Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Factors Affecting Protein-Drug Binding: Drug Interactions01:23

Factors Affecting Protein-Drug Binding: Drug Interactions

733
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...
733
Factors Affecting Protein-Drug Binding: Drug-Related Factors01:18

Factors Affecting Protein-Drug Binding: Drug-Related Factors

673
Drug binding to proteins is a complex phenomenon influenced by various drug-related factors, each playing a significant role in the interaction between drugs and proteins within the body.
One crucial factor in drug-protein binding is the drug's lipophilicity or its affinity for fat. More lipophilic drugs tend to have higher binding extents. For example, highly lipophilic drugs like cloxacillin exhibit substantial protein binding, with as much as 95% of the drug binding to proteins. In...
673
Factors Affecting Protein-Drug Binding: Protein-Related Factors01:20

Factors Affecting Protein-Drug Binding: Protein-Related Factors

707
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...
707
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

121
Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
121
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.6K
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...
12.6K
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

129
Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
129

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

[Arthroscopic reconstruction of anterior cruciate ligament with preservation of the remnant bundle].

Zhongguo gu shang = China journal of orthopaedics and traumatology·2013
Same author

[Anterior cruciate ligament reconstruction with tendon graft enveloped by preserved remnants].

Zhongguo gu shang = China journal of orthopaedics and traumatology·2013
Same author

Genetic and molecular biological characterization of two homologous cheR genes from Leptospira interrogans.

Acta biochimica et biophysica Sinica·2013
Same author

Upregulation of glycoprotein nonmetastatic B by colony-stimulating factor-1 and epithelial cell adhesion molecule in hepatocellular carcinoma cells.

Oncology research·2013
Same author

Effect of implantation of biodegradable magnesium alloy on BMP-2 expression in bone of ovariectomized osteoporosis rats.

Materials science & engineering. C, Materials for biological applications·2013
Same author

[Texture variation of CC 5052 aluminum alloy slab from surface to center layer by XRD].

Guang pu xue yu guang pu fen xi = Guang pu·2013

Related Experiment Video

Updated: May 5, 2026

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

3.2K

Predicting drug-target interactions using probabilistic matrix factorization.

Murat Can Cobanoglu1, Chang Liu, Feizhuo Hu

  • 1Department of Computational & Systems Biology and ‡Department of Pathology, School of Medicine, University of Pittsburgh , Pennsylvania 15213, United States.

Journal of Chemical Information and Modeling
|December 3, 2013
PubMed
Summary

This study introduces probabilistic matrix factorization (PMF) for analyzing drug-target interactions, aiding drug repurposing and side effect prediction. The method effectively identifies new drug-target pairs, particularly those relevant to neurobiological disorders.

More Related Videos

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

2.8K
Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

21.0K

Related Experiment Videos

Last Updated: May 5, 2026

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

3.2K
Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

2.8K
Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

21.0K

Area of Science:

  • Computational biology
  • Pharmacology
  • Bioinformatics

Background:

  • Quantitative analysis of drug-target interactions is crucial for drug repurposing and predicting adverse effects.
  • Probabilistic matrix factorization (PMF) offers a novel computational approach for analyzing large-scale interaction networks.

Purpose of the Study:

  • To present a PMF-based method for quantitative analysis of drug-target interactions.
  • To assess the utility of PMF for drug repurposing, side effect prediction, and novel interaction discovery.

Main Methods:

  • Utilized probabilistic matrix factorization (PMF) to analyze the DrugBank database of known drug-target interactions.
  • Clustered drugs based on PMF latent variables to identify phenotypic similarities.
  • Benchmarked the PMF method against existing approaches using varying dataset sizes.

Main Results:

  • PMF-based drug clustering revealed phenotypic similarity independent of 3D structural similarity.
  • The method demonstrated superior performance on large datasets, such as enzyme and ion channel interactions.
  • On average, 88% of the top 100 predictions accurately identified hidden drug-target interactions when 70% were masked.
  • De novo predictions identified novel potential drug-target interactions, with a notable enrichment of pairs relevant to neurobiological disorders.

Conclusions:

  • PMF is an effective computational tool for analyzing complex drug-target interaction networks.
  • The approach facilitates drug repurposing, side effect assessment, and the discovery of novel therapeutic targets.
  • PMF shows particular promise for identifying drug-target interactions relevant to neurobiological conditions.