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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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Protein-protein Interfaces02:04

Protein-protein Interfaces

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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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Conserved Binding Sites01:49

Conserved Binding Sites

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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...
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Protein Families02:47

Protein Families

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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...
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Protein Organization01:24

Protein Organization

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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....
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Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Accurate prediction of protein function using statistics-informed graph networks.

Yaan J Jang1,2, Qi-Qi Qin3,4, Si-Yu Huang3,5,6

  • 1Department of Biochemistry, University of Oxford, Oxford, UK. yaan.jang@gmail.com.

Nature Communications
|August 3, 2024
PubMed
Summary
This summary is machine-generated.

Predicting protein function from sequence alone is now possible with PhiGnet, a novel deep learning method. This approach analyzes evolutionary signatures to identify functional sites, improving accuracy and narrowing the sequence-function gap for uncharacterized proteins.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Deep learning in protein science

Background:

  • Understanding protein function is critical for medicine and biotechnology.
  • Over 200 million proteins are uncharacterized, hindering biological research.
  • Current computational methods often rely on protein structure, which is not always available.

Purpose of the Study:

  • To develop a method for predicting protein function solely from amino acid sequence.
  • To leverage evolutionary signatures for functional site identification.
  • To overcome limitations of structure-based prediction methods.

Main Methods:

  • Utilized statistics-informed graph networks (PhiGnet).
  • Incorporated evolutionary signatures to assess residue significance.
  • Developed a deep learning approach for sequence-based function prediction.

Main Results:

  • PhiGnet demonstrated superior performance compared to existing methods.
  • The method successfully predicted protein function without structural information.
  • Identified functional sites at the residue level by analyzing evolutionary data.

Conclusions:

  • Deep learning applied to evolutionary data can accurately predict protein function from sequence.
  • PhiGnet narrows the sequence-function gap, aiding in the characterization of unstudied proteins.
  • This approach provides valuable insights for interpreting protein properties and discovering new functionalities in research and biomedicine.