Peer-reviewedLicence not statedJournal of Molecular Biology
Tesorai Search: Cloud-based Database Search Engine Boosts Identifications for Mass Spectrometry Proteomics With a Pretrained Peptide-spectrum Deep-learning Model
- 1Tesorai
- 2Computational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Germany
Related
- Pretrained spectrum representations for diverse tandem mass spectrometry proteomics tasks
- Mass Spectrometry-Based Structural Proteomics and Analytical Ultracentrifugation Characterize the Structures of Oligomeric Apolipoprotein E4 and its Variants
- Cross-Platform Evaluation Reveals Sample Preparation-Driven Biological Biases despite Advances in Mass Spectrometry Performance in Serum Proteomics.
- Data processing in proteomics and metabolomics: liquid chromatography-mass spectrometry-based omics with divergent computational paradigms.
- Foundation model enables interpretable open and error-tolerant searching for mass spectrometry-based proteomics
BibTeX
@article{burq2026tesorai,
title = {{Tesorai Search: Cloud-based Database Search Engine Boosts Identifications for Mass Spectrometry Proteomics With a Pretrained Peptide-spectrum Deep-learning Model}},
author = {Maximilien Burq and Dejan Štepec and Juan Restrepo and Jure Žbontar and Shamil Z. Urazbakhtin and Bryan Crampton and Shivani Tiwary and Rehan Chinoy and Melissa Miao and Jüergen Cox and Peter Cimermančič},
journal = {Journal of Molecular Biology},
year = {2026},
month = jul,
doi = {10.1016/j.jmb.2026.169940},
url = {https://doi.org/10.1101/2024.08.19.606805},
}
RIS
TY - JOUR
TI - Tesorai Search: Cloud-based Database Search Engine Boosts Identifications for Mass Spectrometry Proteomics With a Pretrained Peptide-spectrum Deep-learning Model
AU - Maximilien Burq
AU - Dejan Štepec
AU - Juan Restrepo
AU - Jure Žbontar
AU - Shamil Z. Urazbakhtin
AU - Bryan Crampton
AU - Shivani Tiwary
AU - Rehan Chinoy
AU - Melissa Miao
AU - Jüergen Cox
AU - Peter Cimermančič
PY - 2026
DA - 2026/07/03
JO - Journal of Molecular Biology
DO - 10.1016/j.jmb.2026.169940
UR - https://doi.org/10.1101/2024.08.19.606805
SN - 0022-2836
SN - 1089-8638
ER -