Avoiding accuracy-limiting pitfalls in the study of protein-ligand interactions with isothermal titration calorimetry

Sarah E. Boyce, Joel Tellinghuisen, and John D. Chodera.
Manuscript prior to submission. [bioRxiv] [PDF]
Supplementary files: ITC worksheet [PDF] [XLSX] [ODS]
doi:10.1101/023796

We show how to avoid common accuracy-limiting mistakes in isothermal titration calorimetry, and provide a simple spreadsheet to aid in propagating the dominant source of uncertainty (titrant concentration errors) into the resulting thermodynamic parameters.

Keywords: isothermal titration calorimetry; ITC; propagation of error; entropy-enthalpy compensation

Towards Automated Benchmarking of Atomistic Forcefields: Neat Liquid Densities and Static Dielectric Constants from the ThermoML Data Archive

Kyle A. Beauchamp, Julie M. Behr, Ariën S. Rustenburg, Christopher I. Bayly, Kenneth Kroenlein, and John D. Chodera.
J. Phys. Chem. B 119:12912, 2015. [DOI] [PDF] // code: [GitHub] // preprint: [arXiv

Progress in forcefield validation and parameterization has been hindered by the availability of high-quality machine-readable physical property data for small organic molecules. We show how the NIST ThermoML dataset provides a solution to this problem, and demonstrate its utility in benchmarking the GAFF/AM1-BCC small molecule forcefield on neat liquid densities and static dielectric constants to uncover problems in the representation of low-dielectric environments.

A robust approach to estimating rates from time-correlation functions

John D. ChoderaPhillip J. ElmsWilliam C. SwopeJan-Hendrik PrinzSusan MarquseeCarlos BustamanteFrank NoéVijay S. Pande
Preprint ahead of submission: [arXiv] [PDF] [SI]

The estimation of rates from experimental single-molecule data is fraught with peril. We describe some of the failures of existing methods and suggest a robust way to estimate rates from time-correlation functions.

Bayesian hidden Markov model analysis of single-molecule force spectroscopy: Characterizing kinetics under measurement uncertainty

John D. Chodera, Phillip Elms, Frank Noé, Bettina Keller, Christian M. Kaiser, Aaron Ewall-Wice, Susan Marqusee, Carlos Bustamante, and Nina Singhal Hinrichs.
preprint: [arXiv]

We describe the general theory and implementation for a Bayesian extension of hidden Markov models applicable to the characterization of how measurement uncertainty and finite statistics can impact the confidence in rate constants and conformational state properties.