Background: Detecting multiple viruses in biofluids such as saliva remains challenging because of rapid mutation and low abundance presenting in the complex matrix. Proteomics analysis provides a complementary method by directly measuring viral peptides and resolving protein-level sequence variation in rapidly evolving viruses such as influenza and SARS-CoV-2. However, the ongoing viral evolution challenges search sensitivity by introducing numerous mutations. Here, we develop a saliva viral proteomics strategy that integrates mutation profiling of clinically relevant viral sequences with single amino acid substitution mutation discovery to enable sensitive detection of both known and emerging variants. Results: In silico analysis of SARS-CoV-2 and influenza viral sequences reveals bimodal prevalence distributions of mutated peptides. For mutation profiling of clinically relevant viral sequences, we prioritize high-prevalence peptides, which presumably have higher clinical significance, within a 3-month timespan. Applying a 10% prevalence threshold reduces database size by more than 82.1% for SARS-CoV-2 and 94.5% for influenza. Optimized mutated peptide databases cause <0.8% sensitivity loss compared with searches using viral reference sequences alone, while still covering 97.8% of SARS-CoV-2 and 98.4% of influenza viral populations in the subsequent month. G-PTM-D-based mutation discovery further mitigates the loss of database coverage and enables detection of new variants. In peptide-spiked saliva samples, G-PTM-D detected single-amino-acid variant peptides with femtogram-level sensitivity, outperforming conventional PTM searches and de novo sequencing. Conclusions: This study provides a scalable framework for mutation profiling and discovery proteomics analysis that enables sensitive detection of both known and emerging viral variants in complex clinical samples.
Zhang, Y., Shortreed, M., Timperman, A. T.
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