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Related Concept Videos

Ligand Binding Sites02:40

Ligand Binding Sites

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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.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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Conserved Binding Sites01:49

Conserved Binding Sites

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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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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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The Equilibrium Binding Constant and Binding Strength02:18

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The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
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Protein-Drug Binding: Mechanism and Kinetics01:16

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Protein-drug binding refers to the interaction between drugs and proteins within the body. This binding process can occur intracellularly, involving drug interactions with enzymes or receptors within cells, or extracellularly, involving plasma proteins in the blood.
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Protein-Drug Binding: Determination Methods

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Determining protein-drug binding can be achieved through indirect and direct methods, each providing valuable insights into the interaction between proteins and drugs.
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Dowker complex based machine learning (DCML) models for protein-ligand binding affinity prediction.

Xiang Liu1,2,3, Huitao Feng2,4, Jie Wu3,5

  • 1Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore.

Plos Computational Biology
|April 6, 2022
PubMed
Summary
This summary is machine-generated.

New artificial intelligence (AI) methods use Dowker complexes for molecular representations, improving drug design efficiency. These AI-based drug design approaches show superior performance in predicting protein-ligand binding affinity.

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Area of Science:

  • Computational chemistry and cheminformatics
  • Artificial intelligence in drug discovery
  • Topological data analysis

Background:

  • Artificial intelligence (AI) is revolutionizing drug discovery by accelerating processes.
  • Efficient molecular representations are crucial for AI-based drug design.
  • Existing methods struggle with complex molecular interactions.

Purpose of the Study:

  • To introduce novel molecular representations using Dowker complexes (DC) and Riemann Zeta functions.
  • To develop AI models for drug design tasks, specifically protein-ligand binding affinity prediction.
  • To enhance the efficiency and accuracy of AI-driven drug discovery.

Main Methods:

  • Modeling molecular interactions as Dowker complexes (DC).
  • Generating multiscale representations via filtration and constructing Laplacian matrices.
  • Utilizing Riemann Zeta functions derived from spectral information as molecular descriptors.
  • Applying DC-based machine learning (DCML), including DC-based gradient boosting tree (DC-GBT).

Main Results:

  • DC-based descriptors achieved state-of-the-art results on PDBbind datasets (2007, 2013, 2016).
  • DCML models outperformed traditional molecular descriptor-based machine learning models.
  • Demonstrated superior performance in protein-ligand binding affinity prediction.

Conclusions:

  • Dowker complex-based molecular representations offer a powerful new approach for AI in drug design.
  • The proposed methods significantly improve the accuracy of molecular data analysis.
  • DCML models hold promise for broader applications in pharmaceutical research and development.