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LitCovid-PubTator

LitCovid-PMC-OGER-BB

LitCovid-PD-FMA-UBERON

LitCovid-PD-UBERON

{"project":"LitCovid-PD-UBERON","denotations":[{"id":"T1","span":{"begin":2682,"end":2686},"obj":"Body_part"}],"attributes":[{"id":"A1","pred":"uberon_id","subj":"T1","obj":"http://purl.obolibrary.org/obo/UBERON_0002415"}],"text":"Results\n\nIdentification of 28 exact matches to SARS CoV immunogenic peptides by screening all epitopes deposited in IEDB\nWe collected all peptides in IEDB ( 3, as of 13-02-2020) reported positive in T cell assays and have human as the host organism. We then conducted a local sequence alignment of 10 2019-nCoV open reading frames (ORFs) against 35,225 IEDB peptides, and found 28 exact matches. Surprisingly, all identical hits (against peptides having sequence length greater than 3) were from SARS-CoV ( Table 1, Data Table 1 4). These peptides have been shown to bind various HLA alleles, although with higher tendency towards HLA-A:02:01, from both class I and class II, and can be target for CD8+ and CD4+ T cells respectively.\nTable 1. 28 2019-nCoV peptides having exact matches with immunogenic SARS-CoV peptides.\nIEDB.peptide 2019-nCoV.pattern Antigen.Name Allele.Name\nTLACFVLAAV TLACFVLAAV Membrane glycoprotein HLA-A *02:01\nAFFGMSRIGMEVTPSGTW AFFGMSRIGMEVTPSGTW N protein\nALNTPKDHI ALNTPKDHI Nucleoprotein HLA-A *02:01\nAQFAPSASAFFGMSR AQFAPSASAFFGMSR nucleocapsid protein HLA class II\nAQFAPSASAFFGMSRIGM AQFAPSASAFFGMSRIGM N protein\nGMSRIGMEV GMSRIGMEV Nucleoprotein HLA-A *02:01\nILLNKHIDA ILLNKHIDA Nucleoprotein HLA-A *02:01\nIRQGTDYKHWPQIAQFA IRQGTDYKHWPQIAQFA N protein\nKHWPQIAQFAPSASAFF KHWPQIAQFAPSASAFF N protein\nLALLLLDRL LALLLLDRL Nucleoprotein HLA-A *02:01\nLLLDRLNQL LLLDRLNQL Nucleoprotein HLA-A *02:01\nLLNKHIDAYKTFPPTEPK LLNKHIDAYKTFPPTEPK N protein\nLQLPQGTTL LQLPQGTTL Nucleoprotein HLA-A *02:01\nRRPQGLPNNTASWFT RRPQGLPNNTASWFT nucleocapsid protein HLA class I\nYKTFPPTEPKKDKKKK YKTFPPTEPKKDKKKK N protein\nILLNKHID ILLNKHID Nucleoprotein HLA-A *02:01\nMEVTPSGTWL MEVTPSGTWL nucleocapsid protein HLA-B *40:01\nALNTLVKQL ALNTLVKQL S protein HLA-A *02:01\nFIAGLIAIV FIAGLIAIV Spike glycoprotein precursor HLA-A2\nLITGRLQSL LITGRLQSL Spike glycoprotein precursor HLA-A2\nNLNESLIDL NLNESLIDL S protein HLA-A *02:01\nQALNTLVKQLSSNFGAI QALNTLVKQLSSNFGAI S protein HLA-DRB1 *04:01\nRLNEVAKNL RLNEVAKNL Spike glycoprotein precursor HLA-A *02:01\nVLNDILSRL VLNDILSRL S protein HLA-A *02:01\nVVFLHVTYV VVFLHVTYV Spike glycoprotein precursor HLA-A *02:01\nGAALQIPFAMQMAYRF GAALQIPFAMQMAYRF S protein HLA-DRA *01:01/DRB1 *07:01\nMAYRFNGIGVTQNVLY MAYRFNGIGVTQNVLY S protein HLA-DRB1 *04:01\nQLIRAAEIRASANLAATK QLIRAAEIRASANLAATK S protein HLA-DRB1 *04:01\n*SARS-CoV: Severe acute respiratory syndrome coronavirus\n\nIdentification 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.