Mass fingerprinting of complex mixtures: protein inference from high-resolution peptide masses and predicted retention times

摘要

In typical shotgun experiments, the mass spectrometer records the masses of a large set of ionized analytes, but fragments only a fraction of them. In the subsequent analyses, only the fragmented ions are used to compile a set of peptide identifications, while the unfragmented ones are disregarded. In this work we show how the unfragmented ions, here denoted MS1-features, can be used to increase the confidence of the proteins identified in shotgun experiments. Specifically, we propose the usage of in silico tags, where the observed MS1-features are matched against de novo predicted masses and retention times for all the peptides derived from a sequence database. We present a statistical model to assign protein-level probabilities based on the MS1-features, and combine this data with the fragmentation spectra. Our approach was evaluated for two triplicate datasets from yeast and human, respectively, leading to up to 7% more protein identifications at a fixed protein-level false discovery rate of 1%. The additional protein identifications were validated both in the context of the mass spectrometry data, and by examining their estimated transcript levels generated using RNA-Seq. The proposed method is reproducible, straightforward to apply, and can even be used to re-analyze and increase the yield of existing datasets.Principle contributionA statistical framework that uses the unfragmented MS1-features to increase the confidence of the proteins identified in shotgun experiments.