Common Errors in Peptide Sequence Analysis and Their Solutions

Peptide sequence analysis is a crucial step in proteomics research, widely used for protein identification, post-translational modification (PTM) localization, and quantitative analysis. Despite continuous advancements in mass spectrometry platforms and algorithms, researchers face numerous challenges in various experimental stages. This article systematically reviews common errors in peptide sequence analysis and proposes actionable optimization strategies to provide a reference for high-quality proteomics research.

 

I. Typical Errors in the Sample Preparation Stage

1. Insufficient or Poor Specificity of Enzymatic Digestion

※ Issue: Incomplete or non-specific digestion leads to the loss of target peptides, affecting sequence coverage and quantitative accuracy.

※ Solution:

• Reasonably control the enzyme-to-substrate ratio (recommended 1:50–1:100);

• Use two-step digestion (e.g., Lys-C followed by Trypsin) to enhance specificity;

• Extend reaction time to 12–16 hours, maintaining appropriate temperature and pH.

 

2. Residual Interfering Substances in Samples

※ Issue: Residual impurities such as salts, detergents, and nucleic acids affect mass spectrometry ionization efficiency.

※ Solution:

• Use TCA or organic solvent precipitation to remove impurities;

• Apply C18 solid phase extraction for desalting and purification;

• Use low-salt lysis buffer, avoiding residual strong detergents like SDS.

 

II. Key Mistakes in the Mass Spectrometry Acquisition Stage

1. Unreasonable Acquisition Mode Settings

※ Issue: Data-dependent acquisition (DDA) often fails to detect low-abundance peptides, and DIA requires precise window division.

※ Solution:

• Prioritize using DIA strategy for complex samples;

• Reasonably set m/z window width and scan rate;

• Use hybrid mode or intelligent acquisition algorithms to balance depth and coverage.

 

2. Low Fragmentation Efficiency and Weak Spectral Information

※ Issue: Improper collision energy settings or excessive fragmentation lead to the loss of key fragments.

※ Solution:

• Optimize collision energy (CID/HCD recommended 28–35 eV);

• Enable dynamic exclusion to avoid redundant scans;

• Adjust NCE when using labeling methods (e.g., TMT) to avoid neutral loss.

 

III. Database Search and Result Filtering Issues

1. Errors in Database Selection and Digestion Rules

※ Issue: Using non-matching databases or incorrect digestion settings leads to decreased identification efficiency.

※ Solution:

• Use high-quality databases matching the sample species (e.g., UniProt Reviewed);

• Set appropriate digestion rules, maximum missed cleavages, and variable modification sites;

• Regularly update database versions to prevent outdated entries from affecting alignment.

 

2. Inadequate Control of FDR

※ Issue: Sacrificing result credibility for higher identification numbers.

※ Solution:

• Control FDR at the peptide and protein level ≤ 1%;

• Use target-decoy strategy to evaluate false positives;

• Apply post-processing tools like Percolator to enhance score reliability.

 

3. Failure in Identifying Post-Translational Modifications (PTMs)

※ Issue: Difficulty in identifying modification mass shifts or insufficient modification localization probability.

※ Solution:

• Set high-confidence PTM types and sites (e.g., phosphorylation of Ser/Thr/Tyr);

• Use localization algorithms like PTMProphet, Ascore;

• Conduct enrichment experiments to verify the presence of key modification sites.

 

IV. Common Misconceptions in Quantitative Analysis and Biological Interpretation

1. Peptide Misassignment Leading to Quantitative Bias

※ Issue: Shared peptides among homologous proteins lead to inaccurate protein quantification.

※ Solution:

• Quantification using unique peptides;

• Use proteome clustering algorithms to reduce attribution ambiguity;

• Evaluate the confidence of quantitative data and exclude low-quality values.

 

2. Lack of systematic biological interpretation

※ Issue: Data analysis remains at the identification level without in-depth biological function exploration.

※ Solution:

• Perform functional annotation of protein results using GO, KEGG, etc.;

• Create visual maps such as heatmaps and coverage maps;

• Integrate multi-omics data (such as transcriptomics and metabolomics) to enhance result interpretability.

 

Successful peptide sequence analysis relies not only on high-performance mass spectrometry equipment but also on the scientific nature of experimental design and data processing strategies. By systematically identifying and avoiding common errors, the depth, accuracy, and biological interpretability of protein identification can be significantly enhanced. Biotech Pack BioTech provides integrated services from sample preprocessing, enzyme digestion optimization, mass spectrometry acquisition to bioinformatics analysis, leveraging high-resolution mass spectrometry platforms and standardized analysis processes. We are committed to providing life science researchers with high-quality, customized proteomics solutions to accelerate the realization of research outcomes accurately and swiftly.

 

Biotech Pack BioTech – Characterization of biological products, premium multi-omics mass spectrometry detection service provider

 

Related Services:

Peptide sequencing