ArticleBioinformatics (Oxford, England)2026
Advancing proteomic discovery through optimized multi-stage scoring and deep learning-enhanced open search.
Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
motivationProtein search engines are essential for interpreting mass spectrometry data into biological insight. Current tools often face limitations in sensitivity when analyzing complex modern datasets, and lack a unified framework that effectively integrates deep learning features for both restricted and open searches, especially for scenarios aimed at discovering unknown modifications.
resultsWe present pFind+, a high-performance search engine for data-dependent acquisition (DDA) proteomics, extending pFind. It introduces an enhanced raw scoring that delivers substantially improved pre-filtering ability, while recovering most of the computational overhead through a tailored acceleration strategy. Coupled with an enhanced rescoring framework that effectively integrates deep learning features, pFind+ uniquely supports high-sensitivity, DL-enhanced open search, enabling comprehensive PTM discovery while incorporating hardware-aware inference optimizations for practical deployment. Evaluations across diverse datasets demonstrate its superior sensitivity, with gains of 12.7%-29.3% (average 17.9%) in restricted search and 8.0%-38.4% (average 25.8%) in open search over the best existing tools.
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