Supplementary MaterialsAdditional file 1: Desk S1. Coverage. Desk of all predicted HLA-I binders and their associated allele coverage, including additional indicators for overlap with the human proteome or overlap with the ViPR dataset used. (CSV 1448 kb) 13073_2020_767_MOESM4_ESM.csv (1.4M) GUID:?5C6CD3E2-48A5-45B7-8D0D-365EE6AEEF97 Additional file 5: Table S5. Broadly Binding HLA-I Peptides. The top HLA-I predicted binders from each of the three SARS-CoV-2 proteins: Linderane spike, nucleocapsid and membrane with the Linderane broadest cumulative allele coverage. (CSV 19 kb) 13073_2020_767_MOESM5_ESM.csv (20K) GUID:?FF2CFE89-11C9-480D-8F6C-75B59E27CD43 Additional file 6: Table Linderane S6. SARS-CoV-2 25mers Ranked by HLA-II Populace Coverage. Table of all SARS-CoV-2-derived 25mers made up of at least Rabbit Polyclonal to CDON 3 predicted HLA-II binders as subsequences. (CSV 1268 kb) 13073_2020_767_MOESM6_ESM.csv (1.2M) GUID:?B52B74E9-123F-44FB-AD45-AA075FBCE64A Additional file 7: Table S7. Broadly Binding HLA-II 25mers. The top HLA-II predicted binders from each of the three SARS-CoV-2 proteins: spike, nucleocapsid. (CSV 16 kb) 13073_2020_767_MOESM7_ESM.csv (17K) GUID:?D84B39B9-E2A4-4F32-82C4-7ADDD3055EF7 Additional file 8: Table S8. binding prediction of ViPR HLA-I epitopes. The peptide-HLA alleles pairs from the ViPR database which belong to the family and have a human host that had been scored using our HLA-I binding predictor. (CSV 522 kb) 13073_2020_767_MOESM8_ESM.csv (522K) GUID:?A931F951-2337-4BAA-96F3-13755EA60D03 Additional file 9: Table S9. binding prediction of ViPR HLA-II epitopes. The peptide-HLA alleles pairs from the ViPR database which belong to the family and have a human host that had been scored using our HLA-II Linderane binding predictor. (CSV 39 kb) 13073_2020_767_MOESM9_ESM.csv (39K) GUID:?06608A3C-B678-40CF-B8F9-311D0E572DE4 Additional file 10: Table S10. spectral counts from published SARS-CoV-2 proteomic datasets. MS/MS spectra assigned to peptides from SARS-CoV-2 proteins were tallied across datasets, divided by protein length, and normalized within each dataset to generate Fig. ?Fig.55. 13073_2020_767_MOESM10_ESM.xlsx (13K) GUID:?76B35265-DD92-4E5C-AF74-6F3C8A4261FE Additional file 11. Custom Python script for HLA-I. This Python scripts can be used to generate HLA-I supplementary tables. (PY 5 kb) 13073_2020_767_MOESM11_ESM.py (5.9K) GUID:?B0C55852-BA22-4FF5-B858-A216DA390CF2 Additional file 12. Custom Python script for HLA-II. This Python scripts can be used to generate HLA-II related supplementary tables. (PY 8 kb) 13073_2020_767_MOESM12_ESM.py (8.0K) GUID:?907CFEB1-D812-4405-9FD4-EC7121C1B14F Additional file 13. Custom R script for physique plotting. This R script can be used to story the precision-recall analyses from Fig. ?Fig.1,1, reproduce Fig. ?Fig.5,5, also to make Table ?Desk1.1. (R 9 kb) 13073_2020_767_MOESM13_ESM.r (9.2K) GUID:?3B8550C8-AE5E-4427-94CE-81E5E4FF2560 Data Availability StatementAll data generated or analyzed in this scholarly research are one of them posted content, its supplementary information data files, or the next exterior sources: SARS-CoV-2 reference sequences found in this research were extracted from GenBank (accession: “type”:”entrez-nucleotide”,”attrs”:”text”:”NC_045512.2″,”term_id”:”1798174254″,”term_text”:”NC_045512.2″NC_045512.2, https://www.ncbi.nlm.nih.gov/nuccore/”type”:”entrez-nucleotide”,”attrs”:”text”:”NC_045512″,”term_id”:”1798174254″,”term_text”:”NC_045512″NC_045512) and ORF9b, as annotated by UniProt (“type”:”entrez-protein”,”attrs”:”text”:”P0DTD2″,”term_id”:”1835921959″,”term_text”:”P0DTD2″P0DTD2, https://www.uniprot.org/proteomes/UP000464024). The technique has been referred to comprehensive in Abelin et al., Immunity 2017 [33] and Abelin et al., Immunity 2019 [34]. Analogous on the web tools towards the types deployed listed below are available at (a) http://hlathena.tools/ for HLA I epitopes which is maintained by the Broad institute, and described in Sarkizova et al., Nature Biotechnology 2019 alongside with the associated data [65], and (b) https://neonmhc2.org/ for HLA II epitopes [34]. Mono-allelic MS data utilized for the training of our HLA-II binding predictor is also available as part of Abelin et al., Immunity 2019 [34]. SARS-CoV-2 proteomic datasets were downloaded from your PRIDE repository (Bojkova et al. [40]: PXD017710, https://www.ebi.ac.uk/pride/archive/projects/PXD017710; Bezstarosti et al. [41]: PXD018760, https://www.ebi.ac.uk/pride/archive/projects/PXD018760; Davidson et al. [42]: PXD018241, https://www.ebi.ac.uk/pride/archive/projects/PXD018241). Custom R and Python scripts used in generation of supplementary desks and statistics are included (Extra data files 11, 12 and 13). Abstract History The ongoing COVID-19 pandemic has generated an urgency to recognize novel vaccine goals for defensive immunity against SARS-CoV-2. Early reports identify defensive roles for both cell-mediated and humoral immunity for SARS-CoV-2. Strategies We Linderane leveraged our bioinformatics binding prediction equipment for individual leukocyte antigen (HLA)-I and HLA-II alleles which were created using mass spectrometry-based profiling of specific HLA-I and HLA-II alleles to anticipate peptide binding to different allele pieces. We used these binding predictors to viral genomes in the family and particularly centered on T cell epitopes from SARS-CoV-2 protein. We assayed a subset of the epitopes within a T cell induction assay because of their ability to.