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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...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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...
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,...
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...

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Related Experiment Video

Updated: Jun 23, 2026

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
07:49

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Efficient use of unlabeled data for protein sequence classification: a comparative study.

Pavel Kuksa1, Pai-Hsi Huang, Vladimir Pavlovic

  • 1Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA. pkuksa@cs.rutgers.edu

BMC Bioinformatics
|May 12, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new computational framework for protein analysis that effectively uses unlabeled data to improve fold and homology detection. By focusing on biologically relevant regions and removing database biases, the method enhances prediction accuracy and efficiency.

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Related Experiment Videos

Last Updated: Jun 23, 2026

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
07:49

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

An Integrated Approach for Microprotein Identification and Sequence Analysis
09:37

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

A New Approach for the Comparative Analysis of Multiprotein Complexes Based on 15N Metabolic Labeling and Quantitative Mass Spectrometry
08:04

A New Approach for the Comparative Analysis of Multiprotein Complexes Based on 15N Metabolic Labeling and Quantitative Mass Spectrometry

Published on: March 13, 2014

Area of Science:

  • Computational biology
  • Bioinformatics
  • Protein sequence analysis

Background:

  • Leveraging unlabeled data significantly improves predictive models in computational protein sequence analysis.
  • Semi-supervised learning enhances accuracy by supplementing labeled datasets with unlabeled protein sequences.
  • Unlabeled data, if not properly curated, can introduce biases that negatively impact model performance.

Purpose of the Study:

  • To develop a biologically motivated computational framework for effectively utilizing unlabeled protein sequence data.
  • To improve the accuracy of remote fold and homology detection by focusing on biologically relevant sequence regions.
  • To mitigate bias introduced by overly-represented sequences in large, uncurated databases.

Main Methods:

  • Developed a principled computational framework to selectively exploit unlabeled protein sequence data.
  • Integrated sequence region relevance into a semi-supervised learning approach.
  • Proposed a bias removal method for large, uncurated sequence databases.

Main Results:

  • Achieved highly accurate semi-supervised remote protein fold and homology detection.
  • Outperformed current state-of-the-art methods on three large unlabeled databases.
  • Demonstrated a significant reduction in computational running time.

Conclusions:

  • Unlabeled protein sequences, when refined, considerably improve classifier accuracy in semi-supervised settings.
  • The proposed framework effectively 'cuts and polishes' unlabeled data for enhanced predictive power.
  • This approach offers a more efficient and accurate method for protein classification.