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.