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

Glucagon-like Receptor Agonists01:24

Glucagon-like Receptor Agonists

318
Incretins include glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), which stimulate insulin secretion post-meals. In type 2 diabetes, GIP's efficacy is reduced, making GLP-1 a viable drug target. GIP originates from preproGIP.
GLP-1, when administered in high doses intravenously, triggers insulin secretion, inhibits glucagon release, slows gastric emptying, reduces food intake, and restores normal insulin secretion. However, its rapid inactivation by...
318
Drug-Receptor Interaction: Agonist01:25

Drug-Receptor Interaction: Agonist

2.5K
Agonists are drugs that interact with specific receptors in the body to produce a biological response. When an agonist binds to a receptor, it activates or enhances the receptor's function, leading to physiological effects. The interaction between agonist drugs and receptors is crucial for their therapeutic action in various medical treatments.
Agonists can bind to receptors in different ways. Some agonists bind directly to the receptor's active site, mimicking the endogenous...
2.5K
The Two-State Receptor Model01:29

The Two-State Receptor Model

1.9K
The two-state receptor model explains a drug's interaction with receptors, such as G protein-coupled receptors and ligand-gated ion channels, to induce or inhibit a biological response. When no natural ligands are present, a receptor exists in an equilibrium of inactive (Ri) and active (Ra) conformations. The inactive form does not produce a response, while the active form generates a basal effect known as constitutive activity.
The binding affinity of a drug determines its interaction with...
1.9K
Adrenergic Agonists: Chemistry and Structure-Activity Relationship01:16

Adrenergic Agonists: Chemistry and Structure-Activity Relationship

3.0K
Adrenergic agonists' structure-activity relationship (SAR) determines their selectivity and efficacy. These agonists comprise a phenylethylamine moiety with an aromatic ring and an ethylamine side chain.
Aromatic ring substitutions: Substituting the aromatic ring with –OH groups at positions 3 and 4 yields catecholamines (e.g., epinephrine), which have a high affinity for adrenoceptors. Hydrogen bonding between –OH groups and receptors enhances adrenergic activity.
Separation of...
3.0K
GPCRs Regulate Adenylyl Cylase Activity01:09

GPCRs Regulate Adenylyl Cylase Activity

5.5K
Some GPCRs transmit signals through adenylyl cyclase (AC), a transmembrane enzyme. AC helps synthesize second messenger cyclic adenosine monophosphate (cAMP). AC catalyzes cyclization reaction and converts ATP to cAMP by releasing a pyrophosphate. The pyrophosphate is further hydrolyzed to phosphate by the enzyme pyrophosphatase, which drives cAMP synthesis to completion. However, cAMP is rapidly degraded to 5′ AMP by the enzymes phosphodiesterase (PDE), preventing overstimulation of...
5.5K
Direct-Acting Cholinergic Agonists: Chemistry and Structure-Activity Relationship01:22

Direct-Acting Cholinergic Agonists: Chemistry and Structure-Activity Relationship

947
Cholinergic agonists or cholinomimetics mimic the action of acetylcholine to stimulate the parasympathetic nervous system. They are categorized into direct-acting and indirect-acting agents. The direct-acting cholinergic drugs induce the parasympathetic response by directly binding to the muscarinic or nicotine receptors. In comparison, the indirect-acting cholinergic drugs prevent acetylcholine hydrolysis, indirectly contributing to the extended parasympathetic response.
The direct-acting...
947

You might also read

Related Articles

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

Sort by
Same author

Identification of glicentin as a low-potency glucose-dependent insulinotropic polypeptide receptor agonist.

Peptides·2026
Same author

Multicomponent Stapling of Glucagon-Like Peptide-1 Enables Receptor-Guided PROTAC Delivery.

Angewandte Chemie (International ed. in English)·2026
Same author

Cyclized Peptide Inhibitors of the Small G Protein Cdc42 Mimic Binding of Effector Proteins.

Biochemistry·2026
Same author

Preclinical evaluation and first-in-human phase 1 trial of AZD0186, a novel, oral small molecule glucagon-like peptide-1 receptor agonist.

