A Statistical Model for Identifying Proteins by Tandem Mass Spectrometry

TLDR

The study presents a statistical model that computes probabilities of protein presence based on peptide assignments from tandem mass spectra of proteolytic digests. The model apportions shared peptides among all matching proteins and uses an expectation–maximization algorithm to derive a minimal protein list that explains the observed peptide assignments. The model accurately discriminates true from false protein identifications, enabling high‑sensitivity filtering of large‑scale proteomics data with predictable false‑positive rates, and offers a fast, transparent standard for publishing and comparing protein identification results.

Abstract

A statistical model is presented for computing probabilities that proteins are present in a sample on the basis of peptides assigned to tandem mass (MS/MS) spectra acquired from a proteolytic digest of the sample. Peptides that correspond to more than a single protein in the sequence database are apportioned among all corresponding proteins, and a minimal protein list sufficient to account for the observed peptide assignments is derived using the expectation−maximization algorithm. Using peptide assignments to spectra generated from a sample of 18 purified proteins, as well as complex H. influenzae and Halobacterium samples, the model is shown to produce probabilities that are accurate and have high power to discriminate correct from incorrect protein identifications. This method allows filtering of large-scale proteomics data sets with predictable sensitivity and false positive identification error rates. Fast, consistent, and transparent, it provides a standard for publishing large-scale protein identification data sets in the literature and for comparing the results obtained from different experiments.