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Related Concept Videos

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...
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...
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...
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...
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
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

Dynamically searching for a domain for protein function prediction.

Jingyu Hou1, Yongqing Jiang

  • 1School of Information Technology, Deakin University, 221 Burwood Highway, Burwood, Victoria 3125, Australia. jingyu@deakin.edu.au

Journal of Bioinformatics and Computational Biology
|July 18, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel dynamic method for predicting protein functions using protein-protein interaction (PPI) data. It improves accuracy by adaptively selecting relevant protein subsets for function prediction.

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Last Updated: May 9, 2026

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Published on: November 3, 2011

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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:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Protein-protein interaction (PPI) data is crucial for predicting protein functions computationally.
  • Existing methods use fixed protein sets, which can be overwhelmed by noise in PPI data, distorting predictions.
  • Accurate protein function prediction is vital for understanding biological systems and disease mechanisms.

Purpose of the Study:

  • To develop a novel computational method for dynamic protein function prediction from PPI data.
  • To address limitations of existing approaches that use static prediction domains.
  • To improve the accuracy and reliability of protein function prediction by mitigating noise in PPI networks.

Main Methods:

  • Proposed a dynamic approach that treats function prediction as a process of finding an optimal prediction domain.
  • Integrated topological network structures from PPI data with semantic relationships between protein functions.
  • Developed a method to dynamically select prediction domains and predict functions based on network and functional information.

Main Results:

  • The novel method demonstrated effectiveness in predicting protein functions from real PPI datasets.
  • Dynamic domain selection led to improved prediction accuracy compared to traditional static methods.
  • The approach successfully leveraged both network topology and functional semantics for enhanced prediction.

Conclusions:

  • The proposed dynamic method offers a more robust and accurate approach to protein function prediction.
  • Adaptively selecting prediction domains is key to overcoming noise in large-scale PPI data.
  • This work advances computational methods for functional genomics and systems biology research.