Peptide sequence tag generation for tandem mass spectra containing post-translational modifications

Area of Science:

• Proteomics
• Bioinformatics
• Analytical Chemistry

Background:

• Tandem mass spectrometry (MS/MS) is crucial for peptide identification.
• Accurate peptide sequencing from low signal-to-noise spectra remains challenging.
• Existing algorithms may lack efficiency and accuracy for complex spectra.

Purpose of the Study:

• To develop a rapid and accurate method for peptide sequence generation from MS/MS spectra.
• To address the challenge of identifying peptides in spectra with low signal-to-noise ratios.
• To improve upon existing peptide identification algorithms like PepNovo.

Main Methods:

• A graph search algorithm is proposed for rapid peptide sequence generation.
• The method utilizes high-intensity peak pairs, forming a Pair Peak value Set (PPS).
• A tree-based search identifies the most probable peptide sequence path.

Main Results:

• The algorithm demonstrates high efficiency in generating short peptide sequences.
• Experiments on 2420 MS/MS spectra with post-translational modifications (PTMs) were conducted.
• The proposed method achieved superior accuracy and efficiency compared to the PepNovo approach.

Conclusions:

• The novel graph search method offers a significant advancement in peptide identification via MS/MS.
• This approach is particularly effective for analyzing spectra with low signal-to-noise ratios.
• The algorithm provides a more accurate and efficient alternative for proteomic data analysis.