Here, we list 22 out of 48 2019-nCoV peptides that scored higher compared to their targets that have been characterized to be immunogenic ( Table 2). In this list 15 (68%) 2019-nCov peptides have a score higher than 0.5 whereas only 11(50%) of IEDB get a score immunogenicity score greater than 0.5.\nTable 2. List of 22 2019-nCoV peptides having a higher predicted immunogenicity score than their target peptides.\nIEDB.peptide 2019-nCoV.pattern IEDB.prob nCol.prob\nWYMWLGARY WYIWLG 0.999249 0.999441\nGLMWLSYFV GLMWLSYFI 0.995073 0.998216\nGLVFLCLQY GIVFMCVEY 0.98123 0.984127\nTWLTYHGAIKLDDKDPQFKDNVILL TWLTYTGAIKLDDKDPNFKDQVILL 0.925862 0.975242\nIGMEVTPSGTWLTYH IGMEVTPSGTWLTY 0.903518 0.919184\nGETALALLLLDRLNQ GDAALALLLLDRLNQ 0.853114 0.900655\nTPSGTWLTYHGAIKL TPSGTWLTYTGAIKL 0.620894 0.662417\nSIVAYTMSL SIIAYTMSL 0.589694 0.693763\nRRPQGLPNNIASWFT RRPQGLPNNTASWFT 0.533253 0.584355\nYNLKWN YNL-WN 0.520244 0.765309\nAGCLIGAEHVDTSYECDI AGCLIGAEHVNNSYECDI 0.503905 0.56813\nGFMKQYGECLGDINARDL GFIKQYGDCLGDIAARDL 0.471939 0.506817\nANKEGIVWVATEGAL ANKDGIIWVATEGAL 0.367723 0.404796\nWNPDDY WNADLY 0.355018 0.584726\nPDDYGG PDDFTG 0.334887 0.527287\nTWLTYHGAIKLDDKDPQF TWLTYTGAIKLDDKDPNF 0.27017 0.529675\nDEVNQI DEVRQI 0.18504 0.187797\nSSKRFQPFQQFGRDV SNKKFLPFQQFGRDI 0.098384 0.119472\nNHDSPDAEL NHTSPDVDL 0.067808 0.17889\nTKQYNVTQAF TKAYNVTQAF 0.054818 0.171488\nVKQMYKTPTLKYFGGFNF VKQIYKTPPIKDFGGFNF 0.018685 0.135681\nQKRTATKQYNVTQAF QKRTATKAYNVTQAF 0.004891 0.037776\nFigure 2. Predicted immunogenicity for IEDB immunogenic vs. 2019-nCoV peptides.\n2019-nCoV peptides having a high sequence similarity to immunogenic peptides and their targets were analysed for their immunogenicity potential by iPred algorithm. It is worth noting that in general predicting immunogenicity of given a peptide is challenging and not a fully solved problem, and therefore current models for predicting immunogenicity are suboptimal. iPred is also not an exception. In fact, we could see that a substantial number of IEDB immunogenic peptides were scored \u003c 0.5 (the threshold score used to classify immunogenic vs non-immunogenic). This led us to ask whether we can gather any other evidence of either immunogenicity or cross-reactivity."}

