A Comprehensive Analysis of Peptide Sequencing Techniques: Advantages, Limitations, and Strategic Selection

Peptides serve as critical intermediaries linking protein functions to various biological processes. They play increasingly significant roles in biomarker discovery, immunotherapy target identification, and natural product research. Peptide sequencing refers to analytical methods used to determine the amino acid sequence of peptide molecules. Composed of 20 standard amino acids linked via peptide bonds, peptides exhibit diverse biological functions, stability profiles, and binding affinities depending on their sequences. In basic research, pharmaceutical development, and clinical diagnostics, accurate sequence determination is essential for elucidating functional mechanisms, identifying novel bioactive compounds, and designing synthetic peptide therapeutics. Given the diversity in peptide sequencing technologies, each with its unique capabilities and limitations, selecting the most appropriate method requires a thorough understanding of their respective advantages, drawbacks, and application scenarios. This comprehensive review aims to guide informed decision-making by evaluating mainstream peptide sequencing approaches.

Comprehensive Overview of Mainstream Peptide Sequencing Techniques

1. Mass Spectrometry (MS)-Based Sequencing

(1) Technical Principle

Determines amino acid sequences by analyzing the mass-to-charge ratios (m/z) and fragment ion patterns of enzymatically cleaved peptide fragments. Sequence information can be extracted via database-dependent searches or de novo sequencing algorithms.

(2) Advantages

• High sensitivity, enabling detection of low-abundance or trace-level peptides.

• High throughput, allowing analysis of thousands of peptides in complex mixtures within a single run.

• Capability to identify post-translational modifications (PTMs), such as phosphorylation and acetylation.

• Strong compatibility with proteomics workflows, facilitating integrative analyses.

(3) Limitations

• Sequence reconstruction often relies on database matching; although de novo sequencing is improving, it remains technically challenging.

• Requires optimized sample preparation and fine-tuning of instrument parameters.

• Limited resolution in distinguishing cyclic peptides and structural isomers.

(4) Application Scenarios

Identification of peptides in natural products, screening of functional peptides, analysis of post-translational modifications, and prediction of neoantigens.

2. Edman Degradation

(1) Technical Principle

Sequentially cleaves and identifies N-terminal amino acid residues using phenyl isothiocyanate (PITC), allowing progressive determination of the peptide sequence.

(2) Advantages

• Truly de novo sequencing that does not require database dependence.

• High sequence accuracy, well-suited for confirming the structure of purified peptide fragments.

(3) Limitations

• Low throughput; only applicable to individual peptides.

• Incapable of detecting modified residues such as PTMs.

• Demands highly purified samples for reliable sequencing.

(4) Application Scenarios

Validation of standard peptide references, structural confirmation of known peptides, and verification of low-molecular-weight peptides.

3. Transcriptome-Based Prediction (RNA-Seq + Ribosome Profiling)

(1) Technical Principle

Predicts potential peptide products by sequencing mRNA transcripts and ribosome-protected translation sites, inferring active translation of short peptides.

(2) Advantages

• Enables discovery of non-canonical translation products, including micropeptides derived from non-coding regions.

• Serves as a valuable hypothesis-generating tool in the absence of proteomic data.

(3) Limitations

• Computational predictions do not confirm peptide expression; validation via MS is essential.

• Challenges remain in accurately identifying translation initiation sites.

(4) Application Scenarios

Discovery of novel antigens, investigation of non-coding peptides, and identification of unconventional peptide sources.

Strategic Selection of Sequencing Approaches

1. Decision-Making Based on Research Objectives

 



 

2. Decision-Making Based on Sample Type

(1) For complex samples (e.g., natural products, mixed fermentation broths): mass spectrometry is preferred.

(2) For high-purity standard peptides: Edman degradation is appropriate.

(3) For cell or tissue samples with transcriptomic data, ribosome profiling (Ribo-seq) may be employed.

Emerging Trends: Integration of Multi-Modal Sequencing Technologies

Contemporary peptide sequencing increasingly relies on integrative strategies rather than single-method approaches. Multi-technology platforms enhance reliability, coverage, and biological interpretation, as exemplified by:

• Combined MS and RNA-seq for validating translation of non-coding region-derived peptides.

• Integration of de novo sequencing with artificial intelligence algorithms to improve identification rates of unknown peptides.

• LC-MS coupled with modification-specific omics to elucidate mechanisms underlying functional peptide activity.

At MtoZ Biolabs, a high-resolution Orbitrap platform is employed in conjunction with proprietary deconvolution algorithms and customized de novo sequencing workflows to deliver high-accuracy, high-coverage peptide sequencing data. This platform is particularly effective in resolving complex peptide structures, including cyclic peptides and antimicrobial peptides. A comprehensive end-to-end service pipeline is provided, covering sample preparation, purification, MS analysis, and data interpretation, thereby enabling precise identification of functional peptides from complex biological samples.

MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.

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