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Related Concept Videos

Confirmation Biases01:31

Confirmation Biases

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The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
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Signal Sequences and Sorting Receptors01:41

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Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
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Hindsight Biases01:12

Hindsight Biases

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Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
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Insulin: The Receptor and Signaling Pathways01:28

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Insulin action is mediated through a receptor tyrosine kinase, akin to the IGF-1 receptor. The number of receptors per cell varies significantly, from 40 on erythrocytes to 300,000 on adipocytes and hepatocytes. The insulin receptor consists of linked α/β subunit dimers, forming a heterotetramer glycoprotein with two extracellular α subunits and two β subunits spanning the membrane. The α subunits inhibit the inherent tyrosine kinase activity of the β subunits, but...
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Bias01:22

Bias

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Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
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Constitutional Isomers of Alkanes02:18

Constitutional Isomers of Alkanes

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Organic compounds of the same molecular formula can have different structural formulas called constitutional isomers, and the phenomenon is known as constitutional isomerism. Alkanes with four or more carbons showing multiple structures with the same molecular formula thereby exhibit constitutional isomerism.
The linear isomer of an alkane is prefixed by the term “n”; hence a linear isomer of pentane is known as n-pentane. Based on the type of branching, some of the...
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Related Experiment Video

Updated: Jan 29, 2026

Quantifying Agonist Activity at G Protein-coupled Receptors
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Can Adding Constitutive Receptor Activity Redefine Biased Signaling Quantification?

Bin Zhou1, David A Hall2, Jesús Giraldo1

  • 1Laboratory of Molecular Neuropharmacology and Bioinformatics, Unitat de Bioestadística and Institut de Neurociències, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain; Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Spain; Unitat de Neurociència Traslacional, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT), Institut de Neurociències, Universitat Autònoma de Barcelona, Spain.

Trends in Pharmacological Sciences
|February 6, 2019
PubMed
Summary

Biased signaling research needs models that include constitutive receptor activity. This approach enables robust characterization of all ligand types, including agonists, neutral antagonists, and inverse agonists.

Keywords:
GPCRbiased signalingconstitutive receptor activityinverse agonists

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Area of Science:

  • Pharmacology
  • Molecular Biology
  • Biochemistry

Background:

  • Biased signaling, the selective activation of specific signaling pathways by a single receptor, is a key area in modern pharmacology.
  • Understanding biased signaling is crucial for developing targeted therapeutics with improved efficacy and reduced side effects.
  • Current methods for characterizing biased ligands often lack the robustness to encompass all ligand classes.

Purpose of the Study:

  • To develop and validate robust models for the reliable characterization of biased ligands.
  • To establish a framework that integrates constitutive receptor activity into the assessment of ligand bias.
  • To ensure models are applicable across the full spectrum of ligand types: agonists, neutral antagonists, and inverse agonists.

Main Methods:

  • Development of mathematical models incorporating constitutive receptor activity.
  • In silico simulations to test model performance across various signaling bias scenarios.
  • Comparative analysis of model predictions against existing biased signaling data.

Main Results:

  • Models incorporating constitutive receptor activity provide a more comprehensive assessment of ligand bias.
  • The proposed framework accurately differentiates between agonists, neutral antagonists, and inverse agonists.
  • Demonstrated the necessity of including basal receptor activity for reliable ligand characterization.

Conclusions:

  • Including constitutive receptor activity in models is essential for accurate and comprehensive characterization of biased ligands.
  • This approach enhances the reliability of pharmacological assessments across all ligand classes.
  • The developed models offer a robust tool for advancing the study of biased signaling and drug discovery.