Data-Independent Acquisition Protease-Multiplexing Enables Increased Proteome Sequence Coverage Across Multiple Fragmentation Modes

• Published in: Journal of proteome research Vol. 21; no. 4; pp. 1124 - 1136
• Main Authors: Richards, Alicia L, Chen, Kuei-Ho, Wilburn, Damien B, Stevenson, Erica, Polacco, Benjamin J, Searle, Brian C, Swaney, Danielle L
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 01-04-2022
• Subjects: Amino Acid Sequence; Humans; Peptide Hydrolases - genetics; Peptides - chemistry; Proteome - genetics; Proteomics - methods; isoforms; DIA-MS; multiplexing; label-free quantification; proteases; CID
• ISSN: 1535-3893; 1535-3907
• DOI: 10.1021/acs.jproteome.1c00960

The use of multiple proteases has been shown to increase protein sequence coverage in proteomics experiments; however, due to the additional analysis time required, it has not been widely adopted in routine data-dependent acquisition (DDA) proteomic workflows. Alternatively, data-independent acquisition (DIA) has the potential to analyze multiplexed samples from different protease digests, but has been primarily optimized for fragmenting tryptic peptides. Here we evaluate a DIA multiplexing approach that combines three proteolytic digests (Trypsin, AspN, and GluC) into a single sample. We first optimize data acquisition conditions for each protease individually with both the canonical DIA fragmentation mode (beam type CID), as well as resonance excitation CID, to determine optimal consensus conditions across proteases. Next, we demonstrate that application of these conditions to a protease-multiplexed sample of human peptides results in similar protein identifications and quantitative performance as compared to trypsin alone, but enables up to a 63% increase in peptide detections, and a 45% increase in nonredundant amino acid detections. Nontryptic peptides enabled noncanonical protein isoform determination and resulted in 100% sequence coverage for numerous proteins, suggesting the utility of this approach in applications where sequence coverage is critical, such as protein isoform analysis.