Bayesian analysis of isothermal titration calorimetry for binding thermodynamics

PLoS One. 2018 Sep 13;13(9):e0203224. doi: 10.1371/journal.pone.0203224. eCollection 2018.


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bacillus
  • Bacterial Proteins / metabolism
  • Bayes Theorem
  • Biophysical Phenomena
  • Calorimetry / methods*
  • Chelating Agents / pharmacology
  • Computer Simulation
  • Edetic Acid / pharmacology
  • Ligands
  • Magnesium / chemistry
  • Markov Chains
  • Monte Carlo Method
  • Protein Binding
  • Signal Processing, Computer-Assisted
  • Software
  • Thermodynamics
  • Thermolysin / metabolism
  • Uncertainty


  • Bacterial Proteins
  • Chelating Agents
  • Ligands
  • Edetic Acid
  • Thermolysin
  • Magnesium