De novo sequencing of proteins by mass spectrometry
peer-reviewed · Expert Review of Proteomics · 2020
| Date | 2020-10-01 |
| Type | peer-reviewed |
| Venue | Expert Review of Proteomics |
| Publisher | Taylor & Francis |
| Contribution | review |
| DOI | 10.1080/14789450.2020.1831387 |
| Citations (OpenAlex) | 42 |
| Venue 2-year citedness | 3.10 |
Abstract
Introduction Proteins are crucial for every cellular activity and unraveling their sequence and structure is a crucial step to fully understand their biology. Early methods of protein sequencing were mainly based on the use of enzymatic or chemical degradation of peptide chains. With the completion of the human genome project and with the expansion of the information available for each protein, various databases containing this sequence information were formed.Areas covered De novo protein sequencing, shotgun proteomics and other mass-spectrometric techniques, along with the various software are currently available for proteogenomic analysis. Emphasis is placed on the methods for de novo sequencing, together with potential and shortcomings using databases for interpretation of protein sequence data.Expert opinion As mass-spectrometry sequencing performance is improving with better software and hardware optimizations, combined with user-friendly interfaces, de-novo protein sequencing becomes imperative in shotgun proteomic studies. Issues regarding unknown or mutated peptide sequences, as well as, unexpected post-translational modifications (PTMs) and their identification through false discovery rate searches using the target/decoy strategy need to be addressed. Ideally, it should become integrated in standard proteomic workflows as an add-on to conventional database search engines, which then would be able to provide improved identification.
Methods and tools
- De novo protein sequencing review (Vitorino): Expert Review of Proteomics survey of de novo sequencing at the protein level: how MS/MS-derived peptide sequences are assembled into full protein sequences, the algorithmic and instrumental limits involved, and where the approach beats database search for organisms and proteoforms absent from reference databases.
Cites (28)
- A potential golden age to come—current tools, recent use cases, and future avenues for de novo sequencing in proteomics (2018) both
- De novo peptide sequencing by deep learning (2017) both
- Open-pNovo: De Novo Peptide Sequencing with Thousands of Protein Modifications (2017) both
- Novor: Real-Time Peptide de Novo Sequencing Software (2015) both
- UniNovo: a universal tool for de novo peptide sequencing (2013) both
- De Novo Sequencing and Homology Searching (2012) both
- A high-throughput de novo sequencing approach for shotgun proteomics using high-resolution tandem mass spectrometry (2010) both
- Spectral Dictionaries: Integrating de novo Peptide Sequencing with Database Search of Tandem Mass Spectra (2009) both
- Peptide Fragment Ion Analyser (PFIA): a simple and versatile tool for the interpretation of tandem mass spectrometric data and de novo sequencing of peptides (2007) both
- MSNovo: A Dynamic Programming Algorithm for de Novo Peptide Sequencing via Tandem Mass Spectrometry (2007) both
- Performance Evaluation of Existing De Novo Sequencing Algorithms (2006) both
- De novo peptide sequencing using exhaustive enumeration of peptide composition (2006) both
- De Novo Analysis of Peptide Tandem Mass Spectra by Spectral Graph Partitioning (2006) both
- NovoHMM: A Hidden Markov Model for de Novo Peptide Sequencing (2005) crossref
- AUDENS: A Tool for Automated Peptide de Novo Sequencing (2005) both
- DeNovoID: a web-based tool for identifying peptides from sequence and mass tags deduced from de novo peptide sequencing by mass spectroscopy (2005) both
- SPIDER: software for protein identification from sequence tags with de novo sequencing error (2005) crossref
- An effective algorithm for peptide de novo sequencing from MS/MS spectra (2005) both
- Identification of Protein Modifications Using MS/MS de Novo Sequencing and the OpenSea Alignment Algorithm (2005) both
- PepNovo: de novo peptide sequencing via probabilistic network modeling (2005) both
- A Hidden Markov Model for de Novo Peptide Sequencing (2004) semanticscholar
- GutenTag: High-Throughput Sequence Tagging via an Empirically Derived Fragmentation Model (2003) both
- PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry (2003) both
- A Suboptimal Algorithm for De Novo Peptide Sequencing via Tandem Mass Spectrometry (2003) both
- Searching Sequence Databases via De Novo Peptide Sequencing by Tandem Mass Spectrometry (2002) both
- Automated interpretation of low-energy collision-induced dissociation spectra by SeqMS, a software aid for de novo sequencing by tandem mass spectrometry (2000) both
- De novo peptide sequencing via tandem mass spectrometry (1999) both
- Sequence database searches via de novo peptide sequencing by tandem mass spectrometry (1997) both
Cited by (12)
- Prime-DiffNovo: Accurate Peptide De Novo Sequencing via Non-autoregressive Generation and Diffusion Refinement (2026) crossref
- Improvements to Casanovo, a Deep Learning De Novo Peptide Sequencer (2025) crossref
- Improvements to CasaNovo, a deep learning de novo peptide sequencer (2025) both
- A transformer-based semi-autoregressive framework for high-speed and accurate de novo peptide sequencing (2025) both
- Deep Learning Methods for De Novo Peptide Sequencing (2024) crossref
- Bridging the Gap between Database Search and De Novo Peptide Sequencing with SearchNovo (2024) both
- NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics (2024) semanticscholar
- Unveiling the Diversity and Modifications of Short Peptides in Scorpion Venom through Liquid Chromatography-High Resolution Mass Spectrometry (2024) both
- Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model (2023) both
- Algorithms for de-novo sequencing of peptides by tandem mass spectrometry: A review (2023) crossref
- PGPointNovo: an efficient neural network-based tool for parallel de novo peptide sequencing (2023) both
- Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics (2023) both