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Updated: Mar 13, 2026

A Fluorogenic Peptide Cleavage Assay to Screen for Proteolytic Activity: Applications for coronavirus spike protein activation

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Published on: January 9, 2019

Artificial intelligence-driven protease cleavage site prediction: Advances and challenges.

Lin Yang¹, Zengtao Ji², Cuiling Liu³

¹Beijing Technology and Business University, Beijing 100048, China; Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Laboratory for Agriproduct Quality Traceability, Beijing Technology and Business University, Beijing 100048, China.

Biotechnology Advances

|March 11, 2026

Summary

Artificial intelligence (AI) is revolutionizing protease cleavage site prediction by improving accuracy and generalizability in computational bioinformatics. This review explores AI

Keywords:

Deep learning Machine learning Prediction model Protease Substrate cleavage sites

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Published on: December 21, 2019

Area of Science:

Computational Bioinformatics
Artificial Intelligence in Proteomics
Molecular Biology and Drug Discovery

Background:

Proteolysis is essential for regulating protein function and cellular processes.
Traditional computational methods struggle with complex protease substrate recognition patterns.
Limited generalizability hinders the effectiveness of existing protease cleavage site prediction tools.

Purpose of the Study:

To review the transformative impact of artificial intelligence (AI) on protease cleavage site prediction.
To examine how AI addresses limitations in traditional computational bioinformatics approaches.
To outline progress, challenges, and future directions for AI-driven prediction in the biomedical field.

Main Methods:

AI-driven feature extraction to capture complex biological patterns.
Integration of multidimensional regulatory information for enhanced prediction.
Modeling nonlinear relationships inherent in protease-substrate interactions.

Main Results:

AI successfully models proteolytic mechanism heterogeneity, improving prediction generalizability.
Representative studies show AI's efficacy across diverse protease families.
AI applications demonstrate significant advancements in predicting protease cleavage sites.

Conclusions:

AI offers a powerful framework to overcome traditional limitations in protease cleavage site prediction.
Key challenges include data quality, multimodal feature integration, and model interpretability.
Future AI development promises enhanced prediction efficiency and broader biomedical applications.