NBPMF: Novel network-based inference methods for peptide mass fingerprinting

Mass spectrometry (MS) has recently become a primary tool for protein identification and quantification. Peptide mass fingerprinting (PMF) is widely used to identify proteins from MS data. Conventional PMF representatives such as probabilistic MOWSE algorithm, is based on mass distribution of tryptic peptides. In this paper we develop a novel network-based inference software termed NBPMF. By analyzing peptide-protein bipartite network, we developed new peptide protein matching score functions. We present two methods: the static one, ProbS, is based on an independent probability framework; and the dynamic one, HeatS, depicts input data from dependent perspective. We also use linear regression to adjust the matching score according to the masses of proteins. In addition, we consider the order of retention time to further correct the score function. In the post processing, we restrict that a peak can only be assigned to one peptide in order to reduce random matches. Finally, we try to filter false positive proteins for better result. The experiments on simulated and real data demonstrate that our NBPMF approaches lead to significantly improved performance compared to several state-of-the-art methods.