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Related Concept Videos

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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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.
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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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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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Protein Organization01:24

Protein Organization

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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
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Related Experiment Video

Updated: Aug 29, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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MGPLI: exploring multigranular representations for protein-ligand interaction prediction.

Junjie Wang1, Jie Hu1, Huiting Sun1

  • 1Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China.

Bioinformatics (Oxford, England)
|September 12, 2022
PubMed
Summary
This summary is machine-generated.

Predicting drug binding affinity is crucial for drug discovery. Our new multigranularity protein-ligand interaction (MGPLI) model uses deep learning to improve prediction accuracy over existing methods.

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Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Drug discovery

Background:

  • Predicting drug binding affinity to protein targets is a key challenge in drug discovery.
  • Traditional experimental methods are time-consuming and expensive, necessitating efficient in silico approaches.
  • Deep learning models have shown promise in accelerating drug-target interaction prediction.

Purpose of the Study:

  • To develop an advanced deep learning model for accurate prediction of drug-target binding affinity.
  • To leverage Transformer architecture for capturing complex protein-ligand interactions at multiple granularities.
  • To improve upon existing state-of-the-art methods in computational drug discovery.

Main Methods:

  • Proposed a multigranularity protein-ligand interaction (MGPLI) model.
  • Utilized Transformer encoders for character-level and fragment-level feature extraction.
  • Employed convolutional neural networks and a highway layer for feature fusion and higher-level representation.

Main Results:

  • The MGPLI model demonstrated improved prediction performance on various protein-ligand interaction datasets.
  • Achieved better accuracy compared to current state-of-the-art baseline methods.
  • Successfully modeled interactions between protein residues and drug atoms/fragments.

Conclusions:

  • The MGPLI model offers a significant advancement in predicting drug-target binding affinity.
  • The model's ability to capture multigranularity features enhances prediction accuracy.
  • The developed model and its scripts are publicly available for further research.