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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 Organization01:13

Protein Organization

Overview
Protein Organization01:13

Protein Organization

Overview
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 Folding01:22

Protein Folding

Overview

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Updated: Jul 4, 2026

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

A generative, probabilistic model of local protein structure.

Wouter Boomsma1, Kanti V Mardia, Charles C Taylor

  • 1Bioinformatics Centre, Department of Biology, University of Copenhagen, Ole Maaloes Vej 5, 2200 Copenhagen N, Denmark.

Proceedings of the National Academy of Sciences of the United States of America
|June 27, 2008
PubMed
Summary

This study introduces a new probabilistic model for protein structure prediction, enabling efficient exploration of conformational space and accurate analysis of protein sequences. It offers a significant improvement over existing fragment assembly methods.

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Area of Science:

  • Computational Biology
  • Structural Biology
  • Biophysics

Background:

  • Protein structure prediction remains a significant challenge in computational biology.
  • Efficient exploration of conformational space and accurate assessment of conformational stabilities are key difficulties.
  • Current methods like fragment assembly have limitations due to discrete and nonprobabilistic approaches.

Purpose of the Study:

  • To develop a fully probabilistic, continuous model for local protein structure prediction in atomic detail.
  • To enable efficient conformational sampling and rigorous analysis of sequence-structure correlations.
  • To provide a theoretical and practical improvement over existing protein structure prediction techniques.

Main Methods:

  • Development of a fully probabilistic, continuous generative model for local protein structure.
  • Utilizing the model for efficient conformational sampling.
  • Applying the model to analyze sequence-structure correlations in the native state.

Main Results:

  • The proposed model allows for efficient sampling of protein conformations.
  • It provides a framework for rigorous analysis of local sequence-structure correlations.
  • The method overcomes limitations of discrete and nonprobabilistic approaches.

Conclusions:

  • The new probabilistic model offers a significant advancement in protein structure prediction.
  • It enhances the ability to explore conformational space and understand sequence-structure relationships.
  • This approach represents a substantial improvement over traditional fragment assembly methods.