1. Lessons learned about steered molecular dynamics simulations and free energy calculations
- Author
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Fernando Martín Boubeta, Mariela Sued, Mehrnoosh Arrar, Ezequiel Norberto Lorenzo, Darío A. Estrin, Rocío María Contestín García, and Leonardo Boechi
- Subjects
Pharmacology ,Physics ,Work (thermodynamics) ,Likelihood Functions ,010405 organic chemistry ,Gaussian ,Organic Chemistry ,Estimator ,Molecular Dynamics Simulation ,01 natural sciences ,Biochemistry ,0104 chemical sciences ,010404 medicinal & biomolecular chemistry ,symbols.namesake ,Molecular dynamics ,Jarzynski equality ,Drug Discovery ,symbols ,Molecular Medicine ,Thermodynamics ,Statistical physics ,Dispersion (water waves) ,Energy (signal processing) ,Parametric statistics - Abstract
The calculation of free energy profiles is central in understanding differential enzymatic activity, for instance, involving chemical reactions that require QM-MM tools, ligand migration, and conformational rearrangements that can be modeled using classical potentials. The use of steered molecular dynamics (sMD) together with the Jarzynski equality is a popular approach in calculating free energy profiles. Here, we first briefly review the application of the Jarzynski equality to sMD simulations, then revisit the so-called stiff-spring approximation and the consequent expectation of Gaussian work distributions and, finally, reiterate the practical utility of the second-order cumulant expansion, as it coincides with the parametric maximum-likelihood estimator in this scenario. We illustrate this procedure using simulations of CO, both in aqueous solution and in a carbon nanotube as a model system for biologically relevant nanoheterogeneous environments. We conclude the use of the second-order cumulant expansion permits the use of faster pulling velocities in sMD simulations, without introducing bias due to large dispersion in the non-equilibrium work distribution.
- Published
- 2018