PowerNovo: de novo peptide sequencing via tandem mass spectrometry using an ensemble of transformer and BERT models
peer-reviewed · Scientific Reports · 2024
| Date | 2024-07-01 |
| Type | peer-reviewed |
| Venue | Scientific Reports |
| Publisher | Scientific Reports |
| Contribution | algorithm |
| DOI | 10.1038/s41598-024-65861-0 |
| Citations (OpenAlex) | 28 |
| Venue 2-year citedness | 4.50 |
Abstract
The primary objective of analyzing the data obtained in a mass spectrometry-based proteomic experiment is peptide and protein identification, or correct assignment of the tandem mass spectrum to one amino acid sequence. Comparison of empirical fragment spectra with the theoretical predicted one or matching with the collected spectra library are commonly accepted strategies of proteins identification and defining of their amino acid sequences. Although these approaches are widely used and are appreciably efficient for the well-characterized model organisms or measured proteins, they cannot detect novel peptide sequences that have not been previously annotated or are rare. This study presents PowerNovo tool for de novo sequencing of proteins using tandem mass spectra acquired in a variety of types of mass analyzers and different fragmentation techniques. PowerNovo involves an ensemble of models for peptide sequencing: model for detecting regularities in tandem mass spectra, precursors, and fragment ions and a natural language processing model, which has a function of peptide sequence quality assessment and helps with reconstruction of noisy sequences. The results of testing showed that the performance of PowerNovo is comparable and even better than widely utilized PointNovo, DeepNovo, Casanovo, and Novor packages. Also, PowerNovo provides complete cycle of processing (pipeline) of mass spectrometry data and, along with predicting the peptide sequence, involves the peptide assembly and protein inference blocks.
Methods and tools
- PowerNovo: Transformer + BERT ensemble
Cites (9)
- Transformer-Based De Novo Peptide Sequencing for Data-Independent Acquisition Mass Spectrometry (2024) crossref
- Transformer-Based De Novo Peptide Sequencing for Data-Independent Acquisition Mass Spectrometry (2023) crossref
- Accurate de novo peptide sequencing using fully convolutional neural networks (2023) both
- Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly (2023) both
- De novo mass spectrometry peptide sequencing with a transformer model (2022) both
- Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices (2021) crossref
- Uncovering Thousands of New Peptides with Sequence-Mask-Search Hybrid De Novo Peptide Sequencing Framework (2019) crossref
- De novo peptide sequencing by deep learning (2017) both
- Novor: Real-Time Peptide de Novo Sequencing Software (2015) both
Cited by (6)
- PowerNovo2: A generative flow-based approach to non-autoregressive de novo peptide sequencing (2026) crossref
- AbNovoBench: a resource and benchmarking platform for monoclonal antibody de novo sequencing (2026) crossref
- Modanovo: A Unified Model for Post-translational Modification-Aware De Novo Sequencing Using Experimental Spectra From In Vivo and Synthetic Peptides (2025) both
- Modanovo: A Unified Model for Post-Translational Modification-Aware de Novo Sequencing Using Experimental Spectra from In Vivo and Synthetic Peptides (2025) both
- Framework for de novo sequencing of peptide mixtures via network analysis and two-dimensional tandem mass spectrometry (2025) crossref
- Deep Learning Methods for De Novo Peptide Sequencing (2024) crossref