Probabilistic and Likelihood-Based Methods for Protein Identification from MS/MS Data

Abstract

The process of identification of peptides from the mass spectra and the constituent proteins in a sample is called protein identification. In the current literature, there exist many proposed approaches for the protein identification problem based on tandem mass spectrometry (MS/MS) data. While there are many two-step protein identification procedures that first identify peptides in a separate process and then use the results in protein identification, in recent years there have been attempts to develop a one-step solution to the problem through simultaneous identification of proteins and peptides in a sample. We briefly introduce the probabilistic and likelihood-based two-step and one-step procedures and report some comparative performances of these procedures for different MS/MS data.