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Related Concept Videos

Protein-protein Interfaces02:04

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 polypeptide...
Protein-Protein Interfaces02:04

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 polypeptide...
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,...
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...

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Related Experiment Video

Updated: May 31, 2026

Identifying Protein-protein Interaction Sites Using Peptide Arrays
07:44

Identifying Protein-protein Interaction Sites Using Peptide Arrays

Published on: November 18, 2014

Protein interaction hotspot identification using sequence-based frequency-derived features.

Quang-Thang Nguyen, Ronan Fablet, Dominique Pastor

    IEEE Transactions on Bio-Medical Engineering
    |July 12, 2011
    PubMed
    Summary
    This summary is machine-generated.

    Identifying protein hotspot residues is challenging. New digital signal processing (DSP) descriptors from amino acid sequences show promise, matching structure-based methods for predicting protein interaction hotspots.

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    Last Updated: May 31, 2026

    Identifying Protein-protein Interaction Sites Using Peptide Arrays
    07:44

    Identifying Protein-protein Interaction Sites Using Peptide Arrays

    Published on: November 18, 2014

    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

    Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
    07:08

    Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

    Published on: July 14, 2015

    Area of Science:

    • Computational biology
    • Bioinformatics
    • Structural bioinformatics

    Background:

    • Accurately identifying protein-hotspot residues is crucial for understanding protein interactions.
    • Current methods often rely on complex tertiary structure information, posing limitations.

    Purpose of the Study:

    • To develop novel sequence-based descriptors for predicting protein-hotspot residues.
    • To evaluate the efficacy of these descriptors compared to structure-based methods.

    Main Methods:

    • Utilized digital signal processing (DSP) techniques to analyze amino acid sequences.
    • Developed sequence-derived descriptors for hotspot prediction.
    • Employed a random forest classifier for predictive modeling.

    Main Results:

    • Sequence-derived descriptors achieved 79% accuracy and 75% precision independently.
    • Combining sequence descriptors with tertiary structure features improved prediction to 82% accuracy and 80% precision.

    Conclusions:

    • Digital signal processing-based sequence descriptors are effective for predicting protein-hotspot residues.
    • These descriptors offer a viable alternative or complement to structure-based approaches.
    • Integrating sequence and structure features enhances prediction performance significantly.