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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 Folding01:22

Protein Folding

Overview
Protein Folding01:22

Protein Folding

Overview
Protein Folding01:25

Protein Folding

Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
Protein Structure Is Critical to Its Biological Function
Proteins perform a wide range of biological functions such as catalyzing chemical reactions, providing...
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...

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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 secondary structure prediction using a simple k-mer model.

Martin Madera1, Ryan Calmus, Grant Thiltgen

  • 1Department of Computer Science, University of Bristol, Woodland Road, Bristol BS8 1UB, UK.

Bioinformatics (Oxford, England)
|February 5, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a k-mer order model to improve protein sequence analysis by incorporating longer-range interactions. The new method enhances protein secondary structure prediction realism and accuracy without additional data.

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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Protein Structure Prediction

Background:

  • Many protein sequence analysis methods assume independence between positions, limiting their accuracy.
  • Existing methods can generate unrealistic protein structures, such as short, isolated helices.
  • There is a need for methods that capture longer-range interactions in protein sequences.

Purpose of the Study:

  • To develop a general framework for incorporating longer-range interactions into protein sequence analysis.
  • To improve the realism of protein secondary structure predictions from state-of-the-art methods.
  • To enhance prediction performance without compromising other metrics.

Main Methods:

  • Developed a k-mer order model to introduce longer-range interactions.
  • Applied the model as an additional layer to existing secondary structure prediction methods.
  • Evaluated the model's impact on prediction realism and performance metrics.

Main Results:

  • The k-mer order model successfully generated more realistic and protein-like secondary structure predictions.
  • Overall performance improved, with a 1.8% increase in the Segment OVerlap (SOV) score.
  • The probability of the real sequence given a prediction significantly improved from 0.271 to 0.385 per residue.

Conclusions:

  • The proposed framework effectively models longer-range interactions in protein sequences.
  • Incorporating this model enhances the biological plausibility of predicted protein structures.
  • The method improves prediction accuracy using existing information, demonstrating its efficiency.