MS/MS-Based Detection Procedure of De Novo Peptide Sequencing

Mass spectrometry (MS) technology is pivotal in proteomics research, particularly for peptide sequencing. MS/MS (Tandem Mass Spectrometry) serves as the primary tool for De Novo peptide sequencing due to its high sensitivity and resolution. De Novo sequencing derives peptide sequences directly from mass spectrometry data without relying on reference databases, making it an essential method for protein identification.

 

Steps for De Novo Peptide Sequencing Based on MS/MS

1. Sample Preparation

Sample preparation, the first step in MS/MS analysis, typically involves protein extraction, purification, and enzymatic digestion. Trypsin is frequently used to enzymatically digest proteins into smaller peptides suitable for mass spectrometric analysis.

 

2. Mass Spectrometry Analysis

Following separation by liquid chromatography (LC), the sample is introduced into the mass spectrometer. MS analysis comprises primary mass spectrometry (MS1) and secondary mass spectrometry (MS2). In MS1, the mass-to-charge ratio (m/z) of peptide ions is measured, and specific peptides are selected for collision-induced dissociation (CID) to generate fragment ions. Subsequently, MS2 detects the m/z values of these fragment ions.

 

3. Data Processing

MS/MS data processing involves peak extraction, spectrum matching, and peptide sequence derivation. Common software tools include PEAKS and Mascot. The steps in data processing are as follows:

 

(1) Peak Extraction: Extracting significant peaks from the mass spectra.

(2) Spectrum Matching: Matching MS1 and MS2 data to identify potential peptides.

(3) Sequence Derivation: Inferring peptide sequences based on the m/z values of fragment ions.

 

4. Data Validation

To verify the accuracy of De Novo sequencing results, data validation is necessary. This can be done by comparing the results with known protein sequence databases or confirming through additional mass spectrometry experiments.

 

5. Result Analysis

The final step involves bioinformatics analysis of the derived peptide sequences to identify proteins and infer their functions. This may include protein family analysis, function prediction, and pathway analysis.