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Intrinsically Disordered Proteins

Intrinsically Disordered Proteins

Intrinsically disordered proteins are a group of proteins that do not fold into specific three-dimensional structures. Their structural flexibility allows them to complement ordered proteins to perform functions that are inaccessible to rigid structures. They are more common in eukaryotes than prokaryotes and may either be exclusively intrinsically disordered or hybrid proteins, consisting of a mix of ordered and disordered regions. The absence of a rigid structure in these proteins can be...

Intrinsically Disordered Proteins

Intrinsically Disordered Proteins

Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

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Related Experiment Video

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

DISOselect: Disorder predictor selection at the protein level.

Akila Katuwawala¹, Christopher J Oldfield¹, Lukasz Kurgan¹

¹Department of Computer Science, Virginia Commonwealth University, Richmond, Virginia.

Protein Science : a Publication of the Protein Society

|October 24, 2019

Summary

DISOselect estimates the performance of protein disorder predictors using sequence properties. This approach improves prediction accuracy by recommending the best method for each protein, enhancing overall results.

Keywords:

intrinsic disorder intrinsically disordered proteins intrinsically disordered regions prediction predictive performance protein properties recommendation

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Last Updated: Jan 5, 2026

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Area of Science:

Computational biology
Bioinformatics
Protein science

Background:

Intrinsically disordered proteins (IDPs) are crucial in biological processes.
Numerous computational predictors for IDPs from protein sequences exist.
Predictor performance varies significantly for individual proteins and sequences.

Purpose of the Study:

To develop a computational method, DISOselect, for estimating the predictive performance of IDP predictors.
To guide users in selecting the most accurate disorder predictor for specific protein sequences.
To improve the overall accuracy of intrinsic disorder predictions.

Main Methods:

DISOselect estimates predictor performance based on protein sequence-derived properties.
The method evaluates 12 selected disorder predictors.
Performance estimation is independent of any specific disorder predictor's output.

Main Results:

DISOselect significantly improves predictive performance on a test set of 1,000 proteins compared to alternatives.
Empirical evidence shows a statistically significant improvement in overall predictive performance when using recommended methods.
The approach provides users with expected predictive quality for selected disorder predictors.

Conclusions:

DISOselect offers a robust method for selecting optimal intrinsic disorder predictors.
The tool enhances prediction accuracy by leveraging sequence-specific properties.
DISOselect is available as a free webserver for non-commercial use.