Hybrid Spectral Library Combining DIA-MS Data and a Targeted Virtual Library Substantially Deepens the Proteome Coverage
Ronghui Lou, Pan Tang, Kang Ding, Shanshan Li, Cuiping Tian, Yunxia Li, Suwen Zhao, Yaoyang Zhang, Wenqing Shui
Abstract
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.