Informatics for protein identification by mass spectrometry

Area of Science:

• Biochemistry
• Proteomics
• Mass Spectrometry

Background:

• High-throughput protein analysis (proteomics) advanced with peptide mass mapping and tandem mass spectrometry.
• Identifying proteins in complex mixtures became feasible, but challenges remain with modified or variant peptides.

Purpose of the Study:

• To review advancements in proteomics, focusing on challenges and solutions for protein identification.
• To discuss automated de novo sequencing and homology search modifications for mass spectrometry data.
• To highlight the critical aspect of validating protein identifications.

Main Methods:

• Peptide mass mapping and tandem mass spectrometry for protein identification.
• Automated de novo sequencing to derive peptide sequences independently of databases.
• Homology-based database searches (e.g., BLAST, FASTA) adapted for mass spectrometry data.

Main Results:

• Development of sensitive techniques enabling large-scale protein identification.
• Introduction of de novo sequencing and modified homology searches to address peptide modification and sequence variations.
• Emphasis on the necessity of rigorous validation for protein identifications.

Conclusions:

• Proteomics has evolved significantly, allowing identification of proteins in complex biological samples.
• Addressing challenges like peptide modifications requires advanced computational approaches.
• Robust validation is crucial to ensure the reliability of protein identification results.