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Ligand Binding Sites02:40

Ligand Binding Sites

13.7K
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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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:
14.1K
Protein-protein Interfaces02:04

Protein-protein Interfaces

13.9K
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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Ligand Binding and Linkage00:49

Ligand Binding and Linkage

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3.5K
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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Related Experiment Video

Updated: Sep 27, 2025

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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DLSSAffinity: protein-ligand binding affinity prediction via a deep learning model.

Huiwen Wang1, Haoquan Liu2, Shangbo Ning2

  • 1School of Physics and Engineering, Henan University of Science and Technology, Luoyang 471023, China. huiwenwang@haust.edu.cn.

Physical Chemistry Chemical Physics : PCCP
|April 13, 2022
PubMed
Summary
This summary is machine-generated.

A new deep learning model, DLSSAffinity, accurately predicts protein-ligand binding affinity by integrating local structural and global sequence information. This approach enhances drug discovery by improving prediction accuracy over existing methods.

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Area of Science:

  • Computational chemistry
  • Structural biology
  • Bioinformatics

Background:

  • Protein-ligand binding affinity prediction is crucial for computer-aided drug discovery.
  • Current methods often rely on limited 3D structures or protein sequences, posing a challenge for accurate predictions.

Purpose of the Study:

  • To develop a novel deep learning approach, DLSSAffinity, for accurate protein-ligand binding affinity prediction.
  • To leverage both local structural and global sequence information for improved predictive performance.

Main Methods:

  • DLSSAffinity utilizes pocket-ligand structural pairs for local interaction prediction.
  • It incorporates full-length protein sequences and ligand SMILES for global interaction prediction.
  • The model was evaluated on the PDBbind benchmark dataset.

Main Results:

  • DLSSAffinity achieved a Pearson's R of 0.79, RMSE of 1.40, and SD of 1.35 on the test set.
  • The model demonstrated superior performance compared to existing state-of-the-art deep learning methods for binding affinity prediction.

Conclusions:

  • Combining global sequence and local structure information significantly improves the accuracy of protein-ligand binding affinity prediction.
  • DLSSAffinity represents a promising advancement in computational drug discovery.