Related Experiment Video
Updated: Jun 16, 2026
Identifying Caspases and their Motifs that Cleave Proteins During Influenza A Virus Infection
Published on: July 21, 2022
Cascleave: towards more accurate prediction of caspase substrate cleavage sites
Jiangning Song1, Hao Tan, Hongbin Shen
1Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia. jiangning.song@med.monash.edu.au
Motivation:
The caspase family of cysteine proteases play essential roles in key biological processes such as programmed cell death, differentiation, proliferation, necrosis and inflammation. The complete repertoire of caspase substrates remains to be fully characterized. Accordingly, systematic computational screening studies of caspase substrate cleavage sites may provide insight into the substrate specificity of caspases and further facilitating the discovery of putative novel substrates.
Results:
In this article we develop an approach (termed Cascleave) to predict both classical (i.e. following a P(1) Asp) and non-typical caspase cleavage sites. When using local sequence-derived profiles, Cascleave successfully predicted 82.2% of the known substrate cleavage sites, with a Matthews correlation coefficient (MCC) of 0.667. We found that prediction performance could be further improved by incorporating information such as predicted solvent accessibility and whether a cleavage sequence lies in a region that is most likely natively unstructured. Novel bi-profile Bayesian signatures were found to significantly improve the prediction performance and yielded the best performance with an overall accuracy of 87.6% and a MCC of 0.747, which is higher accuracy than published methods that essentially rely on amino acid sequence alone. It is anticipated that Cascleave will be a powerful tool for predicting novel substrate cleavage sites of caspases and shedding new insights on the unknown caspase-substrate interactivity relationship.
Availability:
http://sunflower.kuicr.kyoto-u.ac.jp/ approximately sjn/Cascleave/
Contact:
jiangning.song@med.monash.edu.au; takutsu@kuicr.kyoto-u.ac.jp; james; whisstock@med.monash.edu.au
Supplementary Information:
Supplementary data are available at Bioinformatics online.
Insights
Cascleave accurately predicts caspase cleavage sites, identifying novel substrates crucial for understanding cell death and inflammation. This computational tool enhances discovery of caspase-substrate interactions.
Area of Science:
- Biochemistry
- Computational Biology
- Proteomics
Background:
- Caspases are cysteine proteases vital for cellular processes like programmed cell death and inflammation.
- Characterizing the full range of caspase substrates is essential for understanding these biological functions.
- Computational screening can reveal caspase substrate specificity and identify new substrates.
Purpose of the Study:
- To develop a computational method (Cascleave) for predicting caspase cleavage sites, including both typical and non-typical sequences.
- To improve the accuracy of caspase substrate prediction by integrating various sequence and structural features.
Main Methods:
- Developed Cascleave, a computational approach utilizing local sequence-derived profiles.
- Incorporated predicted solvent accessibility and unstructured region information.
- Employed novel bi-profile Bayesian signatures for enhanced prediction.
Main Results:
- Cascleave achieved 82.2% accuracy in predicting known caspase substrate cleavage sites using sequence profiles alone.
- Integrating additional features improved accuracy to 87.6% with a Matthews correlation coefficient (MCC) of 0.747.
- The developed method outperformed existing approaches relying solely on amino acid sequences.
Conclusions:
- Cascleave is a powerful tool for predicting novel caspase substrate cleavage sites.
- The findings offer new insights into the complex caspase-substrate interactivity.
- This work facilitates further discovery in caspase-mediated biological processes.
