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Related Concept Videos

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,...
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...
Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...
Microbial Phylogeny01:28

Microbial Phylogeny

Understanding the evolutionary relationships among microorganisms is fundamental to microbial ecology and taxonomy. Phylogenetic trees are essential tools for inferring these relationships, relying primarily on comparative analyses of molecular sequences such as DNA, RNA, or proteins. In microbial studies, these trees typically depict the evolutionary paths of diverse bacterial and archaeal species by mapping genetic differences accumulated over time.Phylogenetic trees are composed of tips,...
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-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...

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Related Experiment Video

Updated: May 7, 2026

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

Combining phylogenetic profiling-based and machine learning-based techniques to predict functional related proteins.

Tzu-Wen Lin1, Jian-Wei Wu, Darby Tien-Hao Chang

  • 1Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan.

Plos One
|September 27, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel two-stage computational framework to improve protein function prediction. The enhanced method successfully links proteins with similar functions, outperforming existing phylogenetic profiling techniques.

More Related Videos

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Related Experiment Videos

Last Updated: May 7, 2026

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

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Accurate protein function annotation is crucial for understanding biological systems.
  • The exponential growth of genomic data necessitates efficient computational tools for protein analysis.
  • Phylogenetic profiling (PP) is a common method for inferring protein function based on evolutionary patterns, but its efficacy is limited in eukaryotes.

Purpose of the Study:

  • To develop an enhanced computational framework for predicting protein functional linkages.
  • To improve the performance of phylogenetic profiling methods, particularly for eukaryotic organisms.
  • To integrate machine learning with phylogenetic profiling for more accurate protein function prediction.

Main Methods:

  • A two-stage framework was proposed, combining phylogenetic profiling with machine learning.
  • The framework was designed to enhance the identification of functional relationships between proteins.
  • Performance was evaluated against existing PP-based methods.

Main Results:

  • The proposed two-stage framework demonstrated superior performance in predicting protein functional linkages.
  • The enhanced method showed significant improvements compared to traditional PP-based approaches.
  • The framework successfully addressed limitations of PP methods in eukaryotic systems.

Conclusions:

  • The developed two-stage framework offers a powerful computational approach for protein function annotation.
  • This method enhances the utility of phylogenetic profiling for systems biology research.
  • The integration of machine learning provides a robust solution for linking proteins with similar functions.