Learning Peptide-Spectrum Alignment Models for Tandem Mass Spectrometry

We present a peptide-spectrum alignment strategy that employs a dynamic Bayesian network (DBN) for the identification of spectra produced by tan-dem mass spectrometry (MS/MS). Our method is fundamentally generative in that it models peptide fragmentation in MS/MS as a physical process. The model traverses an observed MS/MS spec-trum and a peptide-based theoretical spectrum to calculate the best alignment between the two spectra. Unlike all existing state-of-the-art meth-ods for spectrum identification that we are aware of, our method can learn alignment probabilities given a dataset of high-quality peptide-spectrum pairs. The method, moreover, accounts for noise peaks and absent theoretical peaks in the observed spectrum. We demonstrate that our method out-performs, on a majority of datasets, several widely used, state-of-the-art database search tools for spectrum identification. Furthermore, the pro-posed approach provides an extensible framework for MS/MS analysis and provides useful informa-tion that is not produced by other methods, thanks to its generative structure.