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For example, metaproteomics researchers might find that a PSM identifies a peptide mapping to a specific species within a complex community of bacteria, and proteogenomics researchers might use a PSM to identify a novel disease-associated peptide carrying an amino acid sequence variation. These new applications, along with a steady interest in post-translational modification (PTM) identifications depend heavily on peptide-level identifications ( Figure 1). Recently, the emergence of new applications such as metaproteomics 1– 2 and proteogenomics 3– 4 has led to a new emphasis on the accuracy of PSMs 3, 5– 7. The PSMs are then filtered based on score, generating a list of peptides for further analysis, such as inferring the presence of specific proteins within the sample. In addition, each PSM comes with a score, a measure of confidence that the assignment is correct. Each assignment is referred to as a peptide spectrum match (PSM). There are many protocols for analyzing these files, but the initial goal of these protocols is to assign a single peptide sequence to each MS/MS spectra in the dataset.
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After high-performance liquid chromatography (HPLC), a mass spectrometer generates a series of digital files representing many mass spectra, including tandem mass spectra (MS/MS), which contain information on fragmented peptide sequences. One of the common proteomics tools is shotgun proteomics where mass spectrometry (MS) is used to identify fractionated peptides within complex mixtures, derived from proteolysis of intact proteins from a biological sample. Proteomics is now an important tool in many biological and medical applications. Going forward, PMD-FDR should provide detection of high-scoring but likely false-hits, aiding applications which rely heavily on accurate PSMs, such as proteogenomics and metaproteomics. PMD-FDR can also be used to evaluate data quality for results generated within different experimental PSM-generating workflows, assisting in method development. On a ground truth dataset and other representative data, PMD-FDR was able to detect 60-80% of likely incorrect PSMs (false-hits) while losing only 5% of correct PSMs (true-hits).
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We tested PMD-FDR on four datasets across three types of MS-based proteomics projects: standard (single organism reference database), proteogenomics (single organism customized genomic-based database plus reference), and metaproteomics (microorganism community customized conglomerate database).
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We created a post-analysis Bayesian confidence measure based on score and PMD, called PMD-FDR. We identified and addressed three cases of unexpected bias in PMD results: time of acquisition within a LC-MS run, decoy PSMs, and length of peptide. To improve robustness of these workflows, we have investigated the use of the precursor mass discrepancy (PMD) to detect and filter potentially false PSMs that have, nonetheless, a high confidence score. Workflows for large-scale (MS)-based shotgun proteomics can potentially lead to costly errors in the form of incorrect peptide spectrum matches (PSMs).