An algorithm for peptide de novo sequencing from a group of SILAC labeled MS/MS spectra
peer-reviewed · Journal of Bioinformatics and Computational Biology · 2025
| Date | 2025-06-01 |
| Type | peer-reviewed |
| Venue | Journal of Bioinformatics and Computational Biology |
| Publisher | World Scientific Pub Co Pte Ltd |
| Contribution | algorithm |
| DOI | 10.1142/S0219720025500076 |
| Citations (OpenAlex) | 0 |
| Venue 2-year citedness | 0.88 |
Abstract
Shotgun proteomics coupled with high-performance liquid chromatography and mass spectrometry has been instrumental in identifying proteins in complex mixtures. Effective computational approaches are required to automate the spectra interpretation process to handle the vast amount of data collected in a single Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) run. De novo sequencing from MS/MS has emerged as a vital technology for peptide sequencing in proteomics. To enhance the accuracy and practicality of de novo sequencing, previous algorithms have utilized multiple spectra to identify peptide sequences. Here, our study focuses on de novo sequencing of multiple tandem mass spectra of peptides with stable isotope labeling with amino acids in cell culture (SILAC) by incorporating different isotope-labeled amino acids into newly synthesized proteins. Multiple MS/MS spectra for the same peptide sequence are produced by the spectrometer after the SILAC samples undergo processing by LC-MS/MS shotgun proteomics. Taking into consideration the factors such as retention time and precursor ion mass, we aim to identify the peptide sequence with specific SILAC modifications and their locations. To do so, we propose de novo sequencing algorithms to compute the potential candidate peptide sequence by using similarity scores, followed by refinement algorithms to evaluate them. We also use real experimental data to test the algorithms.
Methods and tools
- SILAC group de novo: Algorithm for peptide de novo sequencing from a group of SILAC-labeled MS/MS spectra.
Cites (4)
- NovoBoard: A Comprehensive Framework for Evaluating the False Discovery Rate and Accuracy of De Novo Peptide Sequencing (2024) crossref
- Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics (2023) crossref
- An effective algorithm for peptide de novo sequencing from MS/MS spectra (2005) crossref
- Computer program (SEQPEP) to aid in the interpretation of high-energy collision tandem mass spectra of peptides (1989) crossref