Quantitative proteomics targeting classes of motif-containing peptides using immunoaffinity-based mass spectrometry

Area of Science:

• Proteomics
• Biotechnology
• Molecular Biology

Background:

• Developing high-performance technology platforms for detailed protein expression profiling is crucial.
• Existing methods often lack species independence or require extensive antibody sets.
• A novel global proteome survey (GPS) platform was previously introduced, combining affinity proteomics and mass spectrometry.

Purpose of the Study:

• To demonstrate the quantitative capability, reproducibility, sensitivity, and coverage of the GPS technology.
• To validate the GPS platform using labeled yeast cultures.
• To assess the performance of motif-specific antibodies in proteome profiling.

Main Methods:

• Utilized context-independent-motif-specific antibodies targeting short amino acid motifs.
• Enriched motif-containing peptides from digested proteomes.
• Detected and identified peptides using mass spectrometry in stable isotope labeling with amino acids in cell culture (SILAC)-labeled yeast.

Main Results:

• Achieved highly reproducible detection, identification, and quantification of motif-containing peptides and proteins.
• Each antibody targeted approximately 75 motif-containing proteins on average.
• Successfully profiled peptides from proteins across a wide abundance range (50 to >1 million copies/cell).
• Identified novel peptides not previously reported in the PeptideAtlas database.
• Quantitative data correlated well with conventional strong cation exchange fractionation.
• Detected differentially expressed proteins in glucose- vs. ethanol-cultivated yeast, including those involved in central carbon metabolism.

Conclusions:

• The GPS technology demonstrates significant potential for reproducible, quantitative proteomics.
• The immunoaffinity-based mass spectrometry platform enables targeted profiling of motif-containing peptides.
• This approach advances the development of comprehensive protein atlases.