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In silico identification of vaccine targets for 2019-nCoV [version 2; peer review: 3 approved] Abstract Background: The newly identified coronavirus known as 2019-nCoV has posed a serious global health threat. According to the latest report (18-February-2020), it has infected more than 72,000 people globally and led to deaths of more than 1,016 people in China. Methods: The 2019 novel coronavirus proteome was aligned to a curated database of viral immunogenic peptides. The immunogenicity of detected peptides and their binding potential to HLA alleles was predicted by immunogenicity predictive models and NetMHCpan 4.0. Results: We report in silico identification of a comprehensive list of immunogenic peptides that can be used as potential targets for 2019 novel coronavirus (2019-nCoV) vaccine development. First, we found 28 nCoV peptides identical to Severe acute respiratory syndrome-related coronavirus (SARS CoV) that have previously been characterized immunogenic by T cell assays. Second, we identified 48 nCoV peptides having a high degree of similarity with immunogenic peptides deposited in The Immune Epitope Database (IEDB). Lastly, we conducted a de novo search of 2019-nCoV 9-mer peptides that i) bind to common HLA alleles in Chinese and European population and ii) have T Cell Receptor (TCR) recognition potential by positional weight matrices and a recently developed immunogenicity algorithm, iPred, and identified in total 63 peptides with a high immunogenicity potential. Conclusions: Given the limited time and resources to develop vaccine and treatments for 2019-nCoV, our work provides a shortlist of candidates for experimental validation and thus can accelerate development pipeline. Introduction The emergence and rapid spread of the recent novel coronavirus known as 2019-nCoV has posed a serious global health threat 1 and has already caused a huge financial burden 2. It has further challenged the scientific and industrial community for quick control practices, and equally importantly to develop effective vaccines to prevent its recurrence. In facing a rapid epidemical outbreak to a novel and unknown pathogen, a key bottleneck for a proper and deep investigation, which is fundamental for vaccine development, is the limited -- to almost no -- access of the scientific community to samples from infected subjects. As such, in silico predictions of targets for vaccines are of high importance and can serve as a guidance to medical and experimental experts for the best and timely use of the limited resources. In this regard, we report our recent effort to computationally identify immunogenic and/or cross-reactive peptides from 2019-nCoV. We provide a detailed screen of candidate peptides based on comparison with immunogenic peptides deposited in the Immune Epitope Database and Analysis Resource (IEDB) database including those derived from Severe acute respiratory syndrome-related coronavirus (SARS CoV) along with de novo prediction from 2019-nCoV 9-mer peptides. Here, we found i) 28 SARS-derived peptides having exact matches in 2019-nCoV proteome previously characterized to be immunogenic by in vitro T cell assays, ii) 22 nCoV peptides having a high sequence similarity with immunogenic peptides but with a greater predicted immunogenicity score, and iii) 44 + 19 nCoV peptides predicted to be immunogenic by the iPred algorithm and 1G4 TCR positional weight matrices respectively. Results Identification of 28 exact matches to SARS CoV immunogenic peptides by screening all epitopes deposited in IEDB We 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. Table 1. 