AdaNovo: Adaptive De Novo Peptide Sequencing with Conditional Mutual Information
preprint · ICML 2024 · 2024
| Date | 2024-03-09 |
| Type | preprint |
| Venue | ICML 2024 |
| Publisher | arXiv |
| Contribution | algorithm |
| DOI | 10.48550/arXiv.2403.07013 |
| Citations (OpenAlex) | 2 |
Abstract
Tandem mass spectrometry has played a pivotal role in advancing proteomics, enabling the analysis of protein composition in biological samples. Despite the development of various deep learning methods for identifying amino acid sequences (peptides) responsible for observed spectra, challenges persist in \emph{de novo} peptide sequencing. Firstly, prior methods struggle to identify amino acids with post-translational modifications (PTMs) due to their lower frequency in training data compared to canonical amino acids, further resulting in decreased peptide-level identification precision. Secondly, diverse types of noise and missing peaks in mass spectra reduce the reliability of training data (peptide-spectrum matches, PSMs). To address these challenges, we propose AdaNovo, a novel framework that calculates conditional mutual information (CMI) between the spectrum and each amino acid/peptide, using CMI for adaptive model training. Extensive experiments demonstrate AdaNovo’s state-of-the-art performance on a 9-species benchmark, where the peptides in the training set are almost completely disjoint from the peptides of the test sets. Moreover, AdaNovo excels in identifying amino acids with PTMs and exhibits robustness against data noise. The supplementary materials contain the official code.
Methods and tools
- AdaNovo: Mutual info for PTMs
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Cited by (13)
- AbNovoBench: a resource and benchmarking platform for monoclonal antibody de novo sequencing (2026) crossref
- Bidirectional Representations Augmented Autoregressive Biological Sequence Generation (2025) semanticscholar
- Limitations of de novo sequencing in resolving sequence ambiguity (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
- InstaNovo-P: A de novo peptide sequencing model for phosphoproteomics (2025) crossref
- De Novo Peptide Sequencing for Data-independent Acquisition (DIA) Using Deep Learning (2025) crossref
- Deep Learning Methods for De Novo Peptide Sequencing (2024) crossref
- A multi-species benchmark for training and validating mass spectrometry proteomics machine learning models (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
- A transformer model for de novo sequencing of data independent acquisition mass spectrometry data (2024) crossref