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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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Conserved Binding Sites01:49

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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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Ligand Binding and Linkage00:49

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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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Protein-protein Interfaces02:04

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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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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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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.
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BindWeb: A web server for ligand binding residue and pocket prediction from protein structures.

Ying Xia1, Chunqiu Xia1, Xiaoyong Pan1

  • 1Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China.

Protein Science : a Publication of the Protein Society
|October 3, 2022
PubMed
Summary

Predicting protein-ligand binding sites is crucial for drug discovery. The new BindWeb server accurately identifies binding residues and pockets using integrated deep learning models, aiding biological analysis and rational drug design.

Keywords:
binding pocketsbinding residuesbioinformaticsdeep learningprotein-ligand interaction

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Drug discovery

Background:

  • Understanding protein-ligand interactions is vital for analyzing biological processes and designing new drugs.
  • Experimental determination of binding sites is complex and often limited by data availability, posing challenges for accurate prediction.
  • Existing computational methods struggle with the intricate nature of these interactions.

Purpose of the Study:

  • To develop an accessible web server, BindWeb, for predicting ligand-specific and ligand-general binding residues and pockets from protein structures.
  • To provide a user-friendly platform that integrates advanced deep learning techniques for enhanced prediction accuracy.
  • To facilitate biological process analysis and accelerate rational drug design.

Main Methods:

  • Integration of a graph neural network (GraphBind) and a hybrid convolutional neural network with a bidirectional long short-term memory network (DELIA) for binding residue identification.
  • Application of mean shift clustering to group predicted binding residues into functional binding pockets.
  • Development of the BindWeb web server for easy access and utilization of these predictive models.

Main Results:

  • BindWeb demonstrates improved performance by leveraging the complementary strengths of its integrated deep learning models (GraphBind and DELIA).
  • The web server successfully predicts ligand binding residues and clusters them into pockets, validated by experimental data and case studies.
  • The combination of methods in BindWeb offers enhanced accuracy compared to individual approaches.

Conclusions:

  • BindWeb provides a valuable and easy-to-use tool for predicting protein-ligand binding sites.
  • The server's performance highlights the benefit of integrating diverse deep learning architectures for complex biological predictions.
  • BindWeb is freely available, supporting academic research in structural bioinformatics and drug discovery.