28 2019-nCoV peptides having exact matches with immunogenic SARS-CoV peptides. IEDB.peptide 2019-nCoV.pattern Antigen.Name Allele.Name TLACFVLAAV TLACFVLAAV Membrane glycoprotein HLA-A *02:01 AFFGMSRIGMEVTPSGTW AFFGMSRIGMEVTPSGTW N protein ALNTPKDHI ALNTPKDHI Nucleoprotein HLA-A *02:01 AQFAPSASAFFGMSR AQFAPSASAFFGMSR nucleocapsid protein HLA class II AQFAPSASAFFGMSRIGM AQFAPSASAFFGMSRIGM N protein GMSRIGMEV GMSRIGMEV Nucleoprotein HLA-A *02:01 ILLNKHIDA ILLNKHIDA Nucleoprotein HLA-A *02:01 IRQGTDYKHWPQIAQFA IRQGTDYKHWPQIAQFA N protein KHWPQIAQFAPSASAFF KHWPQIAQFAPSASAFF N protein LALLLLDRL LALLLLDRL Nucleoprotein HLA-A *02:01 LLLDRLNQL LLLDRLNQL Nucleoprotein HLA-A *02:01 LLNKHIDAYKTFPPTEPK LLNKHIDAYKTFPPTEPK N protein LQLPQGTTL LQLPQGTTL Nucleoprotein HLA-A *02:01 RRPQGLPNNTASWFT RRPQGLPNNTASWFT nucleocapsid protein HLA class I YKTFPPTEPKKDKKKK YKTFPPTEPKKDKKKK N protein ILLNKHID ILLNKHID Nucleoprotein HLA-A *02:01 MEVTPSGTWL MEVTPSGTWL nucleocapsid protein HLA-B *40:01 ALNTLVKQL ALNTLVKQL S protein HLA-A *02:01 FIAGLIAIV FIAGLIAIV Spike glycoprotein precursor HLA-A2 LITGRLQSL LITGRLQSL Spike glycoprotein precursor HLA-A2 NLNESLIDL NLNESLIDL S protein HLA-A *02:01 QALNTLVKQLSSNFGAI QALNTLVKQLSSNFGAI S protein HLA-DRB1 *04:01 RLNEVAKNL RLNEVAKNL Spike glycoprotein precursor HLA-A *02:01 VLNDILSRL VLNDILSRL S protein HLA-A *02:01 VVFLHVTYV VVFLHVTYV Spike glycoprotein precursor HLA-A *02:01 GAALQIPFAMQMAYRF GAALQIPFAMQMAYRF S protein HLA-DRA *01:01/DRB1 *07:01 MAYRFNGIGVTQNVLY MAYRFNGIGVTQNVLY S protein HLA-DRB1 *04:01 QLIRAAEIRASANLAATK QLIRAAEIRASANLAATK S protein HLA-DRB1 *04:01 *SARS-CoV: Severe acute respiratory syndrome coronavirus Identification of 22 2019-nCoV peptides with high degree of similarity to previously reported immunogenic viral peptides In 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. Figure 1. 2019-nCoV peptides with high sequence similarity to immunogenic peptides in IEDB. A. 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. In 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). We 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. Table 2. List of 22 2019-nCoV peptides having a higher predicted immunogenicity score than their target peptides. IEDB.peptide 2019-nCoV.pattern IEDB.prob nCol.prob WYMWLGARY WYIWLG 0.999249 0.999441 GLMWLSYFV GLMWLSYFI 0.995073 0.998216 GLVFLCLQY GIVFMCVEY 0.98123 0.984127 TWLTYHGAIKLDDKDPQFKDNVILL TWLTYTGAIKLDDKDPNFKDQVILL 0.925862 0.975242 IGMEVTPSGTWLTYH IGMEVTPSGTWLTY 0.903518 0.919184 GETALALLLLDRLNQ GDAALALLLLDRLNQ 0.853114 0.900655 TPSGTWLTYHGAIKL TPSGTWLTYTGAIKL 0.620894 0.662417 SIVAYTMSL SIIAYTMSL 0.589694 0.693763 RRPQGLPNNIASWFT RRPQGLPNNTASWFT 0.533253 0.584355 YNLKWN YNL-WN 0.520244 0.765309 AGCLIGAEHVDTSYECDI AGCLIGAEHVNNSYECDI 0.503905 0.56813 GFMKQYGECLGDINARDL GFIKQYGDCLGDIAARDL 0.471939 0.506817 ANKEGIVWVATEGAL ANKDGIIWVATEGAL 0.367723 0.404796 WNPDDY WNADLY 0.355018 0.584726 PDDYGG PDDFTG 0.334887 0.527287 TWLTYHGAIKLDDKDPQF TWLTYTGAIKLDDKDPNF 0.27017 0.529675 DEVNQI DEVRQI 0.18504 0.187797 SSKRFQPFQQFGRDV SNKKFLPFQQFGRDI 0.098384 0.119472 NHDSPDAEL NHTSPDVDL 0.067808 0.17889 TKQYNVTQAF TKAYNVTQAF 0.054818 0.171488 VKQMYKTPTLKYFGGFNF VKQIYKTPPIKDFGGFNF 0.018685 0.135681 QKRTATKQYNVTQAF QKRTATKAYNVTQAF 0.004891 0.037776 Figure 2. Predicted immunogenicity for IEDB immunogenic vs. 2019-nCoV peptides. 2019-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 < 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. De novo search of immunogenic peptides in 2019-nCoV proteome As 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). Based 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. Table 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. For different alleles, 0 denotes non-binding and 1 denotes binding predicted for specific HLA allele. Antigen.epitope Imm.prob A0101.NB A0201.NB B0702.NB B4001.NB C0702.NB Total binding HLA VQMAPISAM 0.893938 0 1 1 1 1 4 AMYTPHTVL 0.862427 0 1 1 1 1 4 TLDSKTQSL 0.254998 1 1 1 0 1 4 KVDGVVQQL 0.191786 1 1 1 0 1 4 KVDGVDVEL 0.18632 1 1 1 0 1 4 MADQAMTQM 0.991227 1 0 1 0 1 3 LEAPFLYLY 0.983072 1 0 0 1 1 3 RTAPHGHVM 0.972153 1 0 1 0 1 3 IPFAMQMAY 0.961569 1 0 1 0 1 3 FLTENLLLY 0.951715 1 1 0 0 1 3 YLQPRTFLL 0.947743 1 1 0 0 1 3 MMISAGFSL 0.941318 0 1 1 0 1 3 ATLPKGIMM 0.926603 1 0 1 0 1 3 Figure 3. De novo search of 9-mer 2019-nCoV peptides with MHC presentation and immunogenicity potential. The 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. Immunogenicity of 2019-nCoV peptides to 1G4 CD8+ TCR molecule While 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. T 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. Here, 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 > 0.8 and 516 peptides > 0.7. The 9-mer peptides with