Accurate de novo peptide sequencing using fully convolutional neural networks
peer-reviewed · Nature Communications · 2023
| Date | 2023-12-02 |
| Type | peer-reviewed |
| Venue | Nature Communications |
| Publisher | Nature Communications |
| Contribution | algorithm |
| DOI | 10.1038/s41467-023-43010-x |
| Citations (OpenAlex) | 79 |
| Venue 2-year citedness | 15.88 |
Preprint version: PepNet: A Fully Convolutional Neural Network for De novo Peptide Sequencing (2022-02-09, Research Square)
Abstract
De novo peptide sequencing, which does not rely on a comprehensive target sequence database, provides us with a way to identify novel peptides from tandem mass spectra. However, current de novo sequencing algorithms suffer from low accuracy and coverage, which hinders their application in proteomics. In this paper, we present PepNet, a fully convolutional neural network for high accuracy de novo peptide sequencing. PepNet takes an MS/MS spectrum (represented as a high-dimensional vector) as input, and outputs the optimal peptide sequence along with its confidence score. The PepNet model is trained using a total of 3 million high-energy collisional dissociation MS/MS spectra from multiple human peptide spectral libraries. Evaluation results show that PepNet significantly outperforms current best-performing de novo sequencing algorithms (e.g. PointNovo and DeepNovo) in both peptide-level accuracy and positional-level accuracy. PepNet can sequence a large fraction of spectra that were not identified by database search engines, and thus could be used as a complementary tool to database search engines for peptide identification in proteomics. In addition, PepNet runs around 3x and 7x faster than PointNovo and DeepNovo on GPUs, respectively, thus being more suitable for the analysis of large-scale proteomics data.
Methods and tools
- PepNet: Temporal convolutional network
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Cited by (24)
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- 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
- Limitations of de novo sequencing in resolving sequence ambiguity (2025) crossref
- DiffNovo: A Transformer-Diffusion Model for De Novo Peptide Sequencing (2025) crossref
- A transformer model for de novo sequencing of data independent acquisition mass spectrometry data (2025) crossref
- MassNet: billion-scale AI-friendly mass spectral corpus enables robust de novo peptide sequencing (2025) crossref
- Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing (2025) semanticscholar
- Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (2025) semanticscholar
- De Novo Peptide Sequencing for Data-independent Acquisition (DIA) Using Deep Learning (2025) crossref
- DiNovo: high-coverage, high-confidence de novo peptide sequencing using mirror proteases and deep learning (2025) crossref
- A transformer-based semi-autoregressive framework for high-speed and accurate de novo peptide sequencing (2025) both
- 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) both
- Deep Learning Methods for De Novo Peptide Sequencing (2024) crossref
- Disentangling the Complex Multiplexed DIA Spectra in De Novo Peptide Sequencing (2024) semanticscholar
- A multi-species benchmark for training and validating mass spectrometry proteomics machine learning models (2024) both
- NovoBoard: A Comprehensive Framework for Evaluating the False Discovery Rate and Accuracy of De Novo Peptide Sequencing (2024) crossref
- Transforming de novo peptide sequencing by explainable AI (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
- NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics (2024) semanticscholar
- A transformer model for de novo sequencing of data independent acquisition mass spectrometry data (2024) crossref
- π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2024) crossref