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Hybrid Spectral Library Combining DIA-MS Data and a Targeted Virtual Library Substantially Deepens the Proteome
Ronghui Lou1, Pan Tang1, Kang Ding1
1iHuman Institute, ShanghaiTech University, Shanghai 201210, China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Iscience
|February 29, 2020
Summary
This study introduces a hybrid spectral library for data-independent acquisition mass spectrometry (DIA-MS). This approach enhances proteomic profiling, significantly increasing the identification of transmembrane proteins in mouse brain tissue.
Area of Science:
- Proteomics
- Mass Spectrometry
- Bioinformatics
Background:
- Data-independent acquisition mass spectrometry (DIA-MS) offers deep proteomic profiling with high reproducibility.
- DIA data analysis typically requires comprehensive spectral libraries, often generated via data-dependent acquisition (DDA).
- Expanding proteome coverage, especially for specific protein families, remains a challenge.
Purpose of the Study:
- To develop a novel hybrid spectral library strategy for DIA-MS.
- To enhance the depth of proteomic coverage for targeted protein families.
- To improve the identification and quantification of transmembrane proteins.
Main Methods:
- Construction of a hybrid spectral library by combining DIA experiment-derived data with a deep learning-predicted virtual library.
- Application of the hybrid library to DIA-MS analysis of mouse brain tissue.
- Orthogonal experimental validation of novel peptide identifications.
Main Results:
- Substantial deepening of proteome coverage for three transmembrane protein families (GPCRs, ion channels, transporters).
- Increases in protein identification ranged from 37% to 87%.
- Increases in peptide identification ranged from 58% to 161%, with 53.6% of novel GPCR peptides validated.
Conclusions:
- The DIA hybrid library strategy significantly enhances proteomic coverage for targeted protein families.
- This approach improves the identification of transmembrane proteins, crucial for understanding biological functions.
- The method provides a validated strategy for expanding proteomic discovery in complex biological samples.
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