Retention time and fragmentation predictors increase confidence in variant peptide identification

Abstract

Precision medicine focuses on adapting care to the individual profile of patients, e.g. accounting for their unique genetic makeup. Being able to account for the effect of genetic variation on the proteome holds great promises towards this goal. However, identifying the protein products of genetic variation using mass spectrometry has proven very challenging. Here we show that the identification of variant peptides can be improved by the integration of retention time and fragmentation predictors into a unified proteogenomic pipeline. By combining these intrinsic peptide characteristics using the search-engine post-processor Percolator, we demonstrate improved discrimination power between correct and incorrect peptide-spectrum matches. Our results demonstrate that the drop in performance that is induced when expanding a protein sequence database can be compensated, and hence enabling efficient identification of genetic variation products in proteomics data. We anticipate that this enhancement of proteogenomic pipelines can provide a more refined picture of the unique proteome of patients, and thereby contribute to improving patient care.

Development of novel peptides filtering algorithm based on peptide information

“…Peptide retention time, the point in time at which a peptide elutes from a liquid chromatography column as recorded by the instrument, is an intrinsic characteristic of peptides and it is not used for peptide identification and FDR estimation. If the RT of all peptides can be accurately predicted, the predicted RT of peptides in each PSM can be compared with the observed true RT associated with the spectra, and the difference between the two can be used as a valid quantitative metric for assessing variant peptides [10][11] .…”