Identifying Dynamic Interactors of Protein Complexes by Quantitative Mass Spectrometry

• Published in: Molecular & cellular proteomics Vol. 7; no. 1; pp. 46 - 57
• Main Authors: Wang, Xiaorong, Huang, Lan
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.01.2008; American Society for Biochemistry and Molecular Biology
• Subjects: Amino Acid Sequence; Cell Line; Humans; Immunoblotting; Isotope Labeling; Mass Spectrometry - methods; Molecular Sequence Data; Peptides - chemistry; Peptides - isolation & purification; Peptides - metabolism; Proteasome Endopeptidase Complex - metabolism; Protein Interaction Mapping - methods; Proteins - chemistry; Proteins - isolation & purification; Proteins - metabolism; Reproducibility of Results; Time Factors
• ISSN: 1535-9476; 1535-9484
• DOI: 10.1074/mcp.M700261-MCP200

Dynamically interacting proteins associate and dissociate with their binding partners at high on/off rates. Although their identification is of great significance to proteomics research, lack of an efficient strategy to distinguish stable and dynamic interactors has hampered the efforts toward this goal. In this work, we developed a new method, MAP (mixing after purification)-SILAC (stable isotope labeling of amino acids in cell culture), to quantitatively investigate the interactions of protein complexes by mass spectrometry. In combination with the original SILAC approach, stable and dynamic components were effectively distinguished by the differences in their relative abundance ratio changes. We applied the newly developed strategies to decipher the dynamics of the human 26 S proteasome-interacting proteins. A total of 67 putative human proteasome-interacting proteins were identified by the MAP-SILAC method among which 14 proteins would have been misidentified as background proteins due to low relative abundance ratios in standard SILAC experiments and 57 proteins have not been reported previously. In addition, 35 of the 67 proteins were classified as stable interactors of the proteasome complex, whereas 16 of them were identified as dynamic interactors. The methods reported here provide a valuable expansion of proteomics technologies for identification of important but previously unidentifiable interacting proteins.