Cargando…

Bayesian Regression Quantifies Uncertainty of Binding Parameters from Isothermal Titration Calorimetry More Accurately Than Error Propagation

We compare several different methods to quantify the uncertainty of binding parameters estimated from isothermal titration calorimetry data: the asymptotic standard error from maximum likelihood estimation, error propagation based on a first-order Taylor series expansion, and the Bayesian credible i...

Descripción completa

Detalles Bibliográficos
Autores principales: La, Van N. T., Minh, David D. L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10606514/
https://www.ncbi.nlm.nih.gov/pubmed/37894754
http://dx.doi.org/10.3390/ijms242015074
_version_ 1785127334497484800
author La, Van N. T.
Minh, David D. L.
author_facet La, Van N. T.
Minh, David D. L.
author_sort La, Van N. T.
collection PubMed
description We compare several different methods to quantify the uncertainty of binding parameters estimated from isothermal titration calorimetry data: the asymptotic standard error from maximum likelihood estimation, error propagation based on a first-order Taylor series expansion, and the Bayesian credible interval. When the methods are applied to simulated experiments and to measurements of Mg(II) binding to EDTA, the asymptotic standard error underestimates the uncertainty in the free energy and enthalpy of binding. Error propagation overestimates the uncertainty for both quantities, except in the simulations, where it underestimates the uncertainty of enthalpy for confidence intervals less than 70%. In both datasets, Bayesian credible intervals are much closer to observed confidence intervals.
format Online
Article
Text
id pubmed-10606514
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-106065142023-10-28 Bayesian Regression Quantifies Uncertainty of Binding Parameters from Isothermal Titration Calorimetry More Accurately Than Error Propagation La, Van N. T. Minh, David D. L. Int J Mol Sci Article We compare several different methods to quantify the uncertainty of binding parameters estimated from isothermal titration calorimetry data: the asymptotic standard error from maximum likelihood estimation, error propagation based on a first-order Taylor series expansion, and the Bayesian credible interval. When the methods are applied to simulated experiments and to measurements of Mg(II) binding to EDTA, the asymptotic standard error underestimates the uncertainty in the free energy and enthalpy of binding. Error propagation overestimates the uncertainty for both quantities, except in the simulations, where it underestimates the uncertainty of enthalpy for confidence intervals less than 70%. In both datasets, Bayesian credible intervals are much closer to observed confidence intervals. MDPI 2023-10-11 /pmc/articles/PMC10606514/ /pubmed/37894754 http://dx.doi.org/10.3390/ijms242015074 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
La, Van N. T.
Minh, David D. L.
Bayesian Regression Quantifies Uncertainty of Binding Parameters from Isothermal Titration Calorimetry More Accurately Than Error Propagation
title Bayesian Regression Quantifies Uncertainty of Binding Parameters from Isothermal Titration Calorimetry More Accurately Than Error Propagation
title_full Bayesian Regression Quantifies Uncertainty of Binding Parameters from Isothermal Titration Calorimetry More Accurately Than Error Propagation
title_fullStr Bayesian Regression Quantifies Uncertainty of Binding Parameters from Isothermal Titration Calorimetry More Accurately Than Error Propagation
title_full_unstemmed Bayesian Regression Quantifies Uncertainty of Binding Parameters from Isothermal Titration Calorimetry More Accurately Than Error Propagation
title_short Bayesian Regression Quantifies Uncertainty of Binding Parameters from Isothermal Titration Calorimetry More Accurately Than Error Propagation
title_sort bayesian regression quantifies uncertainty of binding parameters from isothermal titration calorimetry more accurately than error propagation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10606514/
https://www.ncbi.nlm.nih.gov/pubmed/37894754
http://dx.doi.org/10.3390/ijms242015074
work_keys_str_mv AT lavannt bayesianregressionquantifiesuncertaintyofbindingparametersfromisothermaltitrationcalorimetrymoreaccuratelythanerrorpropagation
AT minhdaviddl bayesianregressionquantifiesuncertaintyofbindingparametersfromisothermaltitrationcalorimetrymoreaccuratelythanerrorpropagation