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Related Concept Videos

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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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.
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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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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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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Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

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Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
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Classical and Machine Learning Methods for Protein - Ligand Binding Free Energy Estimation.

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Summary

Accurate binding free energy estimation is crucial for drug discovery but remains challenging. This review explores various computational methods, including Thermodynamic Integration (TI) and machine learning, to improve protein-ligand binding predictions.

Keywords:
Bennett's acceptance ratio (BAR)Free energyalchemical methodscomputer-aided drug discoverymachine learningthermodynamic integration (TI)

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Biomolecular modeling

Background:

  • Accurate binding free energy estimation is vital for computer-aided drug discovery.
  • Despite decades of research, challenges like algorithmic complexity, time constraints, and reproducibility persist.
  • Quantitative estimation of drug candidate binding to biomolecular targets is a key goal.

Purpose of the Study:

  • To review diverse computational methods for binding free energy estimation.
  • To discuss the advantages and disadvantages of established techniques.
  • To explore the potential of machine learning in this field.

Main Methods:

  • Review of established free energy calculation methods: Thermodynamic Integration (TI), Bennett's Acceptance Ratio (BAR), Free Energy Perturbation (FEP), and alchemical approaches.
  • Discussion of algorithmic complexities and practical limitations.
  • Exploration of machine learning applications for protein-ligand binding free energy.

Main Results:

  • Established methods like TI, BAR, and FEP offer quantitative insights but face challenges.
  • Alchemical methods provide alternative approaches to free energy calculations.
  • Machine learning presents a promising avenue for enhancing prediction accuracy and efficiency.

Conclusions:

  • A comprehensive understanding of existing methods is essential for advancing drug discovery.
  • Addressing the limitations of current techniques is critical for reliable binding free energy estimation.
  • Integrating machine learning holds significant potential to overcome current challenges and improve computational drug design.