Abstract
The spike protein of SARS-CoV-2, the virus responsible for the global pandemic of COVID-19, is an abundant, heavily glycosylated surface protein that plays a key role in receptor binding and host cell fusion, and is the focus of all current vaccine development efforts. Variants of concern are now circulating worldwide that exhibit mutations in the spike protein. Protein sequence and glycosylation variations of the spike may affect viral fitness, antigenicity, and immune evasion. Global surveillance of the virus currently involves genome sequencing, but tracking emerging variants should include quantitative measurement of changes in site-specific glycosylation as well. In this work, we used data-dependent acquisition (DDA) and data-independent acquisition (DIA) mass spectrometry to quantitatively characterize the five N-linked glycosylation sites of the glycoprotein standard alpha-1-acid glycoprotein (AGP), as well as the 22 sites of the SARS-CoV-2 spike protein. We found that DIA compared favorably to DDA in sensitivity, resulting in more assignments of low-abundance glycopeptides. However, the reproducibility across replicates of DIA-identified glycopeptides was lower than that of DDA, possibly due to the difficulty of reliably assigning low-abundance glycopeptides confidently. The differences in the data acquired between the two methods suggest that DIA outperforms DDA in terms of glycoprotein coverage but that overall performance is a balance of sensitivity, selectivity, and statistical confidence in glycoproteomics. We assert that these analytical and bioinformatics methods for assigning and quantifying glycoforms would benefit the process of tracking viral variants as well as for vaccine development.