Assessing the Significance of Peptide Spectrum Match Scores

Peptidic Natural Products (PNPs) are highly sought after bioactive compounds that include many antibiotic, antiviral and antitumor agents, immunosuppressors and toxins. Even though recent advancements in mass-spectrometry have led to the development of accurate sequencing methods for nonlinear (cyclic and branch-cyclic) peptides, requiring only picograms of input material, the identification of PNPs via a database search of mass spectra remains problematic. This holds particularly true when trying to evaluate the statistical significance of Peptide Spectrum Matches (PSM) especially when working with non-linear peptides that often contain non-standard amino acids, modifications and have an overall complex structure. In this paper we describe a new way of estimating the statistical significance of a PSM, defined by any peptide (including linear and non-linear), by using state-of-the-art Markov Chain Monte Carlo methods. In addition to the estimate itself our method also provides an uncertainty estimate in the form of confidence bounds, as well as an automatic simulation stopping rule that ensures that the sample size is sufficient to achieve the desired level of result accuracy.