Analysis of Limited Proteolysis-Coupled Mass Spectrometry Data

Area of Science:

• Proteomics
• Structural Biology
• Bioinformatics

Background:

• Limited proteolysis combined with mass spectrometry (LiP-MS) is a powerful technique for assessing protein structure changes across the proteome.
• Interpreting LiP-MS data is challenging due to confounding factors like protein abundance and modifications.
• Existing methods struggle to accurately deconvolute structural signals from other biological variations.

Purpose of the Study:

• To develop and validate a comprehensive computational pipeline for inferring protein structural alterations from LiP-MS data.
• To accurately separate true structural changes from variations in protein abundance, post-translational modifications, and alternative splicing.
• To provide a robust framework for analyzing complex proteomic structural data.

Main Methods:

• A two-step computational approach was developed to process LiP-MS data.
• Step 1: Removal of non-structural variations from the LiP signal.
• Step 2: Inference of structural effects based on the cleaned signal, validated across three species.

Main Results:

• The developed pipeline effectively removes unwanted variations, isolating structural signals.
• The approach demonstrates superior performance compared to previous methods in distinguishing structural changes.
• The pipeline successfully deconvolves LiP-MS signals, separating structural alterations from abundance and modification changes.

Conclusions:

• The novel computational pipeline offers a powerful solution for accurate interpretation of LiP-MS data.
• This framework enhances the ability to study protein structure dynamics on a proteome-wide scale.
• The methodology is applicable to other peptide-centric structural proteomics techniques like FPOP and molecular painting.