geometric mean > 0.7 and positive HLA-A*02:01 binding prediction by NetMHCpan 4.0 are listed in Table 4. Figure 4. Distribution of 1G4 TCR positional weight matrix scores for 2019-nCoV peptides. The positional weight matrices were obtained from 7 and 9613 9-mers generated from 10 2019-nCoV ORFs were computed for their TCR recognition potential. Table 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). Peptide Binding score Activation score Killing score geoMean Rank Binder RIMTWLDMV 0.866377428 0.853995 0.776303 0.831249 0.3481 SB ALNTLVKQL 0.802453741 0.75073 0.785957 0.779413 0.6159 WB LLLDRLNQL 0.809895414 0.7752 0.741096 0.774888 0.0423 SB MIAQYTSAL 0.766262499 0.789511 0.749477 0.768242 0.9238 WB VLSTFISAA 0.799672451 0.756117 0.687278 0.746239 0.536 WB NVLAWLYAA 0.761297552 0.686117 0.739944 0.728423 1.4457 WB RLANECAQV 0.783161706 0.719705 0.680504 0.726572 0.2049 SB KLLKSIAAT 0.748896679 0.708996 0.697463 0.718118 1.0923 WB QLSLPVLQV 0.70128376 0.715259 0.708405 0.708293 0.4768 SB VQMAPISAM 0.729320768 0.698514 0.689612 0.705612 1.4677 WB LLLTILTSL 0.7131709 0.715194 0.680064 0.702623 0.2712 SB SVLLFLAFV 0.736972762 0.690855 0.679534 0.70202 1.1449 WB LMWLIINLV 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 > 0.7 and 12 peptides > 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. Table 5. 2019-nCoV 9-mer peptides having leucine-valine in anchor positions. Peptides 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). Peptide Binding score Activation score Killing score geoMean Rank Binder TLMNVLTLV 0.723687 0.658986 0.652178 0.677534 0.0444 SB QLEMELTPV 0.711291 0.651003 0.608605 0.655625 1.6769 WB MLAKALRKV 0.668756 0.610664 0.65968 0.645854 0.3524 SB GLFKDCSKV 0.675952 0.632375 0.594753 0.633494 0.2677 SB ALSKGVHFV 0.652549 0.604952 0.586236 0.613954 0.0425 SB YLNTLTLAV 0.624147 0.610826 0.575445 0.603119 0.0453 SB Discussion In this study we provide a profile of computationally predicted immunogenic peptides from 2019-nCoV for functional validation and potential vaccine developments. We are fully aware that an effective vaccine development will require a very thorough investigation of immune correlates to 2019-nCoV. However, due to the emergency and severity of the outbreak as well as the lack of access to samples from infected subjects, such approaches would not serve the urgency. Therefore, computational prediction is instrumental for guiding biologists towards a quick and cost-effective solution to prevent the spread and ultimately help eliminate the infection from the individuals. With a rising global concern of novel coronavirus outbreak, numerous research groups have started to investigate and publish their findings. At the time of preparing this manuscript, we became aware of a similar study conducted in comparing 2019-nCoV proteome with SARS CoV immunogenic peptides 9. Our in silico approach takes the search beyond presenting only common immunogenic peptide between SARS and 2019-nCoV and provides the experimental community with a more comprehensive list including de novo and cross reactive candidates. On the other hand, considering the fact that two studies have been accomplished independently with distinct approaches, this serves to demonstrate a high level of confidence in reproducing the results. Reproducibility of computational prediction is always of high importance and becomes even more significant under urgent scenarios as of this outbreak. Our study also suggests the need for further efforts to develop accurate predictive models and algorithms for the characterization of immunogenic peptides. In this study, we provide potential immunogenic peptides from 2019-nCoV for vaccine targets that i) have been characterized immunogenic by previous studies on SARS CoV, ii) have high degree of similarity with immunogenic SARS CoV peptides and iii) are predicted immunogenic by combination of NetMHCpan and iPred/1G4 TCR positional weight matrices. Given the limited time and resources, our work serves as a guide to save time and cost for further experimental validation. Method Data acquisition 2019-nCoV open reading frame sequences were downloaded from NCBI ( MN908947.3). All sequences subjected for analysis are