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Related Concept Videos

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
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Protein Folding Quality Check in the RER01:29

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Related Experiment Video

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A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Probabilistic multi-class multi-kernel learning: on protein fold recognition and remote homology detection.

Theodoros Damoulas1, Mark A Girolami

  • 1Department of Computing Science, University of Glasgow, S. A. W. Building, G12 8QQ, UK. theo@dcs.gla.ac.uk

Bioinformatics (Oxford, England)
|April 2, 2008
PubMed
Summary

This study introduces a novel multi-class kernel machine for protein fold recognition and remote homology detection. The method improves accuracy by combining diverse protein features, outperforming existing classifiers while reducing computational costs.

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Last Updated: Jul 6, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
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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:

  • Computational Biology
  • Bioinformatics
  • Machine Learning

Background:

  • Protein fold recognition and remote homology detection are complex multi-feature, multi-class problems.
  • Current pattern recognition methods show limited performance.
  • Multiple feature spaces (e.g., amino-acid composition, secondary structure, hydrophobicity, sequence alignment scores) exist, necessitating methods to integrate heterogeneous descriptors.

Purpose of the Study:

  • To develop a classification method that effectively assesses and combines diverse protein feature groups to enhance predictive performance.
  • To achieve state-of-the-art accuracy in protein fold recognition.
  • To evaluate the significance of novel protein features and string kernels.

Main Methods:

  • A single multi-class kernel machine is proposed to integrate various feature groups.
  • The method is grounded in a Bayesian hierarchical framework with a variational Bayes approximation for efficient computation.
  • Performance is evaluated on the SCOP PDB-40D and SCOP 1.53 benchmark datasets.

Main Results:

  • Achieved 70% accuracy on the SCOP PDB-40D dataset by combining global protein characteristics and sequence-alignment features.
  • Demonstrated an 8% improvement over existing multi-class k-nn classifiers.
  • Reduced computational costs and assessed the predictive power of different features and string kernels.

Conclusions:

  • The proposed multi-class kernel machine offers state-of-the-art performance in protein fold recognition.
  • The approach effectively integrates heterogeneous features, providing insights into feature significance.
  • The method is computationally efficient and improves upon existing techniques for homology detection.