A decision-driven framework for the mass spectrometry analysis of previously uncharacterized protein modifications

Area of Science:

• Proteomics
• Biochemistry
• Analytical Chemistry

Background:

• Identifying unknown protein modifications is a significant challenge in proteomics.
• Conventional methods often rely on predefined lists, potentially biasing results toward known post-translational modifications (PTMs).

Purpose of the Study:

• To present a decision-driven analytical framework for investigating uncharacterized protein modifications.
• To guide researchers in experimental design, data acquisition, and interpretation for confident identification and localization of novel modifications.

Main Methods:

• Utilizes bottom-up liquid chromatography-tandem mass spectrometry (LC-MS/MS).
• Employs a framework emphasizing chemistry-informed hypothesis generation and iterative refinement of search parameters.
• Integrates experimental controls and targeted data interpretation.

Main Results:

• Highlights key decision points in experimental design and data analysis for robust modification discovery.
• Provides a flexible approach applicable to various modification types, including drug adducts and endogenous PTMs.
• Facilitates confident identification and residue-level localization of previously unknown modifications.

Conclusions:

• The proposed framework offers a versatile strategy for unbiased discovery of novel protein modifications.
• It enables confident identification and localization of modifications across diverse biological and chemical contexts.
• This approach overcomes limitations of predefined workflows in proteomics research.