We present a spike-in experiment to evaluate the performance of software tools in detecting differentially abundant peptides by a label-free LC-MS method. The performance of each tool is assessed by the ability to detect and pick spike-in peptides as differences between groups. Tools that accurately identify the spike-in peptides are likely to be useful in biomarker discovery research.
Two datasets were generated from five serum samples obtained from five healthy individuals. The first dataset was derived from the five serum samples mixed with known concentration of spike-in peptides. The second dataset was obtained from the five serum samples alone. In the first dataset, nine MassPrep peptides (Table 1). The concentration of the MassPrep peptides (1 pmol/Ál) was selected based on best detection of the lowest concentration of MassPrep peptides available on the Qstar instrument (data not shown). Both datasets were acquired by LC-MS/MS on the hybrid Q-TOF mass spectrometer using the same acquisition parameters.
|Component Name||Molecular Weight (g/mol)||pKa||Peptide Sequence|
|RASG-1||1000.4938||9.34||RGDSPASSKP||Angiotensin frag 1-7||898.4661||7.35||DRVYIHP||Bradykinin||1059.5613||12.00||RPPGFSPFR||Angiotensin II||1045.5345||7.35||DRVYIHPF||Angiotensin I||1295.6775||7.51||DRVYIHPFHL||Renin substrate||1757.9253||7.61||DRVYIHPFHLLVYS||Enolase T35||1871.9604||7.34||WLTGPQLADLYHSLMK||Enolase T37||2827.2806||3.97||YPIVSIEDPFAEDDWEAWSHFFK||Melitin||2845.7381||12.06||GIGAVLKVLTTGLPALISWIKRKRQQ|