\n\nDe novo search of immunogenic peptides in 2019-nCoV proteome\nAs a complementary reciprocal approach, we conducted a de novo search of immunogenic peptides against the 2019-nCov proteome sequence. We scanned 9-mers from 2019-nCoV proteome with a window of 9 amino acids and step length of 1 amino acid (9613 in total). The immunogenicity of 9-mer peptides were predicted using iPred and MHC presentation scores were gauged using NetMHCpan 4.0 6 for various HLA types. In this task, we focused on haplotypes common in Chinese and European populations, which include HLA-A*02:01, HLA-A*01:01, HLA-B*07:02, HLA-B*40:01 and HLA-C*07:02 alleles (Data Table 3 4).\nBased on MHC presentation and immunogenicity prediction, we detected 5 peptides predicted to bind 4 different HLA alleles of which 2 had strong immunogenicity scores ( Figure 3). For those 65 strong binders to 3 different HLA types, 39 had immunogenicity scores ≥ 0.5 ( Table 3). Collectively this analysis suggests a number of 9-mer immunogenic candidates for further experimental validation.\nTable 3. 2019-nCoV 9-mer peptides predicted to bind 4 different HLA alleles by NetMHCpan 4.0, and those predicted to bind ≥ 3 HLA alleles and immunogenicity score ≥ 0.9 by iPred.\nFor different alleles, 0 denotes non-binding and 1 denotes binding predicted for specific HLA allele.\nAntigen.epitope Imm.prob A0101.NB A0201.NB B0702.NB B4001.NB C0702.NB Total binding HLA\nVQMAPISAM 0.893938 0 1 1 1 1 4\nAMYTPHTVL 0.862427 0 1 1 1 1 4\nTLDSKTQSL 0.254998 1 1 1 0 1 4\nKVDGVVQQL 0.191786 1 1 1 0 1 4\nKVDGVDVEL 0.18632 1 1 1 0 1 4\nMADQAMTQM 0.991227 1 0 1 0 1 3\nLEAPFLYLY 0.983072 1 0 0 1 1 3\nRTAPHGHVM 0.972153 1 0 1 0 1 3\nIPFAMQMAY 0.961569 1 0 1 0 1 3\nFLTENLLLY 0.951715 1 1 0 0 1 3\nYLQPRTFLL 0.947743 1 1 0 0 1 3\nMMISAGFSL 0.941318 0 1 1 0 1 3\nATLPKGIMM 0.926603 1 0 1 0 1 3\nFigure 3. De novo search of 9-mer 2019-nCoV peptides with MHC presentation and immunogenicity potential.\nThe MHC binding was predicted for HLA-A*02:01, HLA-A*01:01, HLA-B*07:02, HLA-B*40:01 and HLA-C*07:02 alleles by NetMHCpan 4.0 and immunogenicity was predicted by iPred.\n\nImmunogenicity 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-MONDO

LitCovid-PD-CLO

LitCovid-PD-CHEBI

LitCovid-PD-HP

{"project":"LitCovid-PD-HP","denotations":[{"id":"T1","span":{"begin":9325,"end":9330},"obj":"Phenotype"}],"attributes":[{"id":"A1","pred":"hp_id","subj":"T1","obj":"http://purl.obolibrary.org/obo/HP_0002664"}],"text":"Results\n\nIdentification of 28 exact matches to SARS CoV immunogenic peptides by screening all epitopes deposited in IEDB\nWe collected all peptides in IEDB ( 3, as of 13-02-2020) reported positive in T cell assays and have human as the host organism. We then conducted a local sequence alignment of 10 2019-nCoV open reading frames (ORFs) against 35,225 IEDB peptides, and found 28 exact matches. Surprisingly, all identical hits (against peptides having sequence length greater than 3) were from SARS-CoV ( Table 1, Data Table 1 4). These peptides have been shown to bind various HLA alleles, although with higher tendency towards HLA-A:02:01, from both class I and class II, and can be target for CD8+ and CD4+ T cells respectively.