De novo sequencing of multiple SILAC-based tandem mass spectra
peer-reviewed · 2022 IEEE 21st International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) · 2022
| Date | 2022-12-08 |
| Type | peer-reviewed |
| Venue | 2022 IEEE 21st International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) |
| Publisher | IEEE |
| Contribution | algorithm |
| DOI | 10.1109/iccicc57084.2022.10101613 |
| Citations (OpenAlex) | 0 |
Abstract
De novo peptide sequencing of tandem mass spec-trometry (MS/MS) has emerged as an important technology for peptide sequencing in proteomics. To increase the accuracy and practicality of de novo sequencing, some previous algorithms used multiple spectra to identify the peptide sequence. In this approach, we focus on de novo sequencing of multiple SILAC-based tandem mass spectra. SILAC technology uses medium containing different kinds of isotope-labeled essential amino acids, usually Arginine(R) and Lysine(K), to label newly synthesized proteins with stable isotopes during cell growth. Multiple MS/MS spectra for the same peptide sequence are produced by spectrometry after the SILAC samples are processed by LC-MS/MS shotgun proteomics. Based on the factors such as the type of isotope labeling, precursor ion mass, etc., multiple spectra with different type of SILAC PTMs for the same peptide can be used to identify the peptide sequence. In this paper, we present two de novo sequencing algorithms to compute the peptide sequence which are based on total number of SILAC modifications and based on the numbers of SILAC Arginine(R) and Lysine(K).
Methods and tools
- SILAC group de novo: Algorithm for peptide de novo sequencing from a group of SILAC-labeled MS/MS spectra.