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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
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...
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...

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Updated: Jun 23, 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

Using maximum entropy model to predict protein secondary structure with single sequence.

Yong-Sheng Ding1, Tong-Liang Zhang, Quan Gu

  • 1College of Information Sciences and Technology, Donghua University, Shanghai, China. ysding@dhu.edu.cn

Protein and Peptide Letters
|May 16, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new single-sequence method for protein secondary structure prediction. It uses contextual information and a maximum entropy model, offering a promising tool for protein science.

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RNA Secondary Structure Prediction Using High-throughput SHAPE
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RNA Secondary Structure Prediction Using High-throughput SHAPE

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Last Updated: Jun 23, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
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RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Area of Science:

  • Protein Science
  • Bioinformatics
  • Computational Biology

Background:

  • Protein secondary structure prediction is crucial for understanding protein function.
  • Existing methods often rely on homologous sequences, which are not always available.
  • A need exists for accurate prediction methods using single protein sequences.

Purpose of the Study:

  • To develop a novel single-sequence method for protein secondary structure prediction.
  • To incorporate diverse contextual information into the prediction model.
  • To utilize a maximum entropy model as the prediction engine.

Main Methods:

  • Developed a novel single-sequence approach for protein secondary structure prediction.
  • Integrated various types of contextual information into the model.
  • Employed a maximum entropy model classifier for prediction.

Main Results:

  • Cross-validation tests demonstrated promising results on diverse protein datasets.
  • The new method shows potential as a valuable tool in protein science.
  • The approach may complement existing protein secondary structure prediction techniques.

Conclusions:

  • The proposed single-sequence method offers a viable alternative for protein secondary structure prediction.
  • The integration of contextual information and maximum entropy modeling enhances prediction accuracy.
  • This method provides a useful tool for researchers in protein science, especially when homologous sequences are unavailable.