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Advancing proteomic discovery through optimized multi-stage scoring and deep learning-enhanced open search.

Chen Qian·Kaifei Wang·Pengzhi Mao·Ranfei Chen·Hao Chi
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Abstract

MOTIVATION: Protein 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. RESULTS: We 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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Deep LearningProteomicsDatabases, ProteinProteinsSearch EngineMass SpectrometrySoftwareJournal ArticleResearch Support, Non-U.S. Gov't

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Chen Qian, Kaifei Wang, Pengzhi Mao, Ranfei Chen, Hao Chi. 2026-07-01. Advancing proteomic discovery through optimized multi-stage scoring and deep learning-enhanced open search.. https://doi.org/10.1093/bioinformatics%2Fbtag224

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