Postnovo: Postprocessing Enables Accurate and FDR-Controlled de Novo Peptide Sequencing

De novo sequencing offers an alternative to database search methods for peptide identification from mass spectra. Since it does not rely on a predetermined database of expected or potential sequences in the sample, de novo sequencing is particularly appropriate for samples lacking a well-defined or comprehensive reference database. However, the low accuracy of many de novo sequence predictions has prevented the widespread use of the variety of sequencing tools currently available. Here, we present a new open-source tool, Postnovo, that postprocesses de novo sequence predictions to find high-accuracy results. Postnovo uses a predictive model to rescore and rerank candidate sequences in a manner akin to database search postprocessing tools such as Percolator. Postnovo leverages the output from multiple de novo sequencing tools in its own analyses, producing many times the length of amino acid sequence information (including both full- and partial-length peptide sequences) at an equivalent false discovery rate (FDR) compared to any individual tool. We present a methodology to reliably screen the sequence predictions to a desired FDR given the Postnovo sequence score. We validate Postnovo with multiple data sets and demonstrate its ability to identify proteins that are missed by database search even in samples with paired reference databases.