Probability-Based Evaluation of Peptide and Protein Identifications from Tandem Mass Spectrometry and SEQUEST Analysis:  The Human Proteome

Abstract

Large-scale protein identifications from highly complex protein mixtures have recently been achieved using multidimensional liquid chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) and subsequent database searching with algorithms such as SEQUEST. Here, we describe a probability-based evaluation of false positive rates associated with peptide identifications from three different human proteome samples. Peptides from human plasma, human mammary epithelial cell (HMEC) lysate, and human hepatocyte (Huh)-7.5 cell lysate were separated by strong cation exchange (SCX) chromatography coupled offline with reversed-phase capillary LC-MS/MS analyses. The MS/MS spectra were first analyzed by SEQUEST, searching independently against both normal and sequence-reversed human protein databases, and the false positive rates of peptide identifications for the three proteome samples were then analyzed and compared. The observed false positive rates of peptide identifications for human plasma were significantly higher than those for the human cell lines when identical filtering criteria were used, suggesting that the false positive rates are significantly dependent on sample characteristics, particularly the number of proteins found within the detectable dynamic range. Two new sets of filtering criteria are proposed for human plasma and human cell lines, respectively, to provide an overall confidence of >95% for peptide identifications. The new criteria were compared, using a normalized elution time (NET) criterion (Petritis et al. Anal. Chem. 2003, 75, 1039-1048), with previously published criteria (Washburn et al. Nat. Biotechnol. 2001, 19, 242-247). The results demonstrate that the present criteria provide significantly higher levels of confidence for peptide identifications from mammalian proteomes without greatly decreasing the number of identifications.

A proteomic study of the HUPO Plasma Proteome Project's pilot samples using an accurate mass and time tag strategy

“…Subsequently, we determined that ~20% of all AMT tag peptide identifications and ~12% of those that were identified with high matched confidence had a value of >4 for Rsp. The false positive identification rate for peptide identifications was 32%; if the filter also contained the requirement for an Rsp <=4, the false-positive rate was 15% using the approach described in Qian et al 2005 [31].…”

“…Combining multidimensional analyses has been shown elsewhere to increase the completeness of a proteomic analysis [32,37,38]. Recent analyses of human plasma and other samples provided the basis for estimating the false-positives rates for SEQUEST results, although the filter rules were similar, but not identical to those used here for populating the PMT tag database [39,40]. The false-positive values for PMT identifications were 16-32%, depending on the calculation [39,40].…”

“…Recent analyses of human plasma and other samples provided the basis for estimating the false-positives rates for SEQUEST results, although the filter rules were similar, but not identical to those used here for populating the PMT tag database [39,40]. The false-positive values for PMT identifications were 16-32%, depending on the calculation [39,40]. In this analysis, using the same approach as used previously published [31] we calculated a false positive rate of 32% for the filters used here for the mass and time tag database.…”

“…The false-positive values for PMT identifications were 16-32%, depending on the calculation [39,40]. In this analysis, using the same approach as used previously published [31] we calculated a false positive rate of 32% for the filters used here for the mass and time tag database.…”

“…Blood serum and plasma are increasingly proving to be a more difficult to fully characterize with traditional proteomic technologies, as shown by both the HUPO and other efforts. Critical assumptions involving data filters and peptide identifications that have been used effectively in other proteomic efforts will likely need to be modified for plasma and other body fluids and tissues [39,51].…”