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Protein Folding

Protein Folding

Protein Folding

Protein Folding

Protein Folding

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 Organization

Protein Organization

Protein Organization

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 Organization

Protein Organization

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Updated: Jun 15, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Protein secondary structure prediction.

Walter Pirovano¹, Jaap Heringa

¹Centre for Integrative Bioinformatics VU, VU University, Amsterdam, The Netherlands.

Methods in Molecular Biology (Clifton, N.J.)

|March 12, 2010

Summary

This summary is machine-generated.

Predicting protein secondary structure computationally is challenging. Advanced machine learning and homologous sequence data improve accuracy to 80% for protein structure prediction.

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Last Updated: Jun 15, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

RNA Secondary Structure Prediction Using High-throughput SHAPE

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Published on: May 31, 2013

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Published on: July 25, 2013

Area of Science:

Computational biology
Bioinformatics
Structural biology

Background:

Protein structure prediction from sequence remains a significant challenge in molecular biology.
Computational methods have advanced significantly in understanding secondary structure formation.
Numerous secondary structure prediction methods have been developed over decades.

Purpose of the Study:

To provide an overview of secondary structure prediction methods.
To discuss the history and advances in the field.
To highlight current challenges and offer practical recommendations.

Main Methods:

Review of computational methods for protein secondary structure prediction.
Focus on machine learning algorithms and homologous sequence information.
Analysis of prediction accuracy and state-of-the-art techniques.

Main Results:

Machine learning algorithms combined with homologous sequence data achieve up to 80% prediction accuracy.
Significant progress has been made in computational secondary structure prediction.
The field continues to evolve with ongoing research.

Conclusions:

Computational approaches, particularly machine learning, are powerful tools for protein secondary structure prediction.
Further research is needed to address remaining challenges in prediction accuracy.
Practical guidelines can assist users in applying these prediction methods effectively.