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Related Concept Videos

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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Multiple Regression01:25

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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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Prediction Intervals

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Related Experiment Video

Updated: Jun 2, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Calpain cleavage prediction using multiple kernel learning.

David A DuVerle1, Yasuko Ono, Hiroyuki Sorimachi

  • 1Bioinformatics Center, Kyoto University, Uji, Kyoto, Japan. dave@kuicr.kyoto-u.ac.jp

Plos One
|May 12, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced machine learning model to predict calpain protease cleavage sites, improving accuracy by identifying key substrate features and revealing subtype-specific differences.

Related Experiment Videos

Last Updated: Jun 2, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

  • Biochemistry
  • Computational Biology
  • Proteomics

Background:

  • Calpains are intracellular proteases crucial for various cellular processes.
  • Limited knowledge exists regarding calpain substrate recognition and cleavage mechanisms.
  • Existing machine learning methods struggle to predict calpain cleavage accurately.

Purpose of the Study:

  • To develop a more accurate machine learning model for predicting calpain cleavage sites.
  • To identify sequence features that determine calpain substrate specificity.
  • To investigate potential specificity differences among calpain subtypes.

Main Methods:

  • Utilized Multiple Kernel Learning (MKL), an extension of Support Vector Machines.
  • Integrated heterogeneous features: primary sequence, secondary structure, and solvent accessibility.
  • Validated predictions using mutated calpastatin sequences as an independent test set.

Main Results:

  • Achieved a 6% increase in AUC score compared to state-of-the-art methods.
  • Identified primary sequence, secondary structure, and solvent accessibility as critical for specificity.
  • Demonstrated significant specificity differences across calpain subtypes, challenging prior assumptions.

Conclusions:

  • The developed MKL model significantly enhances prediction of calpain cleavage sites.
  • Sequence features play a vital role in calpain substrate recognition.
  • Calpain subtypes exhibit distinct substrate specificities, necessitating subtype-specific analysis.