{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T08:47:24Z","timestamp":1768553244285,"version":"3.49.0"},"posted":{"date-parts":[[2021,2,12]]},"group-title":"Infectious Diseases (except HIV\/AIDS)","reference-count":64,"publisher":"openRxiv","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2021,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>The Coronavirus Disease 2019 (COVID-19) global pandemic has had a profound, lasting impact on the world\u2019s population. A key aspect to providing care for those with COVID-19 and checking its further spread is early and accurate diagnosis of infection, which has been generally done via methods for amplifying and detecting viral RNA molecules. Detection and quantitation of peptides using targeted mass spectrometry-based strategies has been proposed as an alternative diagnostic tool due to direct detection of molecular indicators from non-invasively collected samples as well as the potential for high-throughput analysis in a clinical setting; many studies have revealed the presence of viral peptides within easily accessed patient samples. However, evidence suggests that some viral peptides could serve as better indicators of COVID-19 infection status than others, due to potential misidentification of peptides derived from human host proteins, poor spectral quality, high limits of detection etc. In this study we have compiled a list of 639 peptides identified from Sudden Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) samples, including from in vitro and clinical sources. These datasets were rigorously analyzed using automated, Galaxy-based workflows containing tools such as PepQuery, BLAST-P, and the Multi-omic Visualization Platform as well as the open-source tools MetaTryp and Proteomics Data Viewer (PDV). Using PepQuery for confirming peptide spectrum matches, we were able to narrow down the 639 peptide possibilities to 87 peptides which were most robustly detected and specific to the SARS-CoV-2 virus. The specificity of these sequences to coronavirus taxa was confirmed using Unipept and BLAST-P. Applying stringent statistical scoring thresholds, combined with manual verification of peptide spectrum match quality, 4 peptides derived from the nucleocapsid phosphoprotein and membrane protein were found to be most robustly detected across all cell culture and clinical samples, including those collected non-invasively. We propose that these peptides would be of the most value for clinical proteomics applications seeking to detect COVID-19 from a variety of sample types. We also contend that samples taken from the upper respiratory tract and oral cavity have the highest potential for diagnosis of SARS-CoV-2 infection from easily collected patient samples using mass spectrometry-based proteomics assays.<\/jats:p>","DOI":"10.1101\/2021.02.09.21251427","type":"posted-content","created":{"date-parts":[[2021,2,12]],"date-time":"2021-02-12T11:47:10Z","timestamp":1613130430000},"source":"Crossref","is-referenced-by-count":0,"title":["A rigorous evaluation of optimal peptide targets for MS-based clinical diagnostics of Coronavirus Disease 2019 (COVID-19)"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5119-8075","authenticated-orcid":false,"given":"Andrew T.","family":"Rajczewski","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9818-0537","authenticated-orcid":false,"given":"Subina","family":"Mehta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dinh Duy An","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3079-6586","authenticated-orcid":false,"given":"Bj\u00f6rn A.","family":"Gr\u00fcning","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2590-4765","authenticated-orcid":false,"given":"James E.","family":"Johnson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0391-0600","authenticated-orcid":false,"given":"Thomas","family":"McGowan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6801-2559","authenticated-orcid":false,"given":"Timothy J.","family":"Griffin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0984-0973","authenticated-orcid":false,"given":"Pratik D.","family":"Jagtap","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2021030309250411000_2021.02.09.21251427v2.1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa2001017"},{"key":"2021030309250411000_2021.02.09.21251427v2.2","unstructured":"Nuzzo, J. , Moss, W. , Kahn, J. , Rutgow, L. , al., e. 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