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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...

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Updated: Sep 11, 2025

Exploring Caspase Mutations and Post-Translational Modification by Molecular Modeling Approaches

Exploring Caspase Mutations and Post-Translational Modification by Molecular Modeling Approaches

Published on: October 13, 2022

Highlights of Model Quality Assessment in CASP16.

Alisia Fadini¹, Recep Adiyaman², Shaima N Alhaddad²

¹Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK.

|August 14, 2025

Summary

This study evaluates model quality assessment methods in structural bioinformatics during CASP16. Leading approaches for predicting protein structure accuracy were tested, revealing their strengths and weaknesses.

Keywords:

computational molecular biology molecular models protein conformation protein domains

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Area of Science:

Structural bioinformatics
Computational biology
Protein structure prediction

Background:

Model quality assessment (MQA) is crucial for structural bioinformatics, aiding both structure predictors and experimentalists.
The CASP16 Evaluation of Model Accuracy (EMA) category introduced new challenges for assessing protein model quality.

Purpose of the Study:

To present detailed results from leading CASP16 EMA methods.
To highlight the strengths and limitations of current MQA approaches for protein structures.

Main Methods:

Evaluation of global and local quality estimation for multimeric assemblies (QMODE1 and QMODE2).
Assessment of a novel QMODE3 challenge involving selection of top models from large-scale predictions (MassiveFold).

Main Results:

Analysis of performance for several leading CASP16 EMA methods.
Identification of specific strengths and limitations of the evaluated MQA techniques.

Conclusions:

The study provides insights into the effectiveness of various MQA methods in structural bioinformatics.
Findings will inform the development of improved tools for assessing protein model accuracy.