\nTable 1. 28 2019-nCoV peptides having exact matches with immunogenic SARS-CoV peptides.\nIEDB.peptide 2019-nCoV.pattern Antigen.Name Allele.Name\nTLACFVLAAV TLACFVLAAV Membrane glycoprotein HLA-A *02:01\nAFFGMSRIGMEVTPSGTW AFFGMSRIGMEVTPSGTW N protein\nALNTPKDHI ALNTPKDHI Nucleoprotein HLA-A *02:01\nAQFAPSASAFFGMSR AQFAPSASAFFGMSR nucleocapsid protein HLA class II\nAQFAPSASAFFGMSRIGM AQFAPSASAFFGMSRIGM N protein\nGMSRIGMEV GMSRIGMEV Nucleoprotein HLA-A *02:01\nILLNKHIDA ILLNKHIDA Nucleoprotein HLA-A *02:01\nIRQGTDYKHWPQIAQFA IRQGTDYKHWPQIAQFA N protein\nKHWPQIAQFAPSASAFF KHWPQIAQFAPSASAFF N protein\nLALLLLDRL LALLLLDRL Nucleoprotein HLA-A *02:01\nLLLDRLNQL LLLDRLNQL Nucleoprotein HLA-A *02:01\nLLNKHIDAYKTFPPTEPK LLNKHIDAYKTFPPTEPK N protein\nLQLPQGTTL LQLPQGTTL Nucleoprotein HLA-A *02:01\nRRPQGLPNNTASWFT RRPQGLPNNTASWFT nucleocapsid protein HLA class I\nYKTFPPTEPKKDKKKK YKTFPPTEPKKDKKKK N protein\nILLNKHID ILLNKHID Nucleoprotein HLA-A *02:01\nMEVTPSGTWL MEVTPSGTWL nucleocapsid protein HLA-B *40:01\nALNTLVKQL ALNTLVKQL S protein HLA-A *02:01\nFIAGLIAIV FIAGLIAIV Spike glycoprotein precursor HLA-A2\nLITGRLQSL LITGRLQSL Spike glycoprotein precursor HLA-A2\nNLNESLIDL NLNESLIDL S protein HLA-A *02:01\nQALNTLVKQLSSNFGAI QALNTLVKQLSSNFGAI S protein HLA-DRB1 *04:01\nRLNEVAKNL RLNEVAKNL Spike glycoprotein precursor HLA-A *02:01\nVLNDILSRL VLNDILSRL S protein HLA-A *02:01\nVVFLHVTYV VVFLHVTYV Spike glycoprotein precursor HLA-A *02:01\nGAALQIPFAMQMAYRF GAALQIPFAMQMAYRF S protein HLA-DRA *01:01/DRB1 *07:01\nMAYRFNGIGVTQNVLY MAYRFNGIGVTQNVLY S protein HLA-DRB1 *04:01\nQLIRAAEIRASANLAATK QLIRAAEIRASANLAATK S protein HLA-DRB1 *04:01\n*SARS-CoV: Severe acute respiratory syndrome coronavirus\n\nIdentification 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.\n\nDe novo search of immunogenic peptides in 2019-nCoV proteome\nAs a complementary reciprocal approach, we conducted a de novo search of immunogenic peptides against the 2019-nCov proteome sequence. We scanned 9-mers from 2019-nCoV proteome with a window of 9 amino acids and step length of 1 amino acid (9613 in total). The immunogenicity of 9-mer peptides were predicted using iPred and MHC presentation scores were gauged using NetMHCpan 4.0 6 for various HLA types. In this task, we focused on haplotypes common in Chinese and European populations, which include HLA-A*02:01, HLA-A*01:01, HLA-B*07:02, HLA-B*40:01 and HLA-C*07:02 alleles (Data Table 3 4).\nBased on MHC presentation and immunogenicity prediction, we detected 5 peptides predicted to bind 4 different HLA alleles of which 2 had strong immunogenicity scores ( Figure 3). For those 65 strong binders to 3 different HLA types, 39 had immunogenicity scores ≥ 0.5 ( Table 3). Collectively this analysis suggests a number of 9-mer immunogenic candidates for further experimental validation.