Combining de novo peptide sequencing algorithms, a synergistic approach to boost both identifications and confidence in bottom-up proteomics

ABSTRACT: Complex MS-based proteomics datasets are usually analyzed by protein database-searches. While this approach performs considerably well for sequenced organisms, direct inference of peptide sequences from tandem mass spectra, i.e. de novo peptide sequencing, oftentimes is the only way to obtain information when protein databases are absent. However, available algorithms suffer from drawbacks such as lack of validation and often high rates of false positive hits (FP). Here we present a simple method of combining results from commonly available de novo peptide sequencing algorithms, which in conjunction with minor tweaks in data acquisition ensues lower empirical FDR compared to the analysis using single algorithms. Results were validated using state-of-the art database search algorithms as well specifically synthesized reference peptides. Thus, we could increase the number of PSMs meeting a stringent FDR of 5% more than threefold compared to the single best de novo sequencing algorithm alone, accounting for an average of 11,120 PSMs (combined) instead of 3,476 PSMs (alone) in triplicate 2 h LC-MS runs of tryptic HeLa digestion.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human) Saccharomyces Cerevisiae (baker's Yeast) Radix Auricularia Mus Musculus (mouse)

TISSUE(S): Cell Culture, Whole Body, Hela Cell

SUBMITTER: Bernhard Blank-Landeshammer  

LAB HEAD: Prof. Dr. Albert Sickmann

PROVIDER: PXD005280 | Pride | 2019-09-25

REPOSITORIES: Pride