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Related Concept Videos

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.
Protein and Protein Structure02:15

Protein and Protein Structure

Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme can...
Protein and Protein Structures02:15

Protein and Protein Structures

Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme can...

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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

New tools and expanded data analysis capabilities at the Protein Structure Prediction Center.

Andriy Kryshtafovych1, Andreas Prlic, Zinoviy Dmytriv

  • 1Genome Center, University of California, Davis, California 95616, USA.

Proteins
|August 21, 2007
PubMed
Summary

The Protein Structure Prediction Center supported the CASP7 experiment by developing software for analyzing protein modeling success and visualizing structural superpositions. This facilitated the evaluation of prediction accuracy and data accessibility.

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Area of Science:

  • Structural bioinformatics
  • Computational biology
  • Biophysics

Background:

  • The Critical Assessment of protein Structure Prediction (CASP) experiment is a community-wide effort to assess the accuracy of protein structure prediction methods.
  • The Protein Structure Prediction Center plays a crucial role in organizing and evaluating submissions for CASP.

Purpose of the Study:

  • To detail the tasks undertaken by the Protein Structure Prediction Center for the CASP7 experiment.
  • To review the automated evaluation metrics used in CASP7.
  • To describe novel software tools developed for analyzing and visualizing protein structure predictions.

Main Methods:

  • Overview of CASP7 experimental setup and participant tasks.
  • Description of automated evaluation measures for protein structure predictions.
  • Development and application of Java-based software for analyzing modeling success beyond templates.
  • Implementation of visualization tools for comparing multiple structural superpositions.

Main Results:

  • Successful execution of CASP7 tasks by the Prediction Center.
  • Introduction of advanced software for in-depth analysis of prediction accuracy.
  • Enhanced visualization capabilities for comparing predicted models against experimental templates.
  • Establishment of a comprehensive CASP infrastructure and organized results web pages.

Conclusions:

  • The Center's contributions significantly supported the CASP7 experiment's success.
  • Developed tools provide deeper insights into protein structure prediction methodologies.
  • Improved data accessibility and visualization aid the scientific community in understanding prediction performance.