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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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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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Ligand Binding and Linkage00:49

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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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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

The Equilibrium Binding Constant and Binding Strength

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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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Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source
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Exploring the ability of machine learning-based virtual screening models to identify the functional groups

Thomas E Hadfield1, Jack Scantlebury1, Charlotte M Deane2

  • 1Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, UK.

Journal of Cheminformatics
|September 19, 2023
PubMed
Summary

This study introduces a synthetic data approach to evaluate machine learning models for drug discovery. It shows deep learning models are more efficient at identifying key binding groups than traditional methods, highlighting the need to address dataset biases.

Keywords:
InterpretabilityMachine learningStructure-based virtual screening

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Area of Science:

  • Computational chemistry
  • Machine learning in drug discovery
  • Bioinformatics

Background:

  • Structure-based virtual screening (SBVS) models often rely on dataset biases rather than true binding interactions.
  • Accurate identification of functional groups crucial for binding is essential for effective SBVS.

Purpose of the Study:

  • To develop a novel method for assessing machine learning (ML) models' ability to identify key binding functional groups.
  • To create a synthetic data generation framework for benchmarking SBVS models.

Main Methods:

  • Generated synthetic protein-ligand complex data using a deterministic binding rule and random pharmacophore point clouds.
  • Quantified the ground truth importance of each atom in synthetic complexes.
  • Compared ML model-derived feature attributions against ground truth.

Main Results:

  • The deep learning model PointVS showed 39% greater efficiency in identifying important functional groups compared to a random forest model.
  • Ligand-specific biases in datasets significantly hindered the performance of all tested ML models.
  • The synthetic data generation framework was made publicly available.

Conclusions:

  • The proposed synthetic data approach effectively evaluates the ability of ML models to identify true binding interactions.
  • Deep learning models demonstrate superior performance in identifying key functional groups for binding.
  • Addressing dataset biases is critical for improving the generalizability of SBVS models.