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Related Concept Videos

Protein Networks02:26

Protein Networks

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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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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
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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...
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In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
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Decipher the connections between proteins and phenotypes.

Xiaohui Ren1, Steven Wang2, Tao Huang1

  • 1Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.

Biochimica Et Biophysica Acta. Proteins and Proteomics
|July 25, 2020
PubMed
Summary
This summary is machine-generated.

This study links molecular mechanisms to observable traits (phenotypes) by analyzing phenotype networks and predicting protein-phenotype associations using machine learning, revealing disease insights.

Keywords:
Machine learningPhenotypePhenotype-phenotype networkProteinProtein-phenotype association

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Area of Science:

  • Integrative biology
  • Bioinformatics
  • Systems biology

Background:

  • Phenotype is crucial for understanding life and disease, serving as the external manifestation of biological processes.
  • Current research often focuses on molecular-level details, necessitating translation to phenotypic impact.
  • Bridging the gap between molecular data and observable phenotypes is essential for comprehensive biological understanding.

Purpose of the Study:

  • To develop a computational framework for predicting protein-phenotype associations.
  • To uncover underlying biological mechanisms connecting molecular functions to phenotypic outcomes.
  • To identify clusters of related phenotypes and diseases using network analysis.

Main Methods:

  • Construction of a similarity network based on phenotype ontology.
  • Application of network analysis techniques to identify phenotype/disease clusters.
  • Utilizing machine learning models to predict protein-phenotype associations, with proteins characterized by their interaction neighbors' functional profiles.

Main Results:

  • Successfully predicted protein-phenotype associations.
  • Identified distinct clusters within the phenotype network, suggesting relationships between phenotypes and diseases.
  • Demonstrated the capability of network-based functional profiles to characterize proteins for predictive modeling.

Conclusions:

  • The developed methods effectively predict protein-phenotype associations.
  • Network analysis of phenotype ontology can reveal meaningful biological relationships.
  • This approach provides a pathway to understand molecular mechanisms driving phenotypic traits and diseases.