Background: Compared with all-atom molecular dynamics (MD), constrained MD methods allow for larger time steps, potentially reducing computational cost. For this reason, there has been continued interest in improving constrained MD algorithms to increase configuration space sampling in molecular simulations.
Methods: Here, we introduce Robosample, a software package that implements high-performance constrained dynamics algorithms, originally developed for robotics, and applies them to simulations of biomolecular systems. As in the gMolmodel package developed by Spiridon and Minh in 2017, Robosample uses Constrained Dynamics Hamiltonian Monte Carlo (CDHMC) as a Gibbs sampling move - a type of Monte Carlo move where a subset of coordinates is allowed to change. In addition to the previously described Cartesian and torsional dynamics moves, Robosample implements spherical and cylindrical joints that can be distributed along the molecule by the user.
Results: In alanine dipeptide simulations, the free energy surface is recovered by mixing fully flexible with torsional, cylindrical, or spherical dynamics moves. Ramachandran dynamics, where only the two key torsions are mobile, accelerate the slowest transition by an order of magnitude. We also show that simulations of a complex glycan cover significantly larger regions of the configuration space when mixed with constrained dynamics.
Major conclusions: Robosample is a tool of choice for efficient conformational sampling of large biomolecules.
General significance: Robosample is intended as a reliable and user-friendly simulation package for fast biomolecular sampling that does not require extensive expertise in mechanical engineering or in the statistical mechanics of reduced coordinates.
Keywords: Gibbs sampling; Hamiltonian Monte Carlo; Molecular dynamics; Multibody dynamics; Robotics.
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