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

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.