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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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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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Protein-Drug Binding: Determination Methods01:22

Protein-Drug Binding: Determination Methods

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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.
Indirect methods involve isolating the bound drug from its free form in biological samples such as blood, serum, or plasma. These techniques aim to measure the percentage of drugs bound to proteins. Equilibrium dialysis is a commonly used method where the free drug concentration at equilibrium is measured by separating the bound...
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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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DeepBindBC: A practical deep learning method for identifying native-like protein-ligand complexes in virtual

Haiping Zhang1, Tingting Zhang2, Konda Mani Saravanan3

  • 1Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, PR China; Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518 055, PR China.

Methods (San Diego, Calif.)
|July 25, 2022
PubMed
Summary
This summary is machine-generated.

DeepBindBC, a novel deep learning model, accurately identifies native-like protein-ligand complexes (PLCs) for drug discovery. It surpasses existing methods by incorporating non-binding data and detailed features, aiding lead searching efforts.

Keywords:
Deep learningDrug virtual screeningHuman pancreatic alpha amylase inhibitorNative like protein-ligandResNet

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • Accurate identification of protein-ligand complexes (PLCs) is crucial for virtual drug screening in early drug discovery.
  • Current affinity prediction models face challenges due to limited non-binding data, lost features, and shallow neural networks.

Purpose of the Study:

  • To develop a deep learning model, DeepBindBC, for improved classification of binding and non-binding ligands.
  • To enhance the accuracy of virtual screening by incorporating non-binding interaction data and detailed feature representations.

Main Methods:

  • Proposed a deep learning model, DeepBindBC, utilizing a ResNet architecture.
  • Incorporated detailed atom type representation and non-binding interaction information.
  • Evaluated DeepBindBC performance on DUD.E testing sets against established tools.

Main Results:

  • DeepBindBC demonstrated superior performance compared to Autodock Vina, Pafnucy, and DLSCORE.
  • Identified a novel human pancreatic α-amylase binder, experimentally validated.
  • DeepBindBC can be integrated into hybrid virtual screening pipelines.

Conclusions:

  • DeepBindBC offers a more accurate and reliable method for identifying native-like PLCs in early drug discovery.
  • The developed model and its associated web server provide valuable tools for researchers in lead searching.
  • The approach addresses limitations of existing models by leveraging non-binding data and advanced deep learning techniques.