De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments
preprint · bioRxiv · 2023
| Date | 2023-08-30 |
| Type | preprint |
| Venue | bioRxiv |
| Publisher | Cold Spring Harbor Laboratory |
| Contribution | algorithm |
| DOI | 10.1101/2023.08.30.555055 |
| Citations (OpenAlex) | 30 |
Peer-reviewed version: InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments (2025-04-01, Nature Machine Intelligence)
Abstract
Bottom-up mass spectrometry-based proteomics is challenged by the task of identifying the peptide that generates a tandem mass spectrum. Traditional methods that rely on known peptide sequence databases are limited and may not be applicable in certain contexts. De novo peptide sequencing, which assigns peptide sequences to the spectra without prior information, is valuable for various biological applications; yet, due to a lack of accuracy, it remains challenging to apply this approach in many situations. Here, we introduce InstaNovo, a transformer neural network with the ability to translate fragment ion peaks into the sequence of amino acids that make up the studied peptide(s). The model was trained on 28 million labelled spectra matched to 742k human peptides from the ProteomeTools project. We demonstrate that InstaNovo outperforms current state-of-the-art methods on benchmark datasets and showcase its utility in several applications. Building upon human intuition, we also introduce InstaNovo+, a multinomial diffusion model that further improves performance by iterative refinement of predicted sequences. Using these models, we could de novo sequence antibody-based therapeutics with unprecedented coverage, discover novel peptides, and detect unreported organisms in different datasets, thereby expanding the scope and detection rate of proteomics searches. Finally, we could experimentally validate tryptic and non-tryptic peptides with targeted proteomics, demonstrating the fidelity of our predictions. Our models unlock a plethora of opportunities across different scientific domains, such as direct protein sequencing, immunopeptidomics, and exploration of the dark proteome.
Methods and tools
- InstaNovo: Knapsack beam search
- InstaNovo+: Multinomial diffusion
Cites (15)
- Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly (2023) both
- Sequence-to-sequence translation from mass spectra to peptides with a transformer model (2023) 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
- Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices (2021) both
- Uncovering Thousands of New Peptides with Sequence-Mask-Search Hybrid De Novo Peptide Sequencing Framework (2019) both
- Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning (2019) both
- Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry (2018) both
- 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
- Evaluating de novo sequencing in proteomics: already an accurate alternative to database-driven peptide identification? (2017) both
- Lessons in de novo peptide sequencing by tandem mass spectrometry (2015) both
- De Novo Peptide Sequencing and Identification with Precision Mass Spectrometry (2007) both
- PepNovo: de novo peptide sequencing via probabilistic network modeling (2005) both
- PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry (2003) crossref
Cited by (17)
- Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control (2026) semanticscholar
- Bidirectional Representations Augmented Autoregressive Biological Sequence Generation (2025) semanticscholar
- A transformer model for de novo sequencing of data independent acquisition mass spectrometry data (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
- 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) 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
- π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2024) crossref
- Bidirectional de novo peptide sequencing using a transformer model (2024) both
- Multi-Modal Mass Spectrometry Identifies a Conserved Protective Epitope in S. pyogenes Streptolysin O (2023) crossref