False discovery rate control for trustworthy AI-based de novo peptide sequencing

Abstract

AI-based de novo peptide sequencing predicts peptide sequences from tandem mass spectra, enabling identification beyond predefined databases but leaving prediction reliability difficult to assess, particularly with respect to false discovery rate (FDR). In database search, FDR control is provided by target-decoy competition over a finite search space, whereas de novo predictions are generated in open sequence space and lack naturally matched sequence-level decoys. Here we introduce Counterpart Calibration Theory (CCT), a theory-guided framework that reframes de novo FDR control as a four-group score-ranking and threshold-selection problem over target-side predictions and matched counterpart-side comparators. Implemented in {pi}-NovoQC, CCT provides dual-level FDR control at the peptide-spectrum match and peptide levels. Across models, datasets, instruments and acquisition modes, {pi}-NovoQC achieves stable FDR control while preserving identification yield. In large-scale proteomic applications, {pi}-NovoQC recovers low-abundance in-database peptides missed by database search and provides de novo-supported protein-group and variant evidence.