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Lessons learned about steered molecular dynamics simulations and free energy calculations
Author(s) -
Boubeta Fernando Martín,
Contestín García Rocío María,
Lorenzo Ezequiel Norberto,
Boechi Leonardo,
Estrin Dario,
Sued Mariela,
Arrar Mehrnoosh
Publication year - 2019
Publication title -
chemical biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 77
eISSN - 1747-0285
pISSN - 1747-0277
DOI - 10.1111/cbdd.13485
Subject(s) - statistical physics , molecular dynamics , gaussian , estimator , work (physics) , cumulant , parametric statistics , energy (signal processing) , physics , computer science , biological system , chemistry , computational chemistry , mathematics , thermodynamics , quantum mechanics , statistics , biology
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.