Mechanism of De Novo Peptide Sequencing

Peptide sequencing is a critical technique for determining the order of amino acids in proteins or peptides. Unlike traditional methods that depend on known databases, De Novo peptide sequencing can independently deduce amino acid sequences directly through mass spectrometry techniques.

 

Mass spectrometry (MS) is the core technology used in De Novo peptide sequencing. By measuring the mass-to-charge ratio (m/z) of ions, a mass spectrometer generates a spectrum of a peptide chain. Researchers use this spectral data to infer the amino acid sequence of the peptide.

 

Basic Principles of De Novo Peptide Sequencing

The process of De Novo peptide sequencing mainly involves the following steps:

 

1. Sample Preparation and Separation

Proteins in the sample are first digested into smaller peptides by enzymes such as trypsin. These peptides are then introduced into the mass spectrometer for separation and detection.

 

2. Ionization

Before mass spectrometry analysis, peptides need to be ionized. Common ionization methods include Electrospray Ionization (ESI) and Matrix-Assisted Laser Desorption/Ionization (MALDI). These techniques convert peptide molecules into charged ions, enabling their detection by the mass spectrometer.

 

3. Mass Spectrometry Analysis

The mass spectrometer measures the mass-to-charge ratio of the ions and generates a primary mass spectrum (MS1), displaying the molecular ion peaks of the peptides. These molecular ions are then selectively fragmented to produce a series of fragment ions, which are analyzed in a secondary mass spectrum (MS2) to obtain the fragment ion spectrum.

 

4. Data Processing and Sequence Inference

The key to De Novo peptide sequencing lies in analyzing the MS2 spectrum to infer the amino acid sequence of the peptides. The main types of fragment ions are b ions and y ions, which represent fragments starting from the N-terminus and C-terminus, respectively. By identifying these ion peaks, the peptide sequence can be assembled step by step.

 

Computational Methods and Algorithms

To accurately infer amino acid sequences from mass spectrometry data, various computational methods and algorithms have been developed. Common algorithms include:

 

1. Dynamic Programming Algorithms

Dynamic programming algorithms construct a scoring matrix to incrementally accumulate and optimize the scores of amino acid sequences. This method effectively handles complex mass spectrometry data, improving sequence inference accuracy.

 

2. Graph Theory Algorithms

Graph theory algorithms represent mass spectrometry data as graph structures, where nodes represent mass spectrometry peaks and edges indicate possible amino acid connections. By finding the optimal path in the graph, the peptide sequence can be determined.

 

3. Machine Learning Methods

Recently, machine learning methods have been introduced into De Novo peptide sequencing. By training models, researchers can better recognize patterns in mass spectrometry spectra, enhancing the efficiency and accuracy of sequence inference.

 

De Novo peptide sequencing has extensive applications in biological research, such as discovering new proteins, studying post-translational modifications, and analyzing antibody sequences. However, De Novo peptide sequencing also faces challenges, including the difficulty of interpreting complex spectra and the need for high-throughput data processing.