Protein identification and quantification using mass spectrometry (MS) involves several steps, including sample preparation, protein digestion, peptide separation and analysis by MS, and data analysis.
- Sample preparation: The first step is to prepare the sample for MS analysis. This typically involves lysing cells or tissues and extracting the proteins. The extracted proteins are then usually reduced and alkylated to break disulfide bonds and prevent reformation of these bonds. The protein mixture is then digested with a protease such as trypsin, which breaks down the proteins into peptides.
- Peptide separation and MS analysis: The peptides are separated by liquid chromatography (LC) and introduced into the MS instrument. The peptides are ionized and analyzed by MS. The mass-to-charge ratio (m/z) of the peptides is determined, and the resulting spectra are used to identify the peptides and their corresponding proteins.
- Database searching: The MS data are analyzed by searching a protein sequence database to identify the proteins present in the sample. The search engine compares the observed peptide mass and fragmentation patterns to those predicted from the protein sequences in the database.
- Protein quantification: Protein quantification can be achieved by label-free or labeled methods. Label-free methods compare the intensity or peak area of the peptides in MS spectra between different samples. Labeled methods, such as isotope labeling, involve introducing a stable isotope label to the sample, allowing for relative quantification between labeled and unlabeled samples.
- Data analysis: The identified peptides and proteins are validated based on various criteria, such as the number of peptides matched, the sequence coverage of the protein, and the quality of the fragmentation spectra. The quantitative data are analyzed to identify differentially expressed proteins between samples.
Overall, MS-based protein identification and quantification is a powerful tool for studying complex biological systems and can provide valuable insights into disease mechanisms and therapeutic targets.