\nTable 3. 2019-nCoV 9-mer peptides predicted to bind 4 different HLA alleles by NetMHCpan 4.0, and those predicted to bind ≥ 3 HLA alleles and immunogenicity score ≥ 0.9 by iPred.\nFor different alleles, 0 denotes non-binding and 1 denotes binding predicted for specific HLA allele.\nAntigen.epitope Imm.prob A0101.NB A0201.NB B0702.NB B4001.NB C0702.NB Total binding HLA\nVQMAPISAM 0.893938 0 1 1 1 1 4\nAMYTPHTVL 0.862427 0 1 1 1 1 4\nTLDSKTQSL 0.254998 1 1 1 0 1 4\nKVDGVVQQL 0.191786 1 1 1 0 1 4\nKVDGVDVEL 0.18632 1 1 1 0 1 4\nMADQAMTQM 0.991227 1 0 1 0 1 3\nLEAPFLYLY 0.983072 1 0 0 1 1 3\nRTAPHGHVM 0.972153 1 0 1 0 1 3\nIPFAMQMAY 0.961569 1 0 1 0 1 3\nFLTENLLLY 0.951715 1 1 0 0 1 3\nYLQPRTFLL 0.947743 1 1 0 0 1 3\nMMISAGFSL 0.941318 0 1 1 0 1 3\nATLPKGIMM 0.926603 1 0 1 0 1 3\nFigure 3. De novo search of 9-mer 2019-nCoV peptides with MHC presentation and immunogenicity potential.\nThe MHC binding was predicted for HLA-A*02:01, HLA-A*01:01, HLA-B*07:02, HLA-B*40:01 and HLA-C*07:02 alleles by NetMHCpan 4.0 and immunogenicity was predicted by iPred.\n\nImmunogenicity 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":"32269766-30357391-30420970","span":{"begin":157,"end":158},"obj":"30357391"},{"id":"32269766-30144804-30420971","span":{"begin":4037,"end":4039},"obj":"30144804"},{"id":"32269766-28978689-30420972","span":{"begin":7164,"end":7166},"obj":"28978689"},{"id":"32269766-31695703-30420973","span":{"begin":9612,"end":9614},"obj":"31695703"},{"id":"32269766-31695703-30420974","span":{"begin":10505,"end":10507},"obj":"31695703"},{"id":"32269766-1709722-30420975","span":{"begin":12090,"end":12091},"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":"Results\n\nIdentification of 28 exact matches to SARS CoV immunogenic peptides by screening all epitopes deposited in IEDB\nWe collected all peptides in IEDB ( 3, as of 13-02-2020) reported positive in T cell assays and have human as the host organism. We then conducted a local sequence alignment of 10 2019-nCoV open reading frames (ORFs) against 35,225 IEDB peptides, and found 28 exact matches. Surprisingly, all identical hits (against peptides having sequence length greater than 3) were from SARS-CoV ( Table 1, Data Table 1 4). These peptides have been shown to bind various HLA alleles, although with higher tendency towards HLA-A:02:01, from both class I and class II, and can be target for CD8+ and CD4+ T cells respectively.\nTable 1. 28 2019-nCoV peptides having exact matches with immunogenic SARS-CoV peptides.