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Bayesian analysis of isothermal titration calorimetry for binding thermodynamics

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Listed:
  • Trung Hai Nguyen
  • Ariën S Rustenburg
  • Stefan G Krimmer
  • Hexi Zhang
  • John D Clark
  • Paul A Novick
  • Kim Branson
  • Vijay S Pande
  • John D Chodera
  • David D L Minh

Abstract

Isothermal titration calorimetry (ITC) is the only technique able to determine both the enthalpy and entropy of noncovalent association in a single experiment. The standard data analysis method based on nonlinear regression, however, provides unrealistically small uncertainty estimates due to its neglect of dominant sources of error. Here, we present a Bayesian framework for sampling from the posterior distribution of all thermodynamic parameters and other quantities of interest from one or more ITC experiments, allowing uncertainties and correlations to be quantitatively assessed. For a series of ITC measurements on metal:chelator and protein:ligand systems, the Bayesian approach yields uncertainties which represent the variability from experiment to experiment more accurately than the standard data analysis. In some datasets, the median enthalpy of binding is shifted by as much as 1.5 kcal/mol. A Python implementation suitable for analysis of data generated by MicroCal instruments (and adaptable to other calorimeters) is freely available online.

Suggested Citation

  • Trung Hai Nguyen & Ariën S Rustenburg & Stefan G Krimmer & Hexi Zhang & John D Clark & Paul A Novick & Kim Branson & Vijay S Pande & John D Chodera & David D L Minh, 2018. "Bayesian analysis of isothermal titration calorimetry for binding thermodynamics," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-26, September.
    • Handle: RePEc:plo:pone00:0203224
    DOI: 10.1371/journal.pone.0203224
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    References listed on IDEAS

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    1. Patil, Anand & Huard, David & Fonnesbeck, Christopher J., 2010. "PyMC: Bayesian Stochastic Modelling in Python," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i04).
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