🚀 De Novo Peptide Sequencing with InstaNovo and InstaNovo+
Upload your mass spectrometry data file (.mgf, .mzml, or .mzxml) and get peptide sequence predictions. Choose your prediction method and decoding options.
This Hugging Face Space is powered by a ZeroGPU, which is free but limited to 5 minutes per day per user—so if you test with your own files, please use only small files.
Prediction Results Preview
Example Usage:
| Upload Mass Spectrometry File (.mgf, .mzml, .mzxml) | Prediction Mode | Transformer Decoding Method |
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Notes:
- Predictions use version
instanovo-v1.1.0for the transformer-based InstaNovo model and versioninstanovoplus-v1.1.0-alphafor the diffusion-based InstaNovo+ model. - The InstaNovo+ model
instanovoplus-v1.1.0-alphais an alpha release. - Predction Modes:
- InstaNovo with InstaNovo+ refinement Runs initial prediction with the selected Transformer method (Greedy/Knapsack), then refines using InstaNovo+.
- InstaNovo Only: Uses only the Transformer with the selected decoding method.
- InstaNovo+ Only: Predicts directly using the Diffusion model (alpha release).
- Transformer Decoding Methods:
- Greedy Search: use this for optimal performance, has similar performance as Knapsack Beam Search at 5% FDR.
- Knapsack Beam Search: use this for the best results and highest peptide recall, but is about 10x slower than Greedy Search.
- Check logs for progress, especially for large files or slower methods.
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Links:
- GitHub Repository for InstaNovo
- InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments, Eloff, Kalogeropoulos et al. 2025, Nature Machine Intelligence.
If you use InstaNovo in your research, please cite:
@article{Eloff2025,
title = {InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments},
author = {Eloff, Kevin and Kalogeropoulos, Konstantinos and Mabona, Amandla and Morell, Oliver and Catzel, Rachel and Rivera-de-Torre, Esperanza and Berg Jespersen,
Jakob and Williams, Wesley and van Beljouw, Sam P. B. and Skwark, Marcin J. and Laustsen, Andreas Hougaard and Brouns, Stan J. J. and Ljungars,
Anne and Schoof, Erwin M. and Van Goey, Jeroen and auf dem Keller, Ulrich and Beguir, Karim and Lopez Carranza, Nicolas and Jenkins, Timothy P.},
year = 2025,
month = {Mar},
day = 31,
journal = {Nature Machine Intelligence},
doi = {10.1038/s42256-025-01019-5},
issn = {2522-5839},
url = {https://doi.org/10.1038/s42256-025-01019-5}
}