\nIEDB.peptide 2019-nCoV.pattern Antigen.Name Allele.Name\nTLACFVLAAV TLACFVLAAV Membrane glycoprotein HLA-A *02:01\nAFFGMSRIGMEVTPSGTW AFFGMSRIGMEVTPSGTW N protein\nALNTPKDHI ALNTPKDHI Nucleoprotein HLA-A *02:01\nAQFAPSASAFFGMSR AQFAPSASAFFGMSR nucleocapsid protein HLA class II\nAQFAPSASAFFGMSRIGM AQFAPSASAFFGMSRIGM N protein\nGMSRIGMEV GMSRIGMEV Nucleoprotein HLA-A *02:01\nILLNKHIDA ILLNKHIDA Nucleoprotein HLA-A *02:01\nIRQGTDYKHWPQIAQFA IRQGTDYKHWPQIAQFA N protein\nKHWPQIAQFAPSASAFF KHWPQIAQFAPSASAFF N protein\nLALLLLDRL LALLLLDRL Nucleoprotein HLA-A *02:01\nLLLDRLNQL LLLDRLNQL Nucleoprotein HLA-A *02:01\nLLNKHIDAYKTFPPTEPK LLNKHIDAYKTFPPTEPK N protein\nLQLPQGTTL LQLPQGTTL Nucleoprotein HLA-A *02:01\nRRPQGLPNNTASWFT RRPQGLPNNTASWFT nucleocapsid protein HLA class I\nYKTFPPTEPKKDKKKK YKTFPPTEPKKDKKKK N protein\nILLNKHID ILLNKHID Nucleoprotein HLA-A *02:01\nMEVTPSGTWL MEVTPSGTWL nucleocapsid protein HLA-B *40:01\nALNTLVKQL ALNTLVKQL S protein HLA-A *02:01\nFIAGLIAIV FIAGLIAIV Spike glycoprotein precursor HLA-A2\nLITGRLQSL LITGRLQSL Spike glycoprotein precursor HLA-A2\nNLNESLIDL NLNESLIDL S protein HLA-A *02:01\nQALNTLVKQLSSNFGAI QALNTLVKQLSSNFGAI S protein HLA-DRB1 *04:01\nRLNEVAKNL RLNEVAKNL Spike glycoprotein precursor HLA-A *02:01\nVLNDILSRL VLNDILSRL S protein HLA-A *02:01\nVVFLHVTYV VVFLHVTYV Spike glycoprotein precursor HLA-A *02:01\nGAALQIPFAMQMAYRF GAALQIPFAMQMAYRF S protein HLA-DRA *01:01/DRB1 *07:01\nMAYRFNGIGVTQNVLY MAYRFNGIGVTQNVLY S protein HLA-DRB1 *04:01\nQLIRAAEIRASANLAATK QLIRAAEIRASANLAATK S protein HLA-DRB1 *04:01\n*SARS-CoV: Severe acute respiratory syndrome coronavirus\n\nIdentification 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.\n\nDe novo search of immunogenic peptides in 2019-nCoV proteome\nAs a complementary reciprocal approach, we conducted a de novo search of immunogenic peptides against the 2019-nCov proteome sequence. We scanned 9-mers from 2019-nCoV proteome with a window of 9 amino acids and step length of 1 amino acid (9613 in total). The immunogenicity of 9-mer peptides were predicted using iPred and MHC presentation scores were gauged using NetMHCpan 4.0 6 for various HLA types. In this task, we focused on haplotypes common in Chinese and European populations, which include HLA-A*02:01, HLA-A*01:01, HLA-B*07:02, HLA-B*40:01 and HLA-C*07:02 alleles (Data Table 3 4).\nBased on MHC presentation and immunogenicity prediction, we detected 5 peptides predicted to bind 4 different HLA alleles of which 2 had strong immunogenicity scores ( Figure 3). For those 65 strong binders to 3 different HLA types, 39 had immunogenicity scores ≥ 0.5 ( Table 3). Collectively this analysis suggests a number of 9-mer immunogenic candidates for further experimental validation.\nTable 3. 2019-nCoV 9-mer peptides predicted to bind 4 different HLA alleles by NetMHCpan 4.0, and those predicted to bind ≥ 3 HLA alleles and immunogenicity score ≥ 0.9 by iPred.\nFor different alleles, 0 denotes non-binding and 1 denotes binding predicted for specific HLA allele.