2_test

{"project":"2_test","denotations":[{"id":"32269766-30144804-30420971","span":{"begin":1546,"end":1547},"obj":"30144804"}],"text":"Identification of 22 2019-nCoV peptides with high degree of similarity to previously reported immunogenic viral peptides\nIn addition to 28 identical hits against SARS CoV, we observed a long tail in distribution of normalized alignment scores between 10 2019-nCoV ORFs and 35,225 IEDB peptides ( Figure 1A, Methods). We therefore set out to further investigate potential vaccine targets among highly similar sequences.\nFigure 1. 2019-nCoV peptides with high sequence similarity to immunogenic peptides in IEDB.\nA. Comparison of normalized sequence alignment score for peptides with exact and non-exact matches. B. Number of target peptides grouped by their source organism. The peptides having an exact sequence alignment with epitopes in IEDB had normalized alignment scores ranging from 4 to 6. Taking the normalized alignment score of exact matches as a reference, we extracted 2019-nCoV peptides having score greater or equal to 4. As illustrated in Figure 1A, we observed 45 and 11 peptides having normalized alignment score ≥ 4 and ≥ 5 respectively ( Figure 1A inset). The target peptides were originated from 10 different sources ( Figure 1B) where a total 36 peptides were derived from strains associated to SARS CoV. Of interest, we also observed 7 hits having high sequence similarity to targets from Homo sapiens.\nIn order to investigate the extent to which the difference between the source (2019-nCoV) and target (IEDB) peptides influences the immunogenicity of the source peptides we used a recently published immunogenicity model 5 to predict and compare the immunogenicity between the source and target peptides (Data Table 2 4).\nWe could see a similar (close to identical) immunogenicity scores for a number of IEDB and 2019-nCov peptides especially for those with high immunogenicity scores ( Figure 2). While all 48 can be potential targets, of particular interest were those having higher immunogenicity score than IEDB peptides. Here, we list 22 out of 48 2019-nCoV peptides that scored higher compared to their targets that have been characterized to be immunogenic ( Table 2). In this list 15 (68%) 2019-nCov peptides have a score higher than 0.5 whereas only 11(50%) of IEDB get a score immunogenicity score greater than 0.5.\nTable 2. List of 22 2019-nCoV peptides having a higher predicted immunogenicity score than their target peptides.\nIEDB.peptide 2019-nCoV.pattern IEDB.prob nCol.prob\nWYMWLGARY WYIWLG 0.999249 0.999441\nGLMWLSYFV GLMWLSYFI 0.995073 0.998216\nGLVFLCLQY GIVFMCVEY 0.98123 0.984127\nTWLTYHGAIKLDDKDPQFKDNVILL TWLTYTGAIKLDDKDPNFKDQVILL 0.925862 0.975242\nIGMEVTPSGTWLTYH IGMEVTPSGTWLTY 0.903518 0.919184\nGETALALLLLDRLNQ GDAALALLLLDRLNQ 0.853114 0.900655\nTPSGTWLTYHGAIKL TPSGTWLTYTGAIKL 0.620894 0.662417\nSIVAYTMSL SIIAYTMSL 0.589694 0.693763\nRRPQGLPNNIASWFT RRPQGLPNNTASWFT 0.533253 0.584355\nYNLKWN YNL-WN 0.520244 0.765309\nAGCLIGAEHVDTSYECDI AGCLIGAEHVNNSYECDI 0.503905 0.56813\nGFMKQYGECLGDINARDL GFIKQYGDCLGDIAARDL 0.471939 0.506817\nANKEGIVWVATEGAL ANKDGIIWVATEGAL 0.367723 0.404796\nWNPDDY WNADLY 0.355018 0.584726\nPDDYGG PDDFTG 0.334887 0.527287\nTWLTYHGAIKLDDKDPQF TWLTYTGAIKLDDKDPNF 0.27017 0.529675\nDEVNQI DEVRQI 0.18504 0.187797\nSSKRFQPFQQFGRDV SNKKFLPFQQFGRDI 0.098384 0.119472\nNHDSPDAEL NHTSPDVDL 0.067808 0.17889\nTKQYNVTQAF TKAYNVTQAF 0.054818 0.171488\nVKQMYKTPTLKYFGGFNF VKQIYKTPPIKDFGGFNF 0.018685 0.135681\nQKRTATKQYNVTQAF QKRTATKAYNVTQAF 0.004891 0.037776\nFigure 2. Predicted immunogenicity for IEDB immunogenic vs. 2019-nCoV peptides.\n2019-nCoV peptides having a high sequence similarity to immunogenic peptides and their targets were analysed for their immunogenicity potential by iPred algorithm. It is worth noting that in general predicting immunogenicity of given a peptide is challenging and not a fully solved problem, and therefore current models for predicting immunogenicity are suboptimal. iPred is also not an exception. In fact, we could see that a substantial number of IEDB immunogenic peptides were scored \u003c 0.5 (the threshold score used to classify immunogenic vs non-immunogenic). This led us to ask whether we can gather any other evidence of either immunogenicity or cross-reactivity."}

PMC:7111504 / 5933-10162 JSONTXT

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PMC:7111504 / 5933-10162 JSON TXT