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Mass spectrometry is the dominant technology in the field of proteomics, enabling high-throughput analysis of the protein content of complex biological samples. Due to the complexity of the instrumentation and resulting data, sophisticated computational methods are required for the processing and interpretation of acquired mass spectra. Machine learning has shown great promise to improve the analysis of mass spectrometry data, with numerous purpose-built methods for improving specific steps in the data acquisition and analysis pipeline reaching widespread adoption. Here, we propose unifying various spectrum prediction tasks under a single foundation model for mass spectra. To this end, we pre-train a spectrum encoder using de novo sequencing as a pre-training task. We then show that using these pre-trained spectrum representations improves our performance on the four downstream tasks of spectrum quality prediction, chimericity prediction, phosphorylation prediction, and glycosylation status prediction. Finally, we perform multi-task fine-tuning and find that this approach improves the performance on each task individually. Overall, our work demonstrates that a foundation model for tandem mass spectrometry proteomics trained on de novo sequencing learns generalizable representations of spectra, improves performance on downstream tasks where training data is limited, and can ultimately enhance data acquisition and analysis in proteomics experiments.
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This content will become publicly available on December 18, 2025
Oracle Embeddings for Chemical Detection
The accurate detection of chemical agents promotes many national security and public safety goals, and robust chemical detection methods can prevent disasters and support effective response to incidents. Mass spectrometry is an important tool in detecting and identifying chemical agents. However, there are high costs and logistical challenges associated with acquiring sufficient lab-generated mass spectrometry data for training machine learning algorithms, including skilled personnel, sample preparation and analysis required for data generation. These high costs of mass spectrometry data collection hinder the development of machine learning and deep learning models to detect and identify chemical agents. Accordingly, the primary objective of our research is to create a mass spectrometry data generation model whose output (synthetic mass spectrometry data) would enhance the performance of downstream machine learning chemical classification models. Such a synthetic data generation model would reduce the need to generate costly real-world data, and provide additional training data to use in combination with lab-generated mass spectrometry data when training classifiers. Our approach is a novel combination of autoencoder-based synthetic data generation combined with a fixed, apriori defined hidden layer geometry. In particular, we train pairs of encoders and decoders with an additional loss term that enforces that the hidden layer passed from the encoder to the decoder match the embedding provided by an external deep learning model designed to predict functional properties of chemicals. We have verified that incorporating our synthetic spectra into a lab-generated dataset enhances the performance of classification algorithms compared to using only the real data. Our synthetic spectra have been successfully matched to lab-generated spectra for their respective chemicals using library matching software, further demonstrating the validity of our work.
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- Award ID(s):
- 2021871
- PAR ID:
- 10620869
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-7488-9
- Page Range / eLocation ID:
- 272 to 279
- Format(s):
- Medium: X
- Location:
- Miami, FL, USA
- Sponsoring Org:
- National Science Foundation
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