Using 10,000 Fragment Ions to Inform Scoring in Native Top-down Proteomics

• Published in: Journal of the American Society for Mass Spectrometry 2020-07, Vol.31 (7), p.1398-1409
• Main Authors: Ives, Ashley N, Su, Taojunfeng, Durbin, Kenneth R, Early, Bryan P, dos Santos Seckler, Henrique, Fellers, Ryan T, LeDuc, Richard D, Schachner, Luis F, Patrie, Steven M, Kelleher, Neil L
• Format: Article
• Language: English
• Subjects: Animals; Cell Line; Databases, Protein; Humans; Ions - analysis; Ions - chemistry; Mass Spectrometry; Mice; Peptide Fragments - analysis; Peptide Fragments - chemistry; Proteome - analysis; Proteome - chemistry; Proteomics - methods
• ISSN: 1044-0305; 1879-1123
• DOI: 10.1021/jasms.0c00026

Protein fragmentation is a critical component of top-down proteomics, enabling gene-specific protein identification and full proteoform characterization. The factors that influence protein fragmentation include precursor charge, structure, and primary sequence, which have been explored extensively for collision-induced dissociation (CID). Recently, noticeable differences in CID-based fragmentation were reported for native versus denatured proteins, motivating the need for scoring metrics that are tailored specifically to native top-down mass spectrometry (nTDMS). To this end, position and intensity were tracked for 10,252 fragment ions produced by higher-energy collisional dissociation (HCD) of 159 native monomers and 70 complexes. We used published structural data to explore the relationship between fragmentation and protein topology and revealed that fragmentation events occur at a large range of relative residue solvent accessibility. Additionally, our analysis found that fragment ions at sites with an N-terminal aspartic acid or a C-terminal proline make up on average 40 and 27%, respectively, of the total matched fragment ion intensity in nTDMS. Percent intensity contributed by each amino acid was determined and converted into weights to (1) update the previously published C-score and (2) construct a native Fragmentation Propensity Score. Both scoring systems showed an improvement in protein identification or characterization in comparison to traditional methods and overall increased confidence in results with fewer matched fragment ions but with high probability nTDMS fragmentation patterns. Given the rise of nTDMS as a tool for structural mass spectrometry, we forward these scoring metrics as new methods to enhance analysis of nTDMS data.