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Estimation of false discovery rate (FDR) for identified peptides is an important step in large-scale proteomic studies. We introduced an empirical approach to the problem that is based on the FDR-like functions of sets of peptide spectral matches (PSMs). These functions have close values for equal-sized sets with the same FDR and depend monotonically on the FDR of a set. We have found three of them, based on three complementary sources of data: chromatography, mass spectrometry, and sequences of identified peptides. Using a calibration on a set of putative correct PSMs these functions were converted into the FDR scale. The approach was tested on a set of similar to 2800 PSMs obtained from rat kidney tissue. The estimates based on all three data sources were rather consistent with each other as well as with one made using the target-decoy strategy.

Milk as a key element for infant nutrition represents the only source of feeding for newborns and infants, breast-feeding milk normally contains several bioactive proteins or peptides useful for the development of the immune system that protects infants from diseases. Osteopontin(OPN) plays a distinct role during the processes of lactation. Some studies on OPN isolated and purified from the human milk via HPLC on SCX and C-4 columns adopted biological mass spectrometry. After digesting the purified OPN sample with trypsin, the typical correlative polypeptide fragments GDSVVYGLR and QNLLAPQTLPSK are identified by FT-ICR-MS. The rapid identification of OPN is assumed to be reasonably well behaving in a standard bottom-up experiment. It shows that nano-spray HPLC combined with FT-ICR-MS and Mascot search can be used as a high efficient method for the identification of purified OPN and its polypeptide fragments. Further study will provide more academic theories of their different modifications and the bioactivity as well.