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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 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,...
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...
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
Protein Organization01:24

Protein Organization

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.
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...

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A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Improving protein function prediction methods with integrated literature data.

Aaron P Gabow1, Sonia M Leach, William A Baumgartner

  • 1Department of Pharmacology, University of Colorado at Denver and Health Sciences Center, MS 8303, RC-1 South, 12801 East 17th Avenue, L18-6101, PO Box 6511, Aurora, CO 80045, USA. gabow@cbio.mskcc.org

BMC Bioinformatics
|April 17, 2008
PubMed
Summary
This summary is machine-generated.

Adding literature co-occurrence data significantly improves protein function prediction using graph-theoretic algorithms. This method enhances protein-protein interaction networks, offering a valuable resource for understanding protein roles and potential drug targets.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Determining the function of uncharacterized proteins is a significant challenge in the post-genomic era.
  • Protein function prediction is crucial for understanding biological pathways, identifying drug targets, and exploring protein modifications.
  • Graph-theoretic approaches using protein-protein interaction networks are common for predicting protein functions.

Purpose of the Study:

  • To systematically evaluate the utility of literature co-occurrence data for protein function prediction.
  • To introduce and validate a novel method for quantifying the reliability of co-occurrence data.
  • To assess the performance of prediction algorithms across different species and varying levels of annotation specificity.

Main Methods:

  • Incorporated literature co-occurrence data into the Functional Flow graph-theoretic algorithm.
  • Developed a new method to quantify the reliability of protein co-occurrence from scientific abstracts.
  • Compared the performance of co-occurrence data with genetic interaction data and traditional co-occurrence methods.

Main Results:

  • Including co-occurrence data substantially improved prediction performance in yeast, fly, and worm compared to using only protein-protein interactions.
  • Co-occurrence data outperformed genetic interaction data when supplementing protein-protein interactions, highlighting its unique contribution.
  • The developed co-occurrence reliability quantification method demonstrated superior performance, especially at a 10% threshold, balancing coverage and accuracy.

Conclusions:

  • Literature co-occurrence data is a valuable supplemental resource for graph-theoretic protein function prediction algorithms.
  • The readily available and rapidly growing corpus of scientific literature makes co-occurrence data accessible for most organisms.
  • Co-occurrence data provides critical connections within interaction networks, enhancing the ability of algorithms to predict functions, particularly for organisms with limited protein interaction data.