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Updated: Feb 9, 2026

In Vitro Cleavage Assays using Purified Recombinant Drosophila Caspases for Substrate Screening
Published on: October 6, 2022
Toward more accurate prediction of caspase cleavage sites: a comprehensive review of current methods, tools and
Yu Bao1, Simone Marini2, Takeyuki Tamura1
1Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.
Abstract:
As one of the few irreversible protein posttranslational modifications, proteolytic cleavage is involved in nearly all aspects of cellular activities, ranging from gene regulation to cell life-cycle regulation. Among the various protease-specific types of proteolytic cleavage, cleavages by casapses/granzyme B are considered as essential in the initiation and execution of programmed cell death and inflammation processes. Although a number of substrates for both types of proteolytic cleavage have been experimentally identified, the complete repertoire of caspases and granzyme B substrates remains to be fully characterized. To tackle this issue and complement experimental efforts for substrate identification, systematic bioinformatics studies of known cleavage sites provide important insights into caspase/granzyme B substrate specificity, and facilitate the discovery of novel substrates. In this article, we review and benchmark 12 state-of-the-art sequence-based bioinformatics approaches and tools for caspases/granzyme B cleavage prediction. We evaluate and compare these methods in terms of their input/output, algorithms used, prediction performance, validation methods and software availability and utility. In addition, we construct independent data sets consisting of caspases/granzyme B substrates from different species and accordingly assess the predictive power of these different predictors for the identification of cleavage sites. We find that the prediction results are highly variable among different predictors. Furthermore, we experimentally validate the predictions of a case study by performing caspase cleavage assay. We anticipate that this comprehensive review and survey analysis will provide an insightful resource for biologists and bioinformaticians who are interested in using and/or developing tools for caspase/granzyme B cleavage prediction.
Insights
This study benchmarks 12 bioinformatics tools for predicting caspase and granzyme B cleavage sites, crucial for understanding programmed cell death. Results show significant variability among predictors, highlighting the need for improved computational methods.
Area of Science:
- Biochemistry
- Bioinformatics
- Molecular Biology
Background:
- Proteolytic cleavage is a critical posttranslational modification regulating cellular activities.
- Caspase and granzyme B cleavages are essential for programmed cell death and inflammation.
- Identifying all caspase and granzyme B substrates is incomplete, necessitating computational approaches.
Purpose of the Study:
- To review and benchmark 12 sequence-based bioinformatics tools for predicting caspase/granzyme B cleavage sites.
- To evaluate and compare these prediction methods based on various performance metrics.
- To provide a resource for biologists and bioinformaticians in tool selection and development.
Main Methods:
- Systematic review and benchmarking of 12 state-of-the-art sequence-based bioinformatics tools.
- Evaluation of tools based on input/output, algorithms, prediction performance, validation, and utility.
- Construction of independent datasets for assessing predictor accuracy across different species.
- Experimental validation of predictions using caspase cleavage assays.
Main Results:
- Significant variability in prediction performance was observed among the evaluated tools.
- The accuracy of predictors varied depending on the dataset and species.
- Experimental validation confirmed the utility of computational predictions in a case study.
Conclusions:
- No single bioinformatics tool consistently outperforms others for caspase/granzyme B cleavage site prediction.
- Further development and validation of computational tools are needed for accurate substrate identification.
- This comprehensive analysis serves as a valuable guide for researchers in the field.
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