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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.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
Protein Families02:47

Protein Families

Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key locations, protein...
Tagging and Fusion Proteins01:24

Tagging and Fusion Proteins

Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order to...
Conservation of Protein Domains02:26

Conservation of Protein Domains

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.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

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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

Applying fuzzy technologies to equivalence learning in protein classification.

József Dombi1, Attila Kertész-Farkas

  • 1Department of Informatics, University of Szeged, Szeged, Hungary.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|April 14, 2009
PubMed
Summary

This study introduces a novel supervised learning method for protein sequence similarity. It enhances protein classification accuracy by learning similarity functions tailored to specific sequence classes.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Protein sequence similarity is crucial for genome analysis but current methods struggle with varying sequence group parameters.
  • Existing similarity inference methods lack adaptability across different protein sequence groups due to parameter differences.

Purpose of the Study:

  • To develop a supervised learning framework for creating adaptable protein similarity functions.
  • To improve protein classification accuracy by learning class-specific similarity metrics.

Main Methods:

  • A novel method using a binary classifier to learn protein similarity functions from equivalent and non-equivalent sequence pairs.
  • Application of fuzzy theory techniques, including sigmoid normalization and Dombi operators, for robust sequence pair representation.
  • Development of a new parameter-weighting technique and learning approach for similarity functions, ensuring they act as valid kernels or metrics.

Main Results:

  • The learned similarity function demonstrated improved robustness and accuracy in protein classification.
  • Receiver Operator Characteristic (ROC) analysis confirmed the enhanced performance of the proposed methodology.
  • Successful application on archeal, bacterial, and eukaryotic 3-phosphoglycerate-kinase (3PGK) sequences and COG clusters.

Conclusions:

  • The supervised learning approach offers a general and adaptable framework for protein similarity assessment.
  • The integration of fuzzy theory and novel learning techniques significantly boosts protein classification accuracy.
  • This method provides a more robust and accurate tool for analyzing protein sequences across diverse biological domains.