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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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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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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

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Protein-Protein Docking: Past, Present, and Future.

Sharon Sunny1, P B Jayaraj2

  • 1Department of Computer Science and Engineering, National Institute of Technology, Calicut, India. sharon_p180018cs@nitc.ac.in.

The Protein Journal
|November 17, 2021
PubMed
Summary
This summary is machine-generated.

Computational methods for protein interaction prediction are advancing but still lack experimental accuracy. This review assesses current docking algorithms, challenges, and the future role of artificial intelligence in this evolving field.

Keywords:
Artificial intelligenceDeep learningGeometric algorithmsNature-inspired algorithmsProtein–protein dockingSearching and scoring

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Structural Biology

Background:

  • Proteins are crucial for biological functions, and understanding their interactions is key.
  • Experimental methods for studying protein interactions are complex and time-consuming.
  • Computational methods offer an alternative for predicting protein-protein interactions.

Purpose of the Study:

  • To review existing computational docking algorithms for protein interaction prediction.
  • To identify current challenges and limitations in computational prediction methods.
  • To explore the future scope, including the potential of artificial intelligence.

Main Methods:

  • Assessment of current computational docking algorithms.
  • Discussion of blind docking techniques and information-driven approaches.
  • Analysis of the evolving landscape of protein interaction prediction.

Main Results:

  • Existing computational methods, while automated, do not yet match experimental accuracy.
  • Blind docking is useful when only individual protein structures are known.
  • Information-driven approaches are becoming more viable with increasing structural data.

Conclusions:

  • The field of protein interaction prediction is still evolving, with ongoing challenges in achieving experimental reliability.
  • Artificial intelligence is poised to significantly impact and advance computational docking and interaction prediction.
  • Further development is needed to bridge the gap between computational predictions and experimental validation.