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Related Concept Videos

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...
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...
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,...
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...
Ligand Binding Sites02:40

Ligand Binding Sites

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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Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells
08:38

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells

Published on: March 3, 2015

Revisiting the negative example sampling problem for predicting protein-protein interactions.

Yungki Park1, Edward M Marcotte

  • 1Center for Systems and Synthetic Biology, Institute of Cellular and Molecular Biology, University of Texas at Austin, Austin, Texas 78712, USA. yungki@mail.utexas.edu

Bioinformatics (Oxford, England)
|September 13, 2011
PubMed
Summary
This summary is machine-generated.

Understanding subset sampling for negative protein-protein interactions (PPIs) is crucial for accurate computational predictions. Distinguishing between sampling for testing and training improves predictive algorithm development.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Computational methods predict protein-protein interactions (PPIs) using protein sequence features.
  • Large numbers of non-interacting protein pairs (negative PPIs) necessitate subset sampling for training and validation.

Purpose of the Study:

  • To clarify the distinct types of subset sampling for negative PPIs: one for unbiased testing and another for biased training.
  • To address erroneous conclusions regarding the efficacy of PPI prediction algorithms due to sampling confusion.

Main Methods:

  • Distinguishing between subset sampling for cross-validated testing (unbiased) and training (potentially biased).
  • Analyzing the impact of different sampling strategies on the performance of PPI prediction algorithms.

Main Results:

  • Two fundamentally different types of subset sampling for negative PPIs exist: for testing and for training.
  • Confusion between these sampling types led to incorrect assessments of PPI prediction accuracy.
  • Both protein sequence features and protein 'hubbiness' are vital for effective PPI prediction.

Conclusions:

  • Appropriate use of random versus balanced sampling is essential for reliable PPI prediction.
  • Correctly applying subset sampling methods enhances the accuracy and generalizability of computational PPI prediction models.