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.

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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