Choosing an Optimal Database for Protein Identification from Tandem Mass Spectrometry Data

Area of Science:

• Proteomics
• Mass Spectrometry
• Bioinformatics

Background:

• Database searching is the standard for protein identification using mass spectrometry data.
• The selection of a search database significantly impacts protein discovery and subsequent biological interpretations.
• Current database selection practices are often arbitrary, potentially limiting study outcomes.

Purpose of the Study:

• To describe common protein search databases used in proteomic studies.
• To analyze the influence of database composition and size on protein identification.
• To provide guidance on selecting appropriate databases for specific research questions.

Main Methods:

• Review of commonly used protein sequence databases.
• Analysis of factors affecting protein identification, including database characteristics.
• Discussion of the relationship between database choice and derived biological conclusions.

Main Results:

• The composition and size of a search database are critical factors influencing protein identification.
• Arbitrary database selection can lead to suboptimal protein discovery.
• Tailoring databases to specific research questions enhances identification confidence.

Conclusions:

• Database selection in proteomics is not arbitrary and should align with the desired biological inferences.
• Developing compact, concise databases for targeted proteomic questions yields more confident protein identifications.
• Strategic database selection is key to maximizing the value of mass spectrometry data.