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-Related Factors01:18

Factors Affecting Protein-Drug Binding: Drug-Related Factors

486
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...
486
Tissue-Drug Binding: Localization of Drugs and its Significance01:24

Tissue-Drug Binding: Localization of Drugs and its Significance

445
Body tissues, comprising approximately 40% of the body weight, are crucial in drug distribution and localization. These tissues can serve as drug storage sites, competing with plasma binding sites for drug molecules.
Drugs can bind to different tissue components, enhancing their distribution and localization. The factors influencing drug localization in tissues include the drug's lipophilicity, structural characteristics, tissue perfusion rate, and pH differences. These factors determine...
445
Factors Affecting Protein-Drug Binding: Drug Interactions01:23

Factors Affecting Protein-Drug Binding: Drug Interactions

605
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...
605
Drug Distribution: Tissue Binding01:21

Drug Distribution: Tissue Binding

4.1K
Upon entering the systemic circulation, drugs can distribute into the interstitial and intracellular fluid of various tissue cells. This distribution is facilitated by the binding of drugs to different cellular components within tissues, which may lead to drug accumulation in specific areas. Drugs bound to tissue components serve as reservoirs that release free drugs back into the system, prolonging the drug's overall action. However, this accumulation can also result in local toxicity.
For...
4.1K
Drug Binding to Blood Components01:30

Drug Binding to Blood Components

533
When drugs enter systemic circulation, they interact with various components of the blood, including proteins such as human serum albumin (HSA), α1-acid glycoprotein (AAG), lipoproteins, globulins, and red blood cells (RBCs).
HSA is the most abundant plasma protein and is vital in drug binding. It contains distinct drug-binding sites, with different drugs exhibiting affinity for specific sites. There are three main drug-binding domains for HSA: sites I, II, and III. These domains are...
533
Drug Distribution: Plasma Protein Binding01:29

Drug Distribution: Plasma Protein Binding

8.9K
Drugs predominantly attach to plasma proteins, with only a small percentage remaining unbound. The unbound portion can be calculated as one minus the bound fraction. Acidic drugs form large, inactive complexes by reversibly binding to plasma albumin, which prevents them from diffusing across biological barriers. These drug-protein complexes act as reservoirs for the drugs. As the concentration of unbound drugs decreases, these complexes quickly dissociate to release the free drug, maintaining...
8.9K

You might also read

Related Articles

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

Sort by
Same author

Trypanosoma cruzi cell atlas as a single-cell resource for understanding parasite population heterogeneity and differentiation.

Nature communications·2026
Same author

A genome-wide genetic screen identifies a novel kDNA replication protein in trypanosomes.

Nucleic acids research·2026
Same author

FMOPhore for hotspot identification and efficient fragment-to-lead growth strategies.

Nature communications·2026
Same author

Spliceosome induction is a druggable dependency of RAS-driven senescence and cancer.

Nature communications·2026
Same author

Genetic origins and proteomic consequences of kinetoplast loss in trypanosomes.

PLoS pathogens·2026
Same author

Acoziborole resistance associated mutations in Trypanosoma brucei CPSF3.

PLoS pathogens·2026

Related Experiment Video

Updated: Feb 6, 2026

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
11:34

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Published on: August 9, 2019

7.1K

Decoding efficacy and resistance space at a drug binding site.

Simone Altmann1, Cesar Mendoza-Martinez1,2, Melanie Ridgway1

  • 1Wellcome Centre for Anti-Infectives Research, Faculty of Life Sciences, University of Dundee, Dundee, UK.

Nature Communications
|February 4, 2026
PubMed
Summary

This study decodes drug resistance by profiling all mutations at a binding site using multiplex oligo targeting and computational modeling. This approach reveals extensive constraints on mutational fitness and predicts drug resistance pathways.

More Related Videos

Nanomechanics of Drug-target Interactions and Antibacterial Resistance Detection
11:56

Nanomechanics of Drug-target Interactions and Antibacterial Resistance Detection

Published on: October 25, 2013

14.7K
Looking for Driver Pathways of Acquired Resistance to Targeted Therapy: Drug Resistant Subclone Generation and Sensitivity Restoring by Gene Knock-down
08:59

Looking for Driver Pathways of Acquired Resistance to Targeted Therapy: Drug Resistant Subclone Generation and Sensitivity Restoring by Gene Knock-down

Published on: December 11, 2017

7.6K

Related Experiment Videos

Last Updated: Feb 6, 2026

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
11:34

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Published on: August 9, 2019

7.1K
Nanomechanics of Drug-target Interactions and Antibacterial Resistance Detection
11:56

Nanomechanics of Drug-target Interactions and Antibacterial Resistance Detection

Published on: October 25, 2013

14.7K
Looking for Driver Pathways of Acquired Resistance to Targeted Therapy: Drug Resistant Subclone Generation and Sensitivity Restoring by Gene Knock-down
08:59

Looking for Driver Pathways of Acquired Resistance to Targeted Therapy: Drug Resistant Subclone Generation and Sensitivity Restoring by Gene Knock-down

Published on: December 11, 2017

7.6K

Area of Science:

  • Drug discovery and development
  • Computational biology
  • Parasitology

Background:

  • Understanding drug resistance mechanisms is crucial for developing effective therapeutics.
  • Assessing the impact of all possible mutations at a drug binding site is computationally and experimentally challenging.
  • The proteasome is a validated drug target for treating parasitic diseases like trypanosomiasis.

Purpose of the Study:

  • To decode the efficacy and resistance landscape of an anti-trypanosomal proteasome inhibitor.
  • To assess the impact of all possible mutations at the drug binding site.
  • To develop a predictive model for drug resistance.

Main Methods:

  • Multiplex oligo targeting for saturation mutagenesis of twenty codons in the Trypanosoma brucei proteasome.
  • Stepwise drug selection and codon variant scoring to generate dose-response profiles for mutants.
  • Computational modeling and in silico predictions of mutation impacts.
  • Fitness profiling to identify constraints on mutational fitness.

Main Results:

  • >100 resistance-conferring mutants were identified with detailed dose-response profiles.
  • Codon variant scores accurately predicted relative drug resistance.
  • Fitness profiling revealed extensive constraints on mutational fitness and resistance space.
  • In silico predictions closely aligned with experimentally observed drug resistance in cellulo.

Conclusions:

  • Multiplex oligo targeting is an effective method for assessing all possible mutations at a drug binding site.
  • The study provides a comprehensive understanding of the resistance landscape for an anti-trypanosomal proteasome inhibitor.
  • The findings facilitate the prediction of drug resistance pathways and inform the development of next-generation therapeutics.