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The Equilibrium Binding Constant and Binding Strength

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The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

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Consensus Modeling Strategies for Predicting Transthyretin Binding Affinity from Tox24 Challenge Data.

Thalita Cirino¹, Luis Pinto², Mateusz Iwan³

¹Molecular Biotechnology and Health Sciences Department, University of Turin, Turin 10126, Italy.

Chemical Research in Toxicology

|May 15, 2025

Summary

This summary is machine-generated.

Consensus modeling improves predictions of chemical binding to transthyretin (TTR), a key thyroid hormone transporter. This approach enhances accuracy and identifies potential experimental issues, aiding in endocrine disruption assessment.

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Area of Science:

Computational toxicology
Endocrine disruption
Pharmacology

Background:

Transthyretin (TTR) transports thyroid hormones, and chemicals binding to it can disrupt the endocrine system.
Assessing TTR binding affinity is crucial for identifying potential endocrine disruptors.

Purpose of the Study:

To evaluate computational modeling strategies for predicting TTR binding affinity.
To assess the performance and uncertainty of individual and consensus models.

Main Methods:

Analysis of 1512 compounds using regression metrics and applicability domains (AD).
Development of consensus models by averaging predictions from nine top-performing individual models.
Comparison of consensus models with and without AD constraints.

Main Results:

Consensus models outperformed individual models, with a lower root-mean-square error (RMSE) of 19.8% on the test set.
Applying AD constraints improved individual model accuracy but had limited impact on consensus models.
Identified outliers suggest potential experimental artifacts or activity cliffs.

Conclusions:

Consensus modeling enhances predictive performance and addresses limitations of individual computational models.
Harmonizing divergent model perspectives through averaging improves reliability.
Further research should expand chemical space coverage and refine experimental data.