\nAntigen.epitope Imm.prob A0101.NB A0201.NB B0702.NB B4001.NB C0702.NB Total binding HLA\nVQMAPISAM 0.893938 0 1 1 1 1 4\nAMYTPHTVL 0.862427 0 1 1 1 1 4\nTLDSKTQSL 0.254998 1 1 1 0 1 4\nKVDGVVQQL 0.191786 1 1 1 0 1 4\nKVDGVDVEL 0.18632 1 1 1 0 1 4\nMADQAMTQM 0.991227 1 0 1 0 1 3\nLEAPFLYLY 0.983072 1 0 0 1 1 3\nRTAPHGHVM 0.972153 1 0 1 0 1 3\nIPFAMQMAY 0.961569 1 0 1 0 1 3\nFLTENLLLY 0.951715 1 1 0 0 1 3\nYLQPRTFLL 0.947743 1 1 0 0 1 3\nMMISAGFSL 0.941318 0 1 1 0 1 3\nATLPKGIMM 0.926603 1 0 1 0 1 3\nFigure 3. De novo search of 9-mer 2019-nCoV peptides with MHC presentation and immunogenicity potential.\nThe MHC binding was predicted for HLA-A*02:01, HLA-A*01:01, HLA-B*07:02, HLA-B*40:01 and HLA-C*07:02 alleles by NetMHCpan 4.0 and immunogenicity was predicted by iPred.\n\nImmunogenicity 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":"32269766-30357391-30420970","span":{"begin":157,"end":158},"obj":"30357391"},{"id":"32269766-30144804-30420971","span":{"begin":4037,"end":4038},"obj":"30144804"},{"id":"32269766-28978689-30420972","span":{"begin":7164,"end":7165},"obj":"28978689"},{"id":"32269766-31695703-30420973","span":{"begin":9612,"end":9613},"obj":"31695703"},{"id":"32269766-31695703-30420974","span":{"begin":10505,"end":10506},"obj":"31695703"},{"id":"32269766-1709722-30420975","span":{"begin":12090,"end":12091},"obj":"1709722"}],"text":"Results\n\nIdentification of 28 exact matches to SARS CoV immunogenic peptides by screening all epitopes deposited in IEDB\nWe collected all peptides in IEDB ( 3, as of 13-02-2020) reported positive in T cell assays and have human as the host organism. We then conducted a local sequence alignment of 10 2019-nCoV open reading frames (ORFs) against 35,225 IEDB peptides, and found 28 exact matches. Surprisingly, all identical hits (against peptides having sequence length greater than 3) were from SARS-CoV ( Table 1, Data Table 1 4). These peptides have been shown to bind various HLA alleles, although with higher tendency towards HLA-A:02:01, from both class I and class II, and can be target for CD8+ and CD4+ T cells respectively.\nTable 1. 28 2019-nCoV peptides having exact matches with immunogenic SARS-CoV peptides.\nIEDB.peptide 2019-nCoV.pattern Antigen.Name Allele.Name\nTLACFVLAAV TLACFVLAAV Membrane glycoprotein HLA-A *02:01\nAFFGMSRIGMEVTPSGTW AFFGMSRIGMEVTPSGTW N protein\nALNTPKDHI ALNTPKDHI Nucleoprotein HLA-A *02:01\nAQFAPSASAFFGMSR AQFAPSASAFFGMSR nucleocapsid protein HLA class II\nAQFAPSASAFFGMSRIGM AQFAPSASAFFGMSRIGM N protein\nGMSRIGMEV GMSRIGMEV Nucleoprotein HLA-A *02:01\nILLNKHIDA ILLNKHIDA Nucleoprotein HLA-A *02:01\nIRQGTDYKHWPQIAQFA IRQGTDYKHWPQIAQFA N protein\nKHWPQIAQFAPSASAFF KHWPQIAQFAPSASAFF N protein\nLALLLLDRL LALLLLDRL Nucleoprotein HLA-A *02:01\nLLLDRLNQL LLLDRLNQL Nucleoprotein HLA-A *02:01\nLLNKHIDAYKTFPPTEPK LLNKHIDAYKTFPPTEPK N protein\nLQLPQGTTL LQLPQGTTL Nucleoprotein HLA-A *02:01\nRRPQGLPNNTASWFT RRPQGLPNNTASWFT nucleocapsid protein HLA class I\nYKTFPPTEPKKDKKKK YKTFPPTEPKKDKKKK N protein\nILLNKHID ILLNKHID Nucleoprotein HLA-A *02:01\nMEVTPSGTWL MEVTPSGTWL nucleocapsid protein HLA-B *40:01\nALNTLVKQL ALNTLVKQL S protein HLA-A *02:01\nFIAGLIAIV FIAGLIAIV Spike glycoprotein precursor HLA-A2\nLITGRLQSL LITGRLQSL Spike glycoprotein precursor HLA-A2\nNLNESLIDL NLNESLIDL S protein HLA-A *02:01\nQALNTLVKQLSSNFGAI QALNTLVKQLSSNFGAI S protein HLA-DRB1 *04:01\nRLNEVAKNL RLNEVAKNL Spike glycoprotein precursor HLA-A *02:01\nVLNDILSRL VLNDILSRL S protein HLA-A *02:01\nVVFLHVTYV VVFLHVTYV Spike glycoprotein precursor HLA-A *02:01\nGAALQIPFAMQMAYRF GAALQIPFAMQMAYRF S protein HLA-DRA *01:01/DRB1 *07:01\nMAYRFNGIGVTQNVLY MAYRFNGIGVTQNVLY S protein HLA-DRB1 *04:01\nQLIRAAEIRASANLAATK QLIRAAEIRASANLAATK S protein HLA-DRB1 *04:01\n*SARS-CoV: Severe acute respiratory syndrome coronavirus\n\nIdentification 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.\n\nDe novo search of immunogenic peptides in 2019-nCoV proteome\nAs a complementary reciprocal approach, we conducted a de novo search of immunogenic peptides against the 2019-nCov proteome sequence. We scanned 9-mers from 2019-nCoV proteome with a window of 9 amino acids and step length of 1 amino acid (9613 in total). The immunogenicity of 9-mer peptides were predicted using iPred and MHC presentation scores were gauged using NetMHCpan 4.0 6 for various HLA types. In this task, we focused on haplotypes common in Chinese and European populations, which include HLA-A*02:01, HLA-A*01:01, HLA-B*07:02, HLA-B*40:01 and HLA-C*07:02 alleles (Data Table 3 4).\nBased on MHC presentation and immunogenicity prediction, we detected 5 peptides predicted to bind 4 different HLA alleles of which 2 had strong immunogenicity scores ( Figure 3). For those 65 strong binders to 3 different HLA types, 39 had immunogenicity scores ≥ 0.5 ( Table 3). Collectively this analysis suggests a number of 9-mer immunogenic candidates for further experimental validation.\nTable 3. 2019-nCoV 9-mer peptides predicted to bind 4 different HLA alleles by NetMHCpan 4.0, and those predicted to bind ≥ 3 HLA alleles and immunogenicity score ≥ 0.9 by iPred.\nFor different alleles, 0 denotes non-binding and 1 denotes binding predicted for specific HLA allele.\nAntigen.epitope Imm.prob A0101.NB A0201.NB B0702.NB B4001.NB C0702.NB Total binding HLA\nVQMAPISAM 0.893938 0 1 1 1 1 4\nAMYTPHTVL 0.862427 0 1 1 1 1 4\nTLDSKTQSL 0.254998 1 1 1 0 1 4\nKVDGVVQQL 0.191786 1 1 1 0 1 4\nKVDGVDVEL 0.18632 1 1 1 0 1 4\nMADQAMTQM 0.991227 1 0 1 0 1 3\nLEAPFLYLY 0.983072 1 0 0 1 1 3\nRTAPHGHVM 0.972153 1 0 1 0 1 3\nIPFAMQMAY 0.961569 1 0 1 0 1 3\nFLTENLLLY 0.951715 1 1 0 0 1 3\nYLQPRTFLL 0.947743 1 1 0 0 1 3\nMMISAGFSL 0.941318 0 1 1 0 1 3\nATLPKGIMM 0.926603 1 0 1 0 1 3\nFigure 3. De novo search of 9-mer 2019-nCoV peptides with MHC presentation and immunogenicity potential.\nThe MHC binding was predicted for HLA-A*02:01, HLA-A*01:01, HLA-B*07:02, HLA-B*40:01 and HLA-C*07:02 alleles by NetMHCpan 4.0 and immunogenicity was predicted by iPred.\n\nImmunogenicity 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"}

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