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Caspases

Caspases

Caspase, a family of cysteine proteases, serve as effectors in apoptosis. The ced3 gene in C.elegans was first identified to be involved in apoptosis. This gene encodes the ced-3 caspase that is similar to the interleukin-1-beta converting enzyme or ICE in mammals. In addition to apoptosis, caspases also function in the inflammatory response. Inflammatory caspases are essential in activating pro-inflammatory cytokines that recruit immune cells and block the replication of pathogens inside cells.

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Characterization of Neuronal Lysosome Interactome with Proximity Labeling Proteomics

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LabCaS: labeling calpain substrate cleavage sites from amino acid sequence using conditional random fields.

Yong-Xian Fan¹, Yang Zhang, Hong-Bin Shen

¹Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.

|November 28, 2012

Summary

This study introduces LabCaS, a computational tool for predicting calpain protease cleavage sites in protein sequences. LabCaS accurately identifies these sites, aiding research into calpain functions and related diseases.

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Area of Science:

Biochemistry
Proteomics
Computational Biology

Background:

Calpains are Ca(2+)-dependent cysteine proteases crucial for biological processes and implicated in various pathologies.
Understanding calpain substrate cleavage is vital but experimentally challenging due to laborious and expensive validation methods.

Purpose of the Study:

To develop LabCaS, a novel computational approach for accurately predicting calpain substrate cleavage sites from amino acid sequences.
To address the limitations of previous machine-learning methods, particularly sample imbalance, in predicting cleavage sites.

Main Methods:

Developed a conditional random field algorithm to directly label potential cleavage sites from entire protein sequences.
Integrated multiple amino acid features and sequence-derived features into the LabCaS model.
Validated the approach using a jackknife test on 129 benchmark proteins.

Main Results:

LabCaS achieved an AUC score of 0.862 in a jackknife test, demonstrating high accuracy in predicting calpain cleavage sites.
The method effectively overcomes the issue of imbalanced positive and negative samples in machine learning training.
Accurate recognition of cleavage sites for most calpain proteins was achieved.

Conclusions:

LabCaS provides an accurate and efficient computational method for predicting calpain substrate cleavage sites.
This tool can significantly advance research into calpain functions, mechanisms, and their roles in disease.
The LabCaS program is publicly available for broader scientific use.