Fig 1.
Panel (A) shows an example of the commandline_operation function by which gmxapi can reproduce any GROMACS functionality. Panel (B) shows gmxapi molecular dynamics calls operating on ensemble input, providing straightforward high-level parallelism in addition to parallelization within each command. Panel (C) demonstrates both a while loop and pluggability of gmxapi components. Together, the examples will execute an ensemble of small protein folding simulations until at least one ensemble member samples the native state.
Fig 2.
Schematic of data flow and control flow for a segment of a complex simulation workflow.
Ensembles of simulations can be run (denoted by stacked rectangles) by gmxapi merely by passing an array of inputs instead of a single input. Custom plugins can interact with running MD simulations. Finally, conditional and looping logic can create high-level simulation algorithms.
Fig 3.
Schematics for gmxapi operations.
As shown in panel (A), gmxapi operations have well defined inputs and outputs. These can operate on arrays of inputs and also include support for legacy GROMACS operations by wrapping the command-line toolset, as shown in panel (B). As shown in panel (C), the decorator @function_wrapper allows arbitrary user code to be transformed into a gmxapi operation.
Fig 4.
Detailed diagram of inputs, outputs, and operations in a chain of gmxapi operations.
Operations are shown in green boxes, the corresponding nodes in the work graph in red boxes, inputs in orange boxes, and outputs in blue boxes. The diagram depicts a chain of GROMACS tools preparing simulation input, feeding into "gmxapi.mdrun" to run a batch of simulations.
Fig 5.
Residue-residue distance distributions in a simulated ensemble of HIV gp41.
DEER spectroscopy was used to measure distance distributions between two monomers of the gp41 trimer. Thus, each residue designates a monomer-monomer residue pair. Panels A-E show plots for the 5 restrained residues in the HIV trimer: 106, 173, 202, 306, and 542 respectively. Discretized DEER distance distributions are plotted in green, and simulation results are plotted in blue bars. The simulation ensemble shows good convergence to the measured values.