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Related Concept Videos

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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Protein Networks02:26

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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.
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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.
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Conserved Binding Sites01:49

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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.
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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Normalized L3-based link prediction in protein-protein interaction networks.

Ho Yin Yuen1, Jesper Jansson2

  • 1Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, China. andy.aa.yuen@connect.polyu.hk.

BMC Bioinformatics
|February 23, 2023
PubMed
Summary
This summary is machine-generated.

New NormalizedL3 (L3N) predictors improve protein-protein interaction (PPI) prediction accuracy by leveraging network structure. These computational methods identify missing PPIs more effectively than existing approaches, advancing functional genomics research.

Keywords:
Complex NetworkGraph TheoryL3 PrincipleLink PredictionNetwork ModelingProtein–Protein Interaction

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Protein-protein interaction (PPI) data is crucial for functional genomics but often incomplete.
  • High-throughput experiments cannot fully map the PPI interactome.
  • Computational link prediction infers missing PPIs using known network structures.

Purpose of the Study:

  • To introduce NormalizedL3 (L3N) predictors that enhance L3-based PPI link prediction.
  • To address limitations in current L3 predictors through improved network modeling.
  • To uncover novel candidate PPIs missed by existing L3 methods.

Main Methods:

  • Developed the NormalizedL3 (L3N) link predictor formulation.
  • Validated L3N predictors computationally on multiple biological datasets (BioGRID, STRING, MINT, HuRI).
  • Compared L3N performance against existing PPI prediction methods.

Main Results:

  • L3N predictors demonstrated higher accuracy in identifying missing PPIs compared to previous methods.
  • L3N and other L3-based predictors prioritized different PPIs than general-purpose predictors.
  • Increased computation time was noted in some cases for L3N.

Conclusions:

  • L3N offers a more accurate approach to PPI link prediction.
  • Different network topological assumptions yield distinct PPI prediction outcomes.
  • Future PPI prediction can benefit from further advancements in network modeling.