Modeling error in experimental assays using the bootstrap principle: Understanding discrepancies between assays using different dispensing technologies

Sonya M. Hanson, Sean Ekins, and John D. Chodera.
Journal of Computer Aided Molecular Design 29:1073, 2015. [DOI] [PDF] // IPython notebook [GitHub] // preprint: [bioRxiv]
Inspired by this In the Pipeline blog post

The drug development community faced a puzzling challenge when a disturbing paper published in PLoS One demonstrated results from the same assay performed with different dispensing technologies both varied wildly and significantly different in magnitude of reported potencies. Inspired by a talk given at the 2014 CADD GRC by Cosma Shalizi on bootstrapping to model error, we show how this simple idea can help explain a large amount of the discrepancy in this assay, and provide simple mathematical tools and an IPython notebook illustrating how easy it is to model the error and bias in experimental assays even when other information about assay reliability is unavailable.

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]

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.