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Exploiting Selective Position Labeling to Unveil the Hidden Complexity of Terminomics
Yuqing Deng1, Minghao Li1, Peicheng Lu1
1Department of Biochemistry and Molecular Biology, College of Basic Medicine, Jining Medical University, Jining 272067, China.
Molecules (Basel, Switzerland)
|April 14, 2026
Summary
Selective protein labeling methods are crucial for studying disease-related proteolytic mechanisms. Recent advances in N-/C-terminal modification strategies enhance terminomics for better proteome analysis.
Area of Science:
- Biochemistry and Molecular Biology
- Proteomics
- Chemical Biology
Background:
- Selective protein terminal labeling is vital for understanding proteolytic mechanisms in disease.
- Protein termini offer insights into functional proteome states but are often low in abundance.
- Terminomics, the study of protein termini, requires highly selective labeling approaches.
Purpose of the Study:
- To review recent advances in N-/C-terminal modification strategies for terminomics.
- To evaluate modern chemical and chemoenzymatic methods for site-specific protein labeling.
- To highlight emerging strategies for enhanced substrate adaptability and reaction efficiency.
Main Methods:
- Review of recent literature on N-/C-terminal protein modification.
- Evaluation of chemical and chemoenzymatic labeling techniques.
- Analysis of strategies leveraging engineered reactivity or enzymatic precision.
Main Results:
- Advances in N-/C-terminal modification strategies are enabling precise tracking of cleavage dynamics and protease interactions.
- Modern methods offer enhanced selectivity and efficiency for labeling low-abundance protein termini.
- Emerging strategies demonstrate improved substrate adaptability, reaction efficiency, and workflow compatibility.
Conclusions:
- Selective protein terminal labeling is essential for system-wide studies of proteolytic mechanisms.
- Recent advances in chemical and chemoenzymatic methods significantly advance the field of terminomics.
- Emerging strategies promise broader applications in analyzing cellular networks and proteolytic events.

