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Protein Organization

Protein Organization

Protein Folding

Protein Folding

Protein and Protein Structure

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 Folding

Protein Folding

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 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...

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Updated: Jun 27, 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

Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary

Jiangning Song¹, Zheng Yuan, Hao Tan

¹Advanced Computational Modelling Centre, The University of Queensland, Brisbane, QLD 4072, Australia.

Bioinformatics (Oxford, England)

|October 19, 2007

Summary

This summary is machine-generated.

A new computational method accurately predicts protein disulfide connectivity patterns from primary sequences. This approach enhances protein structure prediction and aids in annotating large-scale genomic data.

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A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

Synthesis and Structure Determination of µ-Conotoxin PIIIA Isomers with Different Disulfide Connectivities

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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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Published on: July 8, 2025

Area of Science:

Computational Biology
Protein Bioinformatics
Structural Bioinformatics

Background:

Disulfide bonds are crucial covalent crosslinks stabilizing protein structures, particularly in secreted proteins.
Accurate prediction of disulfide connectivity is vital for reducing conformational search space in protein folding prediction.
There is a significant need for computational methods to accurately predict disulfide bond patterns.

Purpose of the Study:

To develop a novel computational method for predicting disulfide connectivity patterns directly from protein primary sequences.
To improve the accuracy of disulfide bond prediction compared to existing methods.
To provide a tool for aiding in protein sequence annotation and structural analysis.

Main Methods:

Employed a support vector regression (SVR) approach.
Utilized multiple sequence feature vectors and predicted secondary structures from PSIPRED.
Validated the method using 4-fold cross-validation on a non-homologous dataset.

Main Results:

Achieved prediction accuracies of 74.4% and 77.9% for proteins with 2-5 disulfide bridges.
Demonstrated that combining sequence features with predicted secondary structures significantly improves accuracy.
The developed method outperforms most existing disulfide connectivity predictors.

Conclusions:

The novel SVR-based method provides an accurate and effective approach for predicting disulfide connectivity.
This method offers a valuable complementary tool for computational assignment of disulfide bonds.
The approach aids in the annotation of protein sequences from large-scale genomic projects.