Cargando…

Bayesian analysis of isothermal titration calorimetry for binding thermodynamics

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 ne...

Descripción completa

Detalles Bibliográficos
Autores principales: Nguyen, Trung Hai, Rustenburg, Ariën S., Krimmer, Stefan G., Zhang, Hexi, Clark, John D., Novick, Paul A., Branson, Kim, Pande, Vijay S., Chodera, John D., Minh, David D. L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6136728/
https://www.ncbi.nlm.nih.gov/pubmed/30212471
http://dx.doi.org/10.1371/journal.pone.0203224
Descripción
Sumario: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.