The Journal of pharmacology and experimental therapeutics·2025
Same author

How to think about designing smart antibodies in the age of genAI: integrating biology, technology, and experience.

mAbs·2025
Same author

Roles for Prlhr/GPR10 and Npffr2/GPR74 in feeding responses to PrRP.

Molecular metabolism·2025

Related Experiment Video

Updated: Jun 26, 2025

In Vitro Imaging and Quantification of the Drug Targeting Efficiency of Fluorescently Labeled GnRH Analogues
10:36

In Vitro Imaging and Quantification of the Drug Targeting Efficiency of Fluorescently Labeled GnRH Analogues

Published on: March 21, 2017

7.6K

Machine learning designs new GCGR/GLP-1R dual agonists with enhanced biological potency.

Anna M Puszkarska1,2, Bruck Taddese3,4, Jefferson Revell3

  • 1Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK.

Nature Chemistry
|May 16, 2024
PubMed
Summary
This summary is machine-generated.

Researchers developed AI models to design potent dual peptide agonists targeting the glucagon receptor (GCGR) and glucagon-like peptide-1 receptor (GLP-1R) for diabetes and obesity. Model-designed peptides showed superior biological activity, improving potency at both receptors simultaneously.

More Related Videos

Automated Acoustic Dispensing for the Serial Dilution of Peptide Agonists in Potency Determination Assays
08:06

Automated Acoustic Dispensing for the Serial Dilution of Peptide Agonists in Potency Determination Assays

Published on: November 10, 2016

7.6K
Methods for the Discovery of Novel Compounds Modulating a Gamma-Aminobutyric Acid Receptor Type A Neurotransmission
07:16

Methods for the Discovery of Novel Compounds Modulating a Gamma-Aminobutyric Acid Receptor Type A Neurotransmission

Published on: August 16, 2018

13.6K

Related Experiment Videos

Last Updated: Jun 26, 2025

In Vitro Imaging and Quantification of the Drug Targeting Efficiency of Fluorescently Labeled GnRH Analogues
10:36

In Vitro Imaging and Quantification of the Drug Targeting Efficiency of Fluorescently Labeled GnRH Analogues

Published on: March 21, 2017

7.6K
Automated Acoustic Dispensing for the Serial Dilution of Peptide Agonists in Potency Determination Assays
08:06

Automated Acoustic Dispensing for the Serial Dilution of Peptide Agonists in Potency Determination Assays

Published on: November 10, 2016

7.6K
Methods for the Discovery of Novel Compounds Modulating a Gamma-Aminobutyric Acid Receptor Type A Neurotransmission
07:16

Methods for the Discovery of Novel Compounds Modulating a Gamma-Aminobutyric Acid Receptor Type A Neurotransmission

Published on: August 16, 2018

13.6K

Area of Science:

  • Biochemistry
  • Computational Biology
  • Endocrinology

Background:

  • Peptide dual agonists targeting the human glucagon receptor (GCGR) and glucagon-like peptide-1 receptor (GLP-1R) are promising for treating type 2 diabetes and obesity.
  • Developing effective dual agonists requires high potency at both receptors, a challenge due to limited experimental data for predicting new peptide variant activity.

Purpose of the Study:

  • To assess if existing peptide sequence data can train models for accurate prediction of dual receptor activity.
  • To design novel peptide variants with optimized dual agonism using computational modeling.

Main Methods:

  • Trained various predictive models, including a deep multi-task neural network with multiple loss optimization, using in vitro potency data for human GCGR and GLP-1R.
  • Employed model-guided sequence optimization to design peptide variants with predicted dual activity ranges.

Main Results:

  • Three model-designed peptide sequences demonstrated potent dual agonism with enhanced biological activity.
  • Achieved up to a sevenfold simultaneous potency improvement at both GCGR and GLP-1R compared to the best training set dual agonist.

Conclusions:

  • Computational models, particularly deep neural networks, can effectively predict dual receptor activity for peptide design.
  • Model-guided optimization is a viable strategy for developing superior dual agonists for metabolic disease treatment.