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.

Quantification of uncertainty of peptide retention time predictions from a sequence-based model in LC-MS/MS proteomics experiments.

In high-throughput mass spectrometry-based proteomics, it is necessary to employ separations to reduce sample complexity prior to mass spectrometric peptide identification. Interest has begun to focus on using information from separations to aid in peptide identification. One of the most common separations is reversed-phase liquid chromatography, in which peptides are separated on the basis of their chromatographic retention time. We apply a sequence-based model of peptide hydrophobicity to the problem of predicting peptide retention times, first fitting the model parameters using a large set of peptide identifications and then testing its predictions using a set of completely different peptide identifications. We demonstrate that not only does the model provide reasonably accurate predictions, it also provides a quantification of the uncertainty of its predictions. The model may therefore be used to provide checks on future tentative peptide identifications, even when the peptide species in question has never been observed before.

Quantitative proteomics analysis of the secretory pathway.

We report more than 1400 proteins of the secretory-pathway proteome and provide spatial information on the relative presence of each protein in the rough and smooth ER Golgi cisternae and Golgi-derived COPI vesicles. The data support a role for COPI vesicles in recycling and cisternal maturation, showing that Golgi-resident proteins are present at a higher concentration than secretory cargo. Of the 1400 proteins, 345 were identified as previously uncharacterized. Of these, 230 had their subcellular location deduced by proteomics. This study provides a comprehensive catalog of the ER and Golgi proteomes with insight into their identity and function.

Multicomponent internal recalibration of an LC-FTICR-MS analysis employing a partially characterized complex peptide mixture: systematic and random errors.

In high-throughput proteomics, a promising current approach is the use of liquid chromatography coupled to Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR-MS) of tryptic peptides from complex mixtures of proteins. To apply this method, it is necessary to account for any systematic measurement error, and it is useful to have an estimate of the random error expected in the measured masses. Here, we analyze by LC-FTICR-MS a complex mixture of peptides derived from a sample previously characterized by LC-QTOF-MS. Application of a Bayesian probability model of the data and partial knowledge of the composition of the sample suffice to estimate both the systematic and random errors in measured masses.

Multiple losses of neutral C14H14 in the tandem mass spectrometry of several perbenzyl ether intermediates in the synthesis of green tea constituents.

Electrospray and matrix-assisted laser desorption/ionization (MALDI) tandem mass spectrometry (MS/MS) experiments were used to investigate an unusual fragmentation in collision-induced dissociation (CID) of sodiated and potassiated perbenzyl ether intermediates obtained in the total synthesis of gallate ester constituents of green tea. Prominent fragments correspond to multiple sequential losses of neutral C14H14 that were not observed in the protonated and ammoniated species, that instead present fragment ion series in which members are separated by C7H6. High-resolution MALDI quadrupole time-of-flight (Q-TOF) and electrospray-Fourier transform mass spectrometry (FTMS) were used to confirm elemental compositions of these and related ions.