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Updated: Sep 15, 2025
10:04
Collecting Variable-concentration Isothermal Titration Calorimetry Datasets in Order to Determine Binding Mechanisms
Published on: April 7, 2011
12.9K
Improving parameter inference by resolving Bayesian prior ambiguity via multi-dataset analysis: Application to
Lisa Otten1, Douglas R Walker2, Elisar J Barbar2
1Department of Biomedical Engineering, School of Medicine, Oregon Health and Science University, Portland, Oregon, United States of America.
Biorxiv : the Preprint Server for Biology
|July 17, 2025
Summary
This study introduces a Bayesian pipeline to precisely determine biomolecular binding parameters from isothermal titration calorimetry (ITC) data. By analyzing multiple datasets and refining concentration estimates, it overcomes inherent ambiguities for more accurate interaction studies.
Area of Science:
- Biochemistry
- Biophysics
- Computational Biology
Background:
- Isothermal titration calorimetry (ITC) is crucial for studying biomolecular interactions.
- Accurate determination of binding parameters is often limited by experimental noise and concentration variability.
- Mathematical ambiguities in standard models hinder precise calculation of binding enthalpies and associated uncertainties.
Purpose of the Study:
- To develop a robust Bayesian pipeline for enhanced precision in ITC data analysis.
- To resolve mathematical ambiguities in binding parameter determination, particularly for enthalpy.
- To improve the reliability of biomolecular interaction studies by refining concentration estimates.
Main Methods:
- Simultaneous analysis of multiple ITC datasets.
- Hierarchical Bayesian treatment of analyte concentration priors.
- Application of modern Monte Carlo techniques for robust posterior sampling.
Main Results:
- The pipeline successfully resolves ambiguities in single-dataset ITC studies.
- It enables joint inference of binding parameters and concentrations with improved precision.
- Validated with synthetic and experimental data, including Mg(II)-EDTA and LC8 protein interactions.
Conclusions:
- This Bayesian approach provides a foundation for increasing the precision of binding constants derived from ITC.
- It offers a systematic framework for assessing the reliability of experimental concentration estimates.
- Enables more accurate and reliable biomolecular interaction studies.
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