Integrating Narrow-Window DIA with AI-Powered Search Enables Deep and Reliable Functional Proteomics

Abstract

Narrow-window data-independent acquisition (nDIA) is emerging as a powerful technique for bottom-up proteomics. Here, we systematically benchmarked nDIA, wide-window DIA (wDIA), narrow-window data-dependent acquisition (nDDA), and wide-window DDA (wDDA) for rapid, single-shot proteomic analysis. For data processing, we introduced Tesorai Search, a new search engine leveraging a large pre-trained model and compared it with DIA-NN and FragPipe across both DIA and DDA datasets. Among 12 acquisition-analysis pipelines evaluated, nDIA combined with DIA-NN and Tesorai Search delivered the highest proteome coverage, identifying 10,255 and 10,766 protein groups from benchmark samples, respectively. Both search engines maintained rigorous false-discovery rate (FDR) control. While nDIA generally outperformed nDDA in sensitivity, FragPipe-DDA+ approach proved to be the most sensitive within the nDDA pipelines. However, entrapment analyses indicate that this sensitivity comes at the cost of less robust FDR control compared to Tesorai Search. As a proof of concept, we applied nDIA-MS to 17 cancer cell lines harboring DNA damage response (DDR) gene knockouts, successfully detecting significant downregulation of all targeted proteins and uncovering 81 DDR-related proteins modulated in at least one cell line. These results underscore nDIA-MS, together with DIA-NN and Tesorai Search, as a robust and scalable platform for high-throughput functional proteomic screening.