deposited in GitHub repository. Data analysis All subsequent analyses have been conducted in R 3.6.1. Sequence similarity comparison The sequence similarity between 2019-nCoV open reading frames and previously characterized immunogenic peptides in IEDB was analysed by local alignment using R ‘pairwiseAlignment’ function from Biostrings v2.40.2 package. The local alignment utilized BLOSUM62 matrix, gapOpening of 5 and gapExtension of 5. The alignment score was normalized by length of target peptides. In extracting peptides with the exact sequence alignment with epitope sequences in IEDB, only peptides with more than 3 amino acids in length were shortlisted. Immunogenicity prediction We have used iPred 5 to predict immunogenicity of each given peptide. Briefly, iPred employs peptides’ length and physicochemical properties of amino acids modelled by sums of ten Kidera factors and associates a score to each peptide reflecting its likelihood of recognition by a T cell. Predicting presentation by MHCs In order to predict peptide binding to MHC we used NetMHCpan V4 6. This version of NetMHCpan that comes with a number of improvements, incorporate both eluted ligand and peptide binding affinity data into a neural network model to predict MHC presentation of each given peptide. Predicting cross reactivity to 1G4 TCR To gauge the level of 1G4 TCR cross-reactivity to list of 2019-nCoV virus, we have leveraged the data from a recently published study 7. 1G4 or NY-ESO-1-specific TCR is a very well-studied and clinically efficacious TCR which recognize the peptide ‘SLLMWITQC’ presented by HLA-A*02:01. Karapetyan et al. have recently provided data from three experimental assays reflecting Binding, Activating and Killing upon each mutation at each position of all possible 9-mers using these three datasets. In a similar way to the original paper, we trained three Position Weight Matrices named B, A and K respectively from Binding, Activating and Killing assay. We defined the cross-reactivity score of a given 9-mer sequence as the geometric mean of B, A and K. We then scanned 2019-nCoV virus protein sequence with each of B, A and K PWMs and associated each of 9613 9-mers with a cross reactivity score. At the same we utilized NetMHCpan and associated each 9-mer with its presentation score. Our final list of cross-reactive candidate peptides were those with a cross-reactivity sore >= 0.8 and reported as strong binders from NetMHCpan and have ‘L’ and ‘V’ amino acids at anchor positions. The custom R codes are accessible from GitHub repository (see software availability 4). Software availability Replication code: https://github.com/ChloeHJ/Vaccine-target-for-2019-nCoV.git Archived source code at time of publication: http://doi.org/10.5281/zenodo.3676908 4 License: Creative Commons Attribution 4.0 International Data availability Source data 2019-nCoV open reading frame sequences were downloaded from NCBI ( MN908947.3). Underlying data Zenodo: In silico identification of vaccine targets for 2019-nCoV (Data tables). http://doi.org/10.5281/zenodo.3676886 10 This project contains the following underlying data: – Table1 nCoV peptides having exact match with immunogenic SARS CoV peptides.xlsx (Table of nCoV peptides having exact match with immunogenic SARS CoV peptides) – Table2 nCoV peptides with high sequence similarity with immunogenic IEDB peptides.csv (Table of peptides with high sequence similarity with immunogenic IEDB peptides) – Table3 de novo search on 9-mer nCoV for immunogenic peptides by NetMHCpan and iPred.csv (Table of results of de novo search on 9-mer nCoV for immunogenic peptides by NetMHCpan and iPred) – Table4 de novo search on 9-mer nCoV for immunogenic peptides by NetMHCpan and PWM.xlsx (Table of results of de novo search on 9-mer nCoV for immunogenic peptides by NetMHCpan and PWM) Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Acknowledgements We appreciate assistance and computing support from Human Immunology Unit and WIMM Centre for Computational Biology at MRC Weatherall Institute of Molecular Medicine. We thank G. Napolitani and M. Salio for insightful discussions about the project.

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