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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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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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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 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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The Two-State Receptor Model01:29

The Two-State Receptor Model

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The two-state receptor model explains a drug's interaction with receptors, such as G protein-coupled receptors and ligand-gated ion channels, to induce or inhibit a biological response. When no natural ligands are present, a receptor exists in an equilibrium of inactive (Ri) and active (Ra) conformations. The inactive form does not produce a response, while the active form generates a basal effect known as constitutive activity.
The binding affinity of a drug determines its interaction with...
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Related Experiment Video

Updated: Jun 6, 2025

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Interformer: an interaction-aware model for protein-ligand docking and affinity prediction.

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  • 1AI Lab, Tencent, Shenzhen, China. mosquitolkfo@gmail.com.

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|November 25, 2024
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Interformer, a novel deep learning model, enhances protein-ligand docking and affinity prediction by accurately modeling interactions. This approach improves generalization and interpretability in structure-based drug design.

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Area of Science:

  • Computational chemistry
  • Structural biology
  • Drug discovery

Background:

  • Deep learning models are increasingly applied to protein-ligand docking and affinity prediction for structure-based drug design.
  • Current models often fail to capture intricate atom-level interactions, limiting generalization and interpretability.

Purpose of the Study:

  • To propose Interformer, a unified Graph-Transformer-based model for improved protein-ligand interaction modeling.
  • To enhance generalization and interpretability in deep learning for drug design.

Main Methods:

  • Utilizing a Graph-Transformer architecture to capture non-covalent interactions.
  • Employing an interaction-aware mixture density network for detailed interaction modeling.
  • Introducing a negative sampling strategy for affinity prediction correction.

Main Results:

  • Demonstrated effectiveness and universality across benchmark and in-house datasets.
  • Confirmed improved performance through accurate modeling of specific protein-ligand interactions.
  • Achieved state-of-the-art (SOTA) performance on docking tasks.

Conclusions:

  • Interformer effectively models protein-ligand interactions, advancing structure-based drug design.
  • The model offers improved generalization and interpretability compared to existing methods.
  • The proposed approach sets a new SOTA in protein-ligand docking and affinity prediction.