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De novo peptide sequencing algorithms have been widely used in proteomics toanalyse tandem mass spectra (MS/MS) and assign them to peptides, butquality-control methods to evaluate the confidence of de novo peptidesequencing are lagging behind. A fundamental part of a quality-control methodis the scoring function used to evaluate the quality of peptide-spectrummatches (PSMs). Here, we propose a genetic programming (GP) based method,called GP-PSM, to learn a PSM scoring function for improving the rate ofconfident peptide identification from MS/MS data. The GP method learns fromthousands of MS/MS spectra. Important characteristics about goodness of thematches are extracted from the learning set and incorporated into the GPscoring functions. We compare GP-PSM with two methods including Support VectorRegression (SVR) and Random Forest (RF). The GP method along with RF and SVR,each is used for post-processing the results of peptide identification byPEAKS, a commonly used de novo sequencing method. The results show that GP-PSMoutperforms RF and SVR and discriminates accurately between correct andincorrect PSMs. It correctly assigns peptides to 10% more spectra on anevaluation dataset containing 120 MS/MS spectra and decreases the falsepositive rate (FPR) of peptide identification.

Improving the Results of De novo Peptide Identification via Tandem Mass  Spectrometry Using a Genetic Programming-based Scoring Function for  Re-ranking Peptide-Spectrum Matches