Fusion entrapment enables unbiased assessment of false discovery rate control in cascaded database searches.

Cascaded database searches boost identification sensitivity in vast proteomic search spaces but challenge false discovery rate (FDR) control. The standard target-decoy approach to FDR control relies on decoy matches providing an exchangeable and properly scaled representation of incorrect target matches. This assumption can be disrupted when protein-level filtering is used to define a reduced search space, because target and decoy entries may no longer undergo symmetric retention during database reduction. Although entrapment provides an external benchmark for assessing FDR control, conventional separate-entrapment implementations can become invalid in cascaded searches because entrapment sequences may be disproportionately discarded during protein-level filtering. Here we introduce Fusion Entrapment, a strategy that computationally fuses entrapment sequences with target proteins to preserve identical selection pressure during database reduction. Simulations show that this strategy provides accurate entrapment-based false discovery proportion (FDP) estimation in cascaded searches involving protein-level filtering. Applying Fusion Entrapment to human gut metaproteomic datasets, we further observed that conventional separate target-decoy database reduction led to substantial inflation of the entrapment-estimated FDP relative to the reported FDR threshold. In contrast, fusion target-decoy reduction maintained empirical FDR control under Fusion Entrapment assessment while retaining substantial sensitivity gains over single-step analysis.