The roots of the molecular simulation application can be traced back to physics where it was applied to simplified hard-sphere systems . This field of molecular simulation study has gained a lot of interest since then and applied to perform simulations to fold small protein at multi-microsecond scale [2-4], predict functional properties of receptors and to capture the intermediate transitions of the complex , and to study the movement and behavior of ligand in a binding pocket and also to predict interactions between receptors and ligands [6,7].
GROMACS is the most widely used software implemented to study the molecular dynamics (MD) simulations of complex proteins . GROMACS offers a set of commands which can be easily executed for MD simulation of a protein or to a complex protein with a ligand to study protein folding kinetics to computational drug design to the refinement of molecular structures. Recently,
Irrgang et al.,  have proposed an API for GROMACS called “gmxapi” written in pure Python and implemented as a C++ extension.
This API allows the users to simply construct the computational task graphs permitting the parallel optimizations and mixing of MD simulation and machine-learning operations using other software packages such as TensorFlow . The API provides a native interface to GROMACS MD engine . Users can simply drive MD simulations via high-level procedural commands, an object-oriented interface, or can employ their own extension code.
The restrained-ensemble simulations compute population properties from a set of MD simulation data, then compare these computed simulations to residue-residue distance distributions used as experimental data measured via double electron-electron resonance (DEER) spectroscopy. Then, a distance histogram is calculated by the simulation algorithm from the estimated ensemble and calculates a distance-dependent biasing force for the simulations, which are run for an interval of time (Δt) before repeating the process .
gmxapi enables custom plugins for user-defined forces, allows custom potential functions, provides the optimized performance of the software GROMACS, and allows to build and execute computