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Bayesian regression and model selection for isothermal titration calorimetry with enantiomeric mixtures

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  • Trung Hai Nguyen
  • Van N T La
  • Kyle Burke
  • David D L Minh

Abstract

Bayesian regression is performed to infer parameters of thermodynamic binding models from isothermal titration calorimetry measurements in which the titrant is an enantiomeric mixture. For some measurements the posterior density is multimodal, indicating that additional data with a different protocol are required to uniquely determine the parameters. Models of increasing complexity—two-component binding, racemic mixture, and enantiomeric mixture—are compared using model selection criteria. To precisely estimate one of these criteria, the Bayes factor, a variation of bridge sampling is developed.

Suggested Citation

  • Trung Hai Nguyen & Van N T La & Kyle Burke & David D L Minh, 2022. "Bayesian regression and model selection for isothermal titration calorimetry with enantiomeric mixtures," PLOS ONE, Public Library of Science, vol. 17(9), pages 1-22, September.
    • Handle: RePEc:plo:pone00:0273656
    DOI: 10.1371/journal.pone.0273656
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    References listed on IDEAS

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    1. Overstall, Antony M. & Forster, Jonathan J., 2010. "Default Bayesian model determination methods for generalised linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3269-3288, December.
    2. Justin M. Di Trani & Stephane De Cesco & Rebecca O’Leary & Jessica Plescia & Claudia Jorge Nascimento & Nicolas Moitessier & Anthony K. Mittermaier, 2018. "Rapid measurement of inhibitor binding kinetics by isothermal titration calorimetry," Nature Communications, Nature, vol. 9(1), pages 1-7, December.
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