De novo Peptide Sequencing
peer-reviewed · Proteome Informatics · 2016
| Date | 2016-11-23 |
| Type | peer-reviewed |
| Venue | Proteome Informatics |
| Publisher | The Royal Society of Chemistry |
| Contribution | review |
| DOI | 10.1039/9781782626732-00015 |
| Citations (OpenAlex) | 0 |
Abstract
De novo peptide sequencing refers to the process of determining a peptide’s amino acid sequence from its MS/MS spectrum alone. The principle of this process is fairly straightforward: a high-quality spectrum may present a ladder of fragment ion peaks. The mass difference between every two adjacent peaks in the ladder is used to determine a residue of the peptide. However, most practical spectra do not have sufficient quality to support this straightforward process. Therefore, research in de novo sequencing has largely been a battle against the errors in the data. This chapter reviews some of the major developments in this field. The chapter starts with a quick review of the history in Section 1. Then manual de novo sequencing is examined in Section 2. Section 3 introduces a few commonly used de novo sequencing algorithms. An important aspect of automated de novo sequencing software is a good scoring function that serves as the optimization goal of the algorithm. Thus, Section 4 is devoted for the methods to define good scoring functions. Section 5 reviews a list of relevant software. The chapter concludes with a discussion of the applications and limitations of de novosequencing in Section 6.
Methods and tools
- De novo peptide sequencing (Proteome Informatics chapter): Book chapter reviewing the de novo peptide sequencing field: its history, manual interpretation, the algorithmic approaches, scoring functions, the available software and its applications. Written by the author of PEAKS.
Data used
- High-throughput MS-based immunopeptidomics (as deposited) · PXD006939
- High-throughput proteomics of the 26 medically most important elapids and vipers from sub-Saharan Africa (as deposited) · PXD036161
- InstaNovo enables diffusion-powered de novo peptide identification in large scale proteomics experiments (as deposited) · PXD044934, 10.6084/m9.figshare.24173889
- Nine-species benchmark (InstaNovo split) · 10.57967/hf/3821, InstaDeepAI/ms_ninespecies_benchmark
- Nine-species benchmark (original (DeepNovo, 2017)) · MSV000081382, PXD003868 (provenance), PXD004325 (provenance), PXD004424 (provenance), PXD004467 (provenance), PXD004536 (provenance), PXD004565 (provenance), PXD004947 (provenance), PXD004948 (provenance), PXD005025 (provenance)
- ProteomeTools (Parts I-III) · PXD004732, PXD010595, PXD021013
- ProteomeTools (high-confidence (InstaNovo)) · 10.57967/hf/3822, InstaDeepAI/ms_proteometools, PXD004732 (provenance), PXD010595 (provenance), PXD021013 (provenance)
- Supplementary Data for “Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly” (as deposited) · 10.6084/m9.figshare.21394143
- Winnow MS datasets (version not stated)
Cites (33)
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- Novor: Real-Time Peptide de Novo Sequencing Software (2015) crossref
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