A Bayesian approach to peptide identification using Accurate Mass and Time tags from LC-FTICR-MS proteomics experiments

In high-throughput proteomics, one promising approach presently being explored is the Accurate Mass and Time (AMT) tag approach, in which reversed-phase liquid chromatography coupled to high accuracy mass spectrometry provide measurements of both the masses and chromatographic retention times of tryptic peptides in complex mixtures. These measurements are matched to the mass and predicted retention times of peptides in library. There are two varieties of peptides in the library: peptides whose retention time predictions are derived from previous peptide identifications and therefore are of high precision, and peptides whose retention time predictions are derived from a sequence-based model and therefore have lower precision. We present a Bayesian statistical model that provides probability estimates for the correctness of each match by separately modeling the data distributions of correct matches and incorrect matches. For matches to peptides with high-precision retention time predictions, the model distinguishes correct matches from incorrect matches with high confidence. For matches to peptides having low-precision retention time predictions, match probabilities do not approach certainty; however, even moderate probability matches may provide biologically interesting findings, motivating further investigations.