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Related Concept Videos

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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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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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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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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Task-Specific Scoring Functions for Predicting Ligand Binding Poses and Affinity and for Screening Enrichment.

Hossam M Ashtawy1, Nihar R Mahapatra1

  • 1Department of Electrical and Computer Engineering, Michigan State University , East Lansing, Michigan 48824-1226, United States.

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|December 1, 2017
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Summary

This study introduces task-specific machine learning scoring functions (SFs) for drug discovery, improving molecular docking, scoring, and virtual screening. New models like BT-Dock and BT-Screen outperform generic SFs, enhancing drug discovery efficiency.

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Area of Science:

  • Computational Chemistry
  • Cheminformatics
  • Machine Learning in Drug Discovery

Background:

  • Molecular docking, scoring, and virtual screening are crucial in computer-aided drug discovery.
  • Generic binding affinity-based scoring functions (SFs) are widely used but have limited accuracy across different tasks.
  • This limitation hinders cost-effective drug discovery.

Purpose of the Study:

  • To develop advanced machine learning (ML) scoring functions for enhanced drug discovery tasks.
  • To address the limitations of generic binding affinity-based SFs in predicting binding conformation, affinity, and activity.
  • To introduce task-specific and multi-task models for improved predictive performance.

Main Methods:

  • Developed BT-Score, an ensemble ML SF using boosted decision trees for binding affinity estimation.
  • Created BT-Dock, a task-specific ML SF optimized for predicting ligand binding poses.
  • Developed BT-Screen, a task-specific ML SF for ligand activity classification.
  • Proposed MT-Net, a novel multi-task deep neural network for simultaneous prediction of poses, affinities, and activity levels.

Main Results:

  • BT-Score achieved a correlation of 0.825 for binding affinity prediction.
  • BT-Dock demonstrated an average 25% improvement in ligand pose prediction compared to binding affinity-based SFs.
  • BT-Screen showed significant improvements in virtual screening tasks.
  • MT-Net outperformed conventional SFs and single-task neural networks across all tasks.

Conclusions:

  • Task-specific ML SFs significantly enhance performance in molecular docking, scoring, and virtual screening.
  • The developed models, including BT-Dock, BT-Screen, and MT-Net, offer superior accuracy and efficiency for drug discovery.
  • These advancements pave the way for more cost-effective and successful drug discovery pipelines.