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Related Concept Videos

Protein-protein Interfaces02:04

Protein-protein Interfaces

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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...
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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.
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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.
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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.
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Updated: Jun 12, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Intelligence model on sequence-based prediction of PPI using AISSO deep concept with hyperparameter tuning process.

Preeti Thareja1, Rajender Singh Chhillar1, Sandeep Dalal1

  • 1DCSA, Maharshi Dayanand University, Rohtak, Haryana, India.

Scientific Reports
|September 18, 2024
PubMed
Summary
This summary is machine-generated.

A novel Aquila Influenced Shark Smell (AISSO) model enhances protein-protein interaction (PPI) prediction accuracy to 88%. This sequence-dependent approach improves upon traditional methods for biological interpretation.

Keywords:
Aquilla influenced shark smell optimization (AISSO)Deep belief networkGene ontology (GO)Improved recurrent neural networkPPI predictionSequence-dependent features

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning in Biology

Background:

  • Protein-protein interactions (PPIs) are crucial for understanding biological functions.
  • Existing PPI prediction methods using diverse data and machine learning require performance enhancement.

Purpose of the Study:

  • To develop a sequence-dependent PPI prediction model with improved accuracy.
  • To introduce a hybrid prediction technique incorporating a novel optimization algorithm.

Main Methods:

  • Feature extraction using sequence-based, Gene Ontology, and improved semantic similarity features.
  • Prediction using hybrid neural networks (Improved Recurrent Neural Network, Deep Belief Networks) with score level fusion.
  • Optimization of neural network weights using the Aquila Influenced Shark Smell (AISSO) algorithm.

Main Results:

  • The developed AISSO-based model achieved approximately 88% accuracy in PPI prediction.
  • This performance significantly surpasses traditional prediction methods.

Conclusions:

  • The AISSO-based hybrid prediction model offers a precise and effective approach for sequence-dependent PPI prediction.
  • This method holds promise for advancing biological activity interpretation.