How to Perform Qualitative Shotgun Protein Identification Using DDA Mode?
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High-depth shotgun protein identification services (>7,000 proteins per sample).
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Customized parameter optimization tailored to sample characteristics (e.g., TopN settings and fragmentation energy).
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Support for multiple post-translational modification analyses, including phosphorylation and acetylation.
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Comprehensive data reporting, along with publication-ready scientific figures.
In life science research, proteins serve as a central link between genomic information and cellular functions. To systematically identify and characterize the full complement of proteins in biological samples, shotgun protein identification has become one of the most widely adopted strategies in proteomics. This approach involves the direct introduction of enzymatically digested peptide mixtures into a mass spectrometry system, coupled with data-dependent acquisition (DDA) for high-throughput data collection, making it particularly suitable for comprehensive profiling of complex samples. In studies ranging from disease mechanism investigation and biomarker discovery to drug target identification, accurate and high-coverage protein identification is essential for research success. This article provides a systematic overview of how to perform shotgun protein identification using DDA mode, integrating experimental design considerations and key data analysis strategies to assist researchers in efficiently obtaining deep and comprehensive proteomic information.
What Is DDA Mode?
Data-dependent acquisition (DDA) is a mass spectrometry acquisition strategy in which MS/MS (MS2) scans are triggered based on the signal intensity observed in MS1 survey scans. In a typical DDA workflow, the instrument first performs a full MS1 scan and subsequently selects the top N most intense precursor ions (commonly 10–20) for fragmentation and MS2 analysis before proceeding to the next MS1 cycle. This acquisition mode is well suited for broad, unbiased profiling of complex protein samples, such as cell lysates and tissue extracts, and is therefore widely used in shotgun proteomics, often referred to as shotgun protein identification using DDA.
Workflow of Shotgun Protein Identification under DDA Mode
The typical workflow for a DDA-based shotgun proteomics experiment includes the following steps:
1. Protein Sample Preparation
(1) Sample types: complex biological mixtures such as cells, tissues, and serum.
(2) Protein extraction: lysis using RIPA or SDS buffers, combined with ultrasonication or homogenization.
(3) Protein quantification: BCA or Bradford assays.
(4) Enzymatic digestion: trypsin digestion, typically performed for 12–16 hours at 37 °C.
(5) Desalting: removal of salts and contaminants using C18 solid-phase extraction to enhance mass spectrometric sensitivity.
2. Liquid Chromatography Separation (LC)
(1) Use of high-performance nano-scale reversed-phase chromatographic columns (e.g., C18).
(2) Gradient elution times typically range from 60 to 120 minutes.
(3) Adequate peptide separation to minimize co-elution and improve identification efficiency.
3. Mass Spectrometry Acquisition Settings (MS)
Commonly used mass spectrometry platforms include the Thermo Orbitrap series (e.g., Fusion, Exploris, QE Plus), the SCIEX TripleTOF series, and the Bruker timsTOF system.
Advantages and Limitations of DDA
1. Advantages
(1) Strong performance in the identification of high-abundance proteins.
(2) Technically mature methodology with extensive literature support.
(3) Broad software ecosystem support, including tools such as MaxQuant and Proteome Discoverer.
2. Limitations
(1) Low-abundance peptides may be underrepresented due to the TopN precursor selection mechanism.
(2) Reproducibility is generally lower than that of data-independent acquisition (DIA).
(3) Limited scanning efficiency, which may result in partial loss of detectable information.
Therefore, careful optimization of TopN parameters and instrument settings is critical when performing shotgun protein identification using DDA.
Data Analysis and Protein Identification
The interpretation of DDA data relies on database-driven search engines. Commonly used tools include:
1. MaxQuant + Andromeda
(1) Support for both labeled and label-free quantification strategies.
(2) Integration of quantitative approaches such as LFQ and iBAQ.
(3) Robust capabilities for post-translational modification site identification.
2. Proteome Discoverer + Sequest HT
(1) Flexible and customizable workflow construction.
(2) Seamless integration with Thermo mass spectrometry platforms.
(3) Optional incorporation of Percolator for improved scoring and confidence estimation.
3. Database Selection Recommendations
(1) Use of UniProt or RefSeq protein databases.
(2) Ensuring appropriate species selection, removal of redundancy, and inclusion of contaminant databases.
4. Identification Criteria
(1) Control of the false discovery rate (FDR) at 1%.
(2) Requirement of at least one unique peptide for protein identification.
(3) Recommended consideration of peptide sequence coverage and manual inspection of MS/MS spectra.
These analytical strategies collectively represent essential components of high-throughput shotgun protein identification.
Strategies to Improve DDA-Based Qualitative Identification Performance
1. Sample prefractionation: high-pH reversed-phase fractionation can substantially increase proteome coverage.
2. Dynamic exclusion optimization: appropriate exclusion time settings help reduce redundant MS2 sampling.
3. Targeted enrichment: for specialized studies such as phosphoproteomics, IMAC or TiO₂ enrichment can be incorporated.
4. Instrument calibration and maintenance: maintaining a stable ion source and performing regular cleaning improve signal quality.
These strategies are applicable not only to DDA-based workflows but also contribute broadly to enhanced depth and coverage in shotgun proteomics.
Advantages of the Mass Spectrometry Platform at MtoZ Biolabs
At MtoZ Biolabs, we have established a proteomics platform centered on Orbitrap Exploris 480 combined with FAIMS Pro and nanoLC, providing:
Although emerging technologies such as DIA and PRM have gained increasing attention in recent years, DDA remains widely applied in basic and biomedical research due to its methodological maturity, high data quality, and broad applicability. Mastery of DDA-based shotgun protein identification strategies represents a critical step toward entering the field of proteomics. If you are planning proteomics experiments, we welcome you to consult MtoZ Biolabs. Based on your research objectives and sample characteristics, we will design the most suitable mass spectrometry solution for your study.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
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