Probability-based protein identification by searching sequence databases using mass spectrometry data

TLDR

Protein identification by searching sequence databases with mass spectrometry data has been approached using peptide mass, MS/MS spectra, or combined mass and sequence information. The study introduces Mascot, a program that integrates peptide mass, MS/MS, and sequence data for protein identification. Mascot uses a probability‑based scoring algorithm that allows simple significance testing, comparison with other search types, and iterative optimization of search parameters. The probability‑based scoring reduces false positives, enables comparison with homology searches, allows iterative parameter optimization, and its strengths and limitations for high‑throughput automated identification are discussed.

Abstract

Several algorithms have been described in the literature for protein identification by searching a sequence database using mass spectrometry data. In some approaches, the experimental data are peptide molecular weights from the digestion of a protein by an enzyme. Other approaches use tandem mass spectrometry (MS/MS) data from one or more peptides. Still others combine mass data with amino acid sequence data. We present results from a new computer program, Mascot, which integrates all three types of search. The scoring algorithm is probability based, which has a number of advantages: (i) A simple rule can be used to judge whether a result is significant or not. This is particularly useful in guarding against false positives. (ii) Scores can be com pared with those from other types of search, such as sequence homology. (iii) Search parameters can be readily optimised by iteration. The strengths and limitations of probability-based scoring are discussed, particularly in the context of high throughput, fully automated protein identification.