We show how bound ligand poses can be identified even when the location of the binding sites are unknown using the machinery of alchemical modern free energy calculations on graphics processors.
A new inexpensive polarizable model of liquid water for next-generation forcefields is derived using an automated parameterization engine.
The finite-timestep errors in molecular dynamics simulations can be interpreted as a form of nonequilibrium work. We show how this leads to straightforward schemes for correcting for these errors or assessing their impact.
Keywords: velocity verlet with Velocity randomization; VVVR; nonequilibrium free energy; integrator error; nonequilibrium integration
Peter Eastman, Mark S. Friedrichs, John D. Chodera, Randy J. Radmer, Chris M. Bruns, Joy P. Ku, Kyle A. Beauchamp, T. J. Lane, Lee-Ping Wang, Diwakar Shukla, Tony Tye, Mike Houston, Timo Stich, Christoph Klein, Michael R. Shirts, and Vijay S. Pande.
J. Chem. Theor. Comput. 9:461, 2013. [DOI] [PDF]
We describe the latest version of an open-source, GPU-accelerated library and toolkit for molecular simulation.
Popular constant-force-feedback single-molecule experiments can cause severe artifacts in single-molecule force spectroscopy data. We demonstrate a simple alternative that eliminates these artifacts.
We show how the concept of maximum entropy can be used to recover unbiased conformational distributions from experimental data, and how this concept relates to the popular `ensemble refinement' schemes for NMR data analysis.
We present a significant generalization of Monte Carlo methods that provide an enormously useful tool for enhancing the efficiency of molecular simulations and enabling molecular design.
Keywords: NCMC; Monte Carlo; Metropolis-Hastings; acceptance rates; molecular dynamics
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.
We describe how reweighing techniques can provide optimal estimates of temperature-dependent dynamical properties from simulations conducted at multiple temperatures.
A review of current best practices for the generation and validation of Markov state models for describing the stochastic dynamics of biomolecular systems.