Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly
peer-reviewed · Briefings in Bioinformatics · 2023
| Date | 2023-01-19 |
| Type | peer-reviewed |
| Venue | Briefings in Bioinformatics |
| Publisher | Oxford University Press (OUP) |
| Contribution | benchmark |
| DOI | 10.1093/bib/bbac542 |
| Citations (OpenAlex) | 72 |
| Venue 2-year citedness | 6.23 |
Abstract
Monoclonal antibodies are biotechnologically produced proteins with various applications in research, therapeutics and diagnostics. Their ability to recognize and bind to specific molecule structures makes them essential research tools and therapeutic agents. Sequence information of antibodies is helpful for understanding antibody-antigen interactions and ensuring their affinity and specificity. De novo protein sequencing based on mass spectrometry is a valuable method to obtain the amino acid sequence of peptides and proteins without a priori knowledge. In this study, we evaluated six recently developed de novo peptide sequencing algorithms (Novor, pNovo 3, DeepNovo, SMSNet, PointNovo and Casanovo), which were not specifically designed for antibody data. We validated their ability to identify and assemble antibody sequences on three multi-enzymatic data sets. The deep learning-based tools Casanovo and PointNovo showed an increased peptide recall across different enzymes and data sets compared with spectrum-graph-based approaches. We evaluated different error types of de novo peptide sequencing tools and their performance for different numbers of missing cleavage sites, noisy spectra and peptides of various lengths. We achieved a sequence coverage of 97.69-99.53% on the light chains of three different antibody data sets using the de Bruijn assembler ALPS and the predictions from Casanovo. However, low sequence coverage and accuracy on the heavy chains demonstrate that complete de novo protein sequencing remains a challenging issue in proteomics that requires improved de novo error correction, alternative digestion strategies and hybrid approaches such as homology search to achieve high accuracy on long protein sequences.
Methods and tools
- De novo evaluation for monoclonal-antibody assembly: Benchmark evaluation of de novo peptide sequencing tools for monoclonal antibody assembly.
Cites (26)
- De novo mass spectrometry peptide sequencing with a transformer model (2022) crossref
- DPST: De Novo Peptide Sequencing with Amino-Acid-Aware Transformers (2022) both
- DePS: An improved deep learning model for de novo peptide sequencing (2022) both
- PepNet: A Fully Convolutional Neural Network for De novo Peptide Sequencing (2022) both
- De novo mass spectrometry peptide sequencing with a transformer model (2022) semanticscholar
- Highly Robust de Novo Full-Length Protein Sequencing (2021) both
- Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices (2021) both
- Flying blind, or just flying under the radar? The underappreciated power of de novo methods of mass spectrometric peptide identification (2020) both
- Uncovering Thousands of New Peptides with Sequence-Mask-Search Hybrid De Novo Peptide Sequencing Framework (2019) both
- pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework (2019) both
- Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry (2018) both
- Postnovo: Postprocessing Enables Accurate and FDR-Controlled de Novo Peptide Sequencing (2018) both
- A potential golden age to come—current tools, recent use cases, and future avenues for de novo sequencing in proteomics (2018) both
- SearchGUI: A Highly Adaptable Common Interface for Proteomics Search and de Novo Engines (2018) both
- De novo peptide sequencing by deep learning (2017) both
- Combining De Novo Peptide Sequencing Algorithms, A Synergistic Approach to Boost Both Identifications and Confidence in Bottom-up Proteomics (2017) both
- Evaluating de novo sequencing in proteomics: already an accurate alternative to database-driven peptide identification? (2017) both
- Novor: Real-Time Peptide de Novo Sequencing Software (2015) both
- PeptideShaker enables reanalysis of MS-derived proteomics data sets (2015) both
- Lessons in de novo peptide sequencing by tandem mass spectrometry (2015) both
- DeNovoGUI: An Open Source Graphical User Interface for de Novo Sequencing of Tandem Mass Spectra (2014) both
- Shotgun Protein Sequencing with Meta-contig Assembly (2012) both
- PEAKS DB: De Novo Sequencing Assisted Database Search for Sensitive and Accurate Peptide Identification (2012) both
- Performance Evaluation of Existing De Novo Sequencing Algorithms (2006) both
- SPIDER: software for protein identification from sequence tags with de novo sequencing error (2005) both
- PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry (2003) crossref
Cited by (22)
- Reference-free protein sequencing by consensus assembly of redundant de novo peptide reads (2026) both
- Prime-DiffNovo: Accurate Peptide De Novo Sequencing via Non-autoregressive Generation and Diffusion Refinement (2026) crossref
- XA-Novo: an accurate and high-throughput mass spectrometry-based de novo sequencing technology for monoclonal antibodies and antibody mixtures (2026) crossref
- AbNovoBench: a resource and benchmarking platform for monoclonal antibody de novo sequencing (2026) crossref
- Bidirectional Representations Augmented Autoregressive Biological Sequence Generation (2025) semanticscholar
- SequenceAssembler: A tool for protein sequence assembly from mass spectrometry data (2025) crossref
- Limitations of de novo sequencing in resolving sequence ambiguity (2025) crossref
- InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments (2025) both
- DiNovo: high-coverage, high-confidence de novo peptide sequencing using mirror proteases and deep learning (2025) crossref
- Metaproteomics Beyond Databases: Addressing the Challenges and Potentials of De Novo Sequencing (2025) crossref
- π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2025) crossref
- Deep Learning Methods for De Novo Peptide Sequencing (2024) crossref
- Sequence-to-sequence translation from mass spectra to peptides with a transformer model (2024) both
- PowerNovo: de novo peptide sequencing via tandem mass spectrometry using an ensemble of transformer and BERT models (2024) both
- π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2024) crossref
- Transformer-Based De Novo Peptide Sequencing for Data-Independent Acquisition Mass Spectrometry (2024) semanticscholar
- Introducing π-HelixNovo for practical large-scale de novo peptide sequencing (2024) crossref
- Transformer-Based De Novo Peptide Sequencing for Data-Independent Acquisition Mass Spectrometry (2023) both
- Multi-Modal Mass Spectrometry Identifies a Conserved Protective Epitope in S. pyogenes Streptolysin O (2023) crossref
- De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments (2023) both
- Algorithms for de-novo sequencing of peptides by tandem mass spectrometry: A review (2023) crossref
- Introducing PandaNovo for practical large-scale de novo peptide sequencing (2023) semanticscholar