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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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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.
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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review.

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  • 1School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea.

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Summary
This summary is machine-generated.

Deep learning is revolutionizing computational biology by accelerating the study of Protein-Protein Interactions (PPIs). This review analyzes recent advancements in deep learning for PPI analysis, aiding researchers in this dynamic field.

Keywords:
AIPPI predictionartificial intelligencebioinformaticscomputational biologydeep learningmachine learningprotein networksprotein–protein interactions

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Area of Science:

  • Computational Biology
  • Artificial Intelligence
  • Bioinformatics

Background:

  • Protein-Protein Interactions (PPIs) are fundamental to biological processes.
  • Understanding PPIs is crucial for deciphering biological systems and developing therapeutics.
  • Deep learning (AI) is increasingly applied to analyze complex biological data.

Purpose of the Study:

  • To provide a comprehensive review of deep learning methodologies applied to PPI analysis.
  • To critically assess novel developments in the field from 2021-2023.
  • To serve as a reference for researchers navigating deep learning in PPI analysis.

Main Methods:

  • Literature review of studies published between 2021 and 2023.
  • Analysis of cutting-edge deep learning techniques used in PPI analysis.
  • Synthesis of recent advancements and trends.

Main Results:

  • Identified a proliferation of deep learning applications in PPI analysis.
  • Highlighted key deep learning methodologies currently employed.
  • Showcased the rapid evolution of the field.

Conclusions:

  • Deep learning is a transformative tool in computational biology for PPI analysis.
  • This review consolidates recent progress, aiding researchers in understanding current trends.
  • The findings offer insights into the synergistic relationship between AI and PPI research.