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Related Concept Videos

Protein-protein Interfaces02:04

Protein-protein Interfaces

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 polypeptide...
Protein Networks02:26

Protein Networks

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,...
Factors Affecting Protein-Drug Binding: Drug Interactions01:23

Factors Affecting Protein-Drug Binding: Drug Interactions

Drug interactions are a critical aspect of pharmacology and can occur when two or more drugs compete for the same binding site. This competition can result in one drug displacing another, altering the effect of the displaced drug. Drug interactions are complex processes that rely heavily on how much of the displacer drug is present and how strongly it can bind to the same sites as the displaced drug.
Displacement interactions can have varying outcomes, ranging from toxicity to virtually...
Protein-Drug Binding: Determination Methods01:22

Protein-Drug Binding: Determination Methods

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...
Protein-Drug Binding: Mechanism and Kinetics01:16

Protein-Drug Binding: Mechanism and Kinetics

Protein-drug binding refers to the interaction between drugs and proteins within the body. This binding process can occur intracellularly, involving drug interactions with enzymes or receptors within cells, or extracellularly, involving plasma proteins in the blood.
Various forces drive these interactions, including hydrogen bonds, hydrophobic interactions, ionic bonds, electrostatic interactions, and van der Waals forces. These bonds enable drugs to bind to specific sites on proteins,...
Factors Affecting Protein-Drug Binding: Protein-Related Factors01:20

Factors Affecting Protein-Drug Binding: Protein-Related Factors

Drug binding to proteins is a key aspect of pharmacokinetics and can influence a drug's distribution, absorption, and elimination in the body. Several factors, including the drug's physiochemical properties, protein concentration, disease states, and the number of binding sites on the protein, influence this process.
The physicochemical properties of a drug play a significant role in its ability to bind to proteins. Lipophilic drugs, which dissolve in fats, oils, and lipids, can be bound by...

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Related Experiment Video

Updated: May 27, 2026

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Kernel-based data fusion improves the drug-protein interaction prediction.

Yong-Cui Wang1, Chun-Hua Zhang, Nai-Yang Deng

  • 1Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Science, Xining 810001, China. ycwang@nwipb.cas.cn

Computational Biology and Chemistry
|November 22, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new kernel-based method to predict drug-protein interactions by integrating diverse data sources. This data fusion approach improves prediction accuracy, aiding in drug discovery.

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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

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Last Updated: May 27, 2026

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

  • Computational biology
  • Bioinformatics
  • Drug discovery

Background:

  • Drug-protein interactions are fundamental to biological processes and drug development.
  • Predicting these interactions computationally accelerates novel drug discovery.
  • Current methods often rely on single data sources, limiting prediction accuracy.

Purpose of the Study:

  • To develop a novel kernel-based method for predicting drug-protein interactions.
  • To integrate heterogeneous data sources for improved prediction accuracy.
  • To validate the method's effectiveness across various drug target classes.

Main Methods:

  • Collected and integrated drug pharmacological effects, chemical structures, and protein genomic information.
  • Employed a kernel function for data fusion within a support vector machine (SVM) predictor.
  • Validated the method on enzymes, ion channels (ICs), G-protein couple receptors (GPCRs), and nuclear receptors (NRs).

Main Results:

  • Individual data sources showed predictive power for drug-target interactions.
  • Integration of multiple data sources significantly improved prediction accuracy compared to single-source methods.
  • The method uncovered more experimentally observed drug-target interactions at a similar false positive rate.

Conclusions:

  • The proposed kernel-based method efficiently integrates diverse data for drug-protein interaction prediction.
  • Data integration enhances the discovery of potential drug-target relationships.
  • This approach holds promise for advancing drug discovery research and experimental validation.