Tandem mass spectrometry (MS/MS) has emerged as the leading method for high- throughput protein identification in proteomics. Recent technological breakthroughs have dramatically increased the efficiency of MS/MS data generation. Meanwhile, sophisticated algorithms have been developed for identifying proteins from peptide MS/MS data by searching available protein sequence databases for the peptide that is most likely to have produced the observed spectrum. The popular SEQUEST algorithm relies on the cross-correlation between the experimental mass spectrum and the theoretical spectrum of a peptide. It utilizes a simplified fragmentation model that assigns a fixed and identical intensity for all major ions and fixed and lower intensity for their neutral losses. In this way, the common issues involved in predicting theoretical spectra are circumvented. In practice, however, an experimental spectrum is usually not similar to its SEQUEST -predicted theoretical one, and as a result, incorrect identifications are often generated.