MemNovo: Look Back at the Spectrum for Balanced De Novo Peptide Sequencing from Mass Spectrometry
arXiv:2606.11868v1 Announce Type: new Abstract: De novo peptide sequencing from tandem mass spectrometry is pivotal in proteomics, enabling identification of novel peptides without reference databases. While recent Transformer-based encoder-decoder models have achieved remarkable performance, we uncover a critical pathology in their inference dynamics. Through comprehensive feature scaling experiments, we demonstrate that existing auto-regressive peptide decoders tend to over-rely on generated-sequence priors while progressively under-utilizing fine-grained physical evidence from the input mas
The paper highlights a critical limitation in current Transformer-based models for de novo peptide sequencing, which is a foundational AI task in proteomics, indicating a maturation of the field where deeper pathologies are being identified.
Improving de novo peptide sequencing accuracy can accelerate drug discovery, biomarker identification, and fundamental biological research, impacting both healthcare and agricultural sectors significantly.
This research suggests a potential shift in how AI models are designed and trained for mass spectrometry data, moving towards more balanced utilization of physical evidence over sequence priors.
- · Proteomics researchers
- · Biopharmaceutical companies
- · AI model developers specializing in scientific data
- · Mass spectrometry instrument manufacturers
- · Developers of current Transformer-based models without these improvements
- · Researchers relying solely on reference databases for peptide identification
More accurate and reliable identification of novel peptides will become possible.
This improved accuracy will accelerate the discovery of new drug targets and diagnostic biomarkers.
The enhanced understanding of proteomes could lead to personalized medicine advancements and novel biotechnological applications.
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Read at arXiv cs.LG