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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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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-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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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.
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...
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Protein-Drug Binding: Determination Methods01:22

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.
Indirect methods involve isolating the bound drug from its free form in biological samples such as blood, serum, or plasma. These techniques aim to measure the percentage of drugs bound to proteins. Equilibrium dialysis is a commonly used method where the free drug concentration at equilibrium is measured by separating the bound...
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Ligand Binding and Linkage00:49

Ligand Binding and Linkage

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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

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Machine-learning methods for ligand-protein molecular docking.

Kevin Crampon1, Alexis Giorkallos2, Myrtille Deldossi2

  • 1Université de Reims Champagne Ardenne, CNRS, MEDyC UMR 7369, 51097 Reims, France; Université de Reims Champagne Ardenne, LICIIS - LRC CEA DIGIT, 51100 Reims, France; Atos SE, Center of Excellence in Advanced Computing, 38130 Echirolles, France.

Drug Discovery Today
|September 24, 2021
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) is revolutionizing molecular simulation for drug discovery. Machine learning (ML), particularly deep learning (DL), is overcoming key challenges in ligand-protein molecular docking.

Keywords:
Data representationDeep learningMachine learningMolecular dockingSamplingScoring

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Area of Science:

  • Computational chemistry and cheminformatics
  • Artificial intelligence and machine learning applications in drug discovery

Background:

  • Artificial intelligence (AI) is increasingly recognized as a transformative technology, akin to a new Industrial Revolution.
  • Molecular simulation, particularly ligand-protein molecular docking, is a critical area within drug discovery.
  • Traditional methods face challenges in accuracy, speed, and scalability for complex molecular interactions.

Purpose of the Study:

  • To provide a comprehensive overview of ligand-protein molecular docking techniques.
  • To explore the impact and application of machine learning (ML), with a focus on deep learning (DL), in addressing the challenges of molecular docking.
  • To highlight how AI is advancing the field of drug discovery through enhanced molecular simulation.

Main Methods:

  • Review of existing literature on ligand-protein molecular docking.
  • Analysis of machine learning and deep learning methodologies applied to molecular docking.
  • Discussion of AI-driven approaches for improving docking accuracy and efficiency.

Main Results:

  • Machine learning models, especially deep learning, demonstrate significant potential in improving the prediction of ligand-protein binding affinities and poses.
  • AI algorithms are being developed to handle large-scale datasets and complex molecular interactions more effectively than traditional methods.
  • The integration of ML/DL is accelerating the identification of potential drug candidates by refining docking protocols.

Conclusions:

  • Machine learning and deep learning are pivotal in overcoming the limitations of conventional molecular docking.
  • AI-driven approaches are poised to significantly accelerate and enhance the efficiency of drug discovery pipelines.
  • The continued development and application of AI in molecular simulation represent a major advancement in pharmaceutical research.