How to Analyze Protein Identification Data and Choose the Best Result from Dozens of Proteins
Protein identification is typically performed using mass spectrometry, particularly liquid chromatography-tandem mass spectrometry (LC-MS/MS). The main types of data obtained from protein mass spectrometric analysis include:
1. Peptide Mass Spectra
Peptides resulting from proteolytic digestion are analyzed by mass spectrometry to determine their mass-to-charge ratios (m/z).
2. Fragment Ion Spectra
Selected peptides are further fragmented, and the m/z values of the resulting fragment ions are measured.
3. Protein Identification and Sequence Coverage
The name of the identified protein and the extent to which its peptide sequences are represented in the experimental data.
4. Confidence Score
A numerical metric representing the reliability or accuracy of the protein identification.
Further processing of the raw data is often required to obtain interpretable and meaningful results. Commonly used data analysis approaches include:
1. Database Matching
Software tools (e.g., Mascot, SEQUEST) compare the experimental peptide mass and fragment spectra to known protein databases to identify potential protein candidates.
2. False Positive Rate Estimation
Approaches such as the target-decoy strategy are employed to estimate the false discovery rate (FDR) of protein identifications.
3. Quantitative Analysis
The relative or absolute abundance of proteins across different samples can be assessed using labeling techniques (e.g., iTRAQ, TMT) or label-free quantification (e.g., LFQ).
When dozens of proteins are identified, the following parameters can be considered to determine the most reliable result:
1. Confidence Score
This is usually the primary criterion in protein identification; a higher score generally indicates a more reliable result.
2. False Positive Rate
This should be maintained within an acceptable threshold, commonly set at 1%.
3. Protein Sequence Coverage
Greater coverage suggests higher identification accuracy.
4. Reproducibility Across Replicates
Consistency of identification across repeated experiments enhances the overall confidence in the result.
In summary, protein identifications characterized by high confidence scores, low false positive rates, extensive sequence coverage, and strong reproducibility are generally considered to be the most robust and reliable.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
Related Services
How to order?
