Abstract
Within the last several years, top-down proteomics has emerged as a high throughput technique for protein and proteoform identification. This technique has the potential to identify and characterize thousands of proteoforms within a single study, but the absence of accurate false discovery rate (FDR) estimation could hinder the adoption and consistency of top-down proteomics in the future. In automated identification and characterization of proteoforms, FDR calculation strongly depends on the context of the search. The context includes MS data quality, the database being interrogated, the search engine, and the parameters of the search. Particular to top-down proteomics-there are four molecular levels of study: proteoform spectral match (PrSM), protein, isoform, and proteoform. Here, a context-dependent framework for calculating an accurate FDR at each level was designed, implemented, and validated against a manually curated training set with 546 confirmed proteoforms. We examined several search contexts and found that an FDR calculated at the PrSM level under-reported the true FDR at the protein level by an average of 24-fold. We present a new open-source tool, the TDCD_FDR_Calculator, which provides a scalable, context-dependent FDR calculation that can be applied post-search to enhance the quality of results in top-down proteomics from any search engine.
Highlights
Accurate and efficient false discovery rate (FDR)1 determination of protein and proteoform identifications is needed to improve top-down proteomics for large-scale, automated proteoform discovery and relative quantification [1,2]
The list of proteins discovered with a 1% FDR is not the same list as the list of proteins resulting from Proteoform Spectral Match (PrSM) discovered at a 1% FDR
The data used in the training set from Park et al yields 298 proteins when aggregated with a 1% protein level context-dependent FDR (CD FDR), but there 324 proteins when aggregated at the PrSM level and naïvely merged
Summary
Accurate and efficient false discovery rate (FDR)1 determination of protein and proteoform identifications is needed to improve top-down proteomics for large-scale, automated proteoform discovery (qualitative analysis) and relative quantification (quantitative analysis) [1,2]. A context-dependent framework for calculating an accurate FDR at each level was designed, implemented, and validated against a manually curated training set with 546 confirmed proteoforms. We present a logical structure for calculating an identification FDR at the proteoform, isoform, and protein level using PrSMs from their given search context.
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