Artificial Decoy Spectral Libraries for False Discovery Rate Estimation in Spectral Library Searching in Proteomics

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Abstract

The challenge of estimating false discovery rates (FDR) in peptide identification from MS/MS spectra has received increased attention in proteomics. The simple approach of target-decoy searching has become popular with traditional sequence (database) searching methods, but has yet to be practiced in spectral (library) searching, an emerging alternative to sequence searching. We extended this target-decoy searching approach to spectral searching by developing and validating a robust method to generate realistic, but unnatural, decoy spectra. Our method involves randomly shuffling the peptide identification of each reference spectrum in the library, and repositioning each fragment ion peak along the m/z axis to match the fragment ions expected from the shuffled sequence. We show that this method produces decoy spectra that are sufficiently realistic, such that incorrect identifications are equally likely to match real and decoy spectra, a key assumption necessary for decoy counting. This approach has been implemented in the open-source library building software, SpectraST.

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