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bioRxiv · 10.64898/2026.01.06.697814

DelPi Learns Generalizable Peptide-Signal Correspondence for Mass Spectrometry-Based Proteomics

Park, J.·Kim, K.·Kang, U.-B.·Kim, S.
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Abstract

Peptide identification in mass spectrometry-based proteomics has traditionally relied on handcrafted features or simplified probabilistic approaches that limit the interpretation of structured peptide evidence. We present DelPi, an open-source peptide identification framework that learns generalizable peptide-signal correspondence from raw spectra through self-supervised pre-training followed by task-specific fine-tuning. With model distillation enabling practical deployment, DelPi expands the interpretation of peptide evidence across data-independent and data-dependent acquisition while maintaining robust false discovery control.

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Park, J., Kim, K., Kang, U.-B., Kim, S.. 2026-01-07. DelPi Learns Generalizable Peptide-Signal Correspondence for Mass Spectrometry-Based Proteomics. https://doi.org/10.64898/2026.01.06.697814

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