Staged-probability strategy of processing shotgun proteomic data to discover more functionally important proteins.

Biologically important proteins related to membrane receptors, signal transduction, regulation, transcription, and translation are usually low in abundance and identified with low probability in mass spectroscopy (MS)-based analyses. Most valuable proteomics information on them were hitherto discarded due to the application of excessively strict data filtering for accurate identification. In this study, we present a staged-probability strategy for assessing proteomic data for potential functionally important protein clues. MS-based protein identifications from the second (L2) and third (L3) layers of the cascade affinity fractionation using the Trans-Proteomic Pipeline software were classified into three probability stages as 1.00-0.95, 0.95-0.50, and 0.50-0.20 according to their distinctive identification correctness rates (i.e. 100%-95%, 95%-50%, and 50%-20%, respectively). We found large data volumes and more functionally important proteins located at the previously unacceptable lower probability stages of 0.95-0.50 and 0.50-0.20 with acceptable correctness rate. More importantly, low probability proteins in L2 were verified to exist in L3. Together with some MS spectrogram examples, comparisons of protein identifications of L2 and L3 demonstrated that the staged-probability strategy could more adequately present both quantity and quality of proteomic information, especially for researches involving biomarker discovery and novel therapeutic target screening.