We review alchemical methods for computing solvation free energies and present an update (version 0.5) to the FreeSolv database of experimental and calculated hydration free energies of neutral compounds.
We demonstrate a new tool that enables---for the first time---massively parallel molecular simulation studies of biomolecular dynamics at the superfamily scale, illustrating its application to protein tyrosine kinases, an important class of drug targets in cancer.
We present a simple scheme for automatically selecting how much initial simulation data to discard to equilibration or burn-in based on maximizing the number of statistically uncorrelated samples in the dataset.
Keywords: molecular simulation; molecular dynamics; burn-in; equilibration; production; analysis
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.
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
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.
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.
John D. Chodera, Phillip Elms, Frank Noé, Bettina Keller, Christian M. Kaiser, Aaron Ewall-Wice, Susan Marqusee, Carlos Bustamante, and Nina Singhal Hinrichs.
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.