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Related Concept Videos

Intrinsically Disordered Proteins02:18

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 Proteins02:18

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...
Protein Families02:47

Protein Families

Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key locations, protein...
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...
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
Protein Networks02:26

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

Updated: May 11, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

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

Published on: January 26, 2024

A novel method of predicting protein disordered regions based on sequence features.

Tong-Hui Zhao1, Min Jiang, Tao Huang

  • 1Institute of Systems Biology, Shanghai University, Shanghai 200444, China.

Biomed Research International
|May 28, 2013
PubMed
Summary

A new computational method using Random Forest, mRMR, and Incremental Feature Selection effectively predicts protein disordered regions. This approach enhances accuracy and Matthews correlation coefficient (MCC), aiding in understanding protein structure formation.

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Paramagnetic Relaxation Enhancement for Detecting and Characterizing Self-Associations of Intrinsically Disordered Proteins
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Paramagnetic Relaxation Enhancement for Detecting and Characterizing Self-Associations of Intrinsically Disordered Proteins
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Paramagnetic Relaxation Enhancement for Detecting and Characterizing Self-Associations of Intrinsically Disordered Proteins

Published on: September 23, 2021

Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Intrinsically disordered proteins (IDPs) play crucial roles in various biological processes.
  • Accurate computational prediction of disordered regions in proteins is essential for functional studies.

Purpose of the Study:

  • To develop a novel computational method for predicting protein disordered regions.
  • To identify key features contributing to protein disorder prediction.

Main Methods:

  • Utilized Random Forest (RF), Maximum Relevancy Minimum Redundancy (mRMR), and Incremental Feature Selection (IFS).
  • Selected 128 optimal features, including 92 Position Specific Scoring Matrix (PSSM) conservation scores and 36 secondary structure features.
  • Employed a scanning and modification strategy to refine predictions.

Main Results:

  • Achieved a Matthews correlation coefficient (MCC) of 0.3895 on the training set via 10-fold cross-validation.
  • The developed method demonstrated improved accuracy (ACC) and MCC compared to DISOPRED, DISOclust, and OnD-CRF.
  • The selected features provide insights into the mechanisms underlying disordered protein structures.

Conclusions:

  • The novel computational method offers an effective approach for predicting protein disordered regions.
  • The identified features can guide experimental validation and deepen the understanding of protein disorder.
  • This work contributes to the advancement of bioinformatics tools for protein structure analysis.