Interrogating data-independent acquisition LC-MS/MS for affinity proteomics.

Area of Science:

• Proteomics
• Biochemistry
• Analytical Chemistry

Background:

• Data-Independent Acquisition (DIA) LC-MS/MS is a powerful technique for affinity proteomics and co-immunoprecipitation (co-IP).
• Reducing quantitation variability in DIA can improve the detection of specific protein interactors compared to Data-Dependent Acquisition (DDA).

Purpose of the Study:

• To evaluate spectral libraries, protein quantity missingness, and coefficient of variation (CV) in DIA experiments across different instrument manufacturers and bioinformatics workflows.
• To compare the performance of DIA against DDA for affinity proteomics applications.

Main Methods:

• Interrogation of affinity proteomes from six studies using both DDA and DIA experiments.
• Analysis of four contemporary DIA bioinformatics workflows: FragPipe, DIA-NN, Spectronaut, and MaxQuant.
• Assessment of spectral library generation, protein quantification, and CV values.

Main Results:

• Spectral libraries generated directly from DIA experiments are effective, comparable to those from DDA with equivalent instrument time.
• Contemporary software may struggle to quantify indistinct signals in experiments with mock pull-downs or IgG controls.
• Spectronaut, DIA-NN, and FragPipe demonstrated well-controlled CV values for protein quantification.
• Using FragPipe for both spectral library building and quantification in DIA outperformed DDA, yielding more quantified proteins and lower CVs.

Conclusions:

• DIA-LC-MS/MS is a robust method for affinity proteomics, offering advantages over DDA in protein quantification and detection.
• Optimized DIA bioinformatics workflows, such as FragPipe, can significantly enhance the depth and reliability of proteomic analyses.
• Careful experimental design is needed to manage indistinct signals in control experiments for accurate proteomic profiling.