Hybrid Spectral Library Combining DIA-MS Data and a Targeted Virtual Library Substantially Deepens the Proteome Coverage

• Published in: iScience Vol. 23; no. 3; p. 100903
• Main Authors: Lou, Ronghui, Tang, Pan, Ding, Kang, Li, Shanshan, Tian, Cuiping, Li, Yunxia, Zhao, Suwen, Zhang, Yaoyang, Shui, Wenqing
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 27.03.2020; Elsevier
• Subjects: Analytical Chemistry; Biological Sciences; Classification of Proteins; Proteomics; Biological Sciences; Analytical Chemistry; Classification of Proteins; Proteomics
• ISSN: 2589-0042; 2589-0042
• DOI: 10.1016/j.isci.2020.100903

Data-independent acquisition mass spectrometry (DIA-MS) is a powerful technique that enables relatively deep proteomic profiling with superior quantification reproducibility. DIA data mining predominantly relies on a spectral library of sufficient proteome coverage that, in most cases, is built on data-dependent acquisition-based analysis of the same sample. To expand the proteome coverage for a pre-determined protein family, we report herein on the construction of a hybrid spectral library that supplements a DIA experiment-derived library with a protein family-targeted virtual library predicted by deep learning. Leveraging this DIA hybrid library substantially deepens the coverage of three transmembrane protein families (G protein-coupled receptors, ion channels, and transporters) in mouse brain tissues with increases in protein identification of 37%–87% and peptide identification of 58%–161%. Moreover, of the 412 novel GPCR peptides exclusively identified with the DIA hybrid library strategy, 53.6% were validated as present in mouse brain tissues based on orthogonal experimental measurement. [Display omitted] •A virtual library is built for a selected protein family using deep learning models•The hybrid library strategy vastly deepens the coverage for the targeted protein family•About 53.6% of novel GPCR peptides identified with the DIA hybrid library are validated•Extend the strategy to deep mapping of multiple transmembrane protein families Analytical Chemistry; Biological Sciences; Classification of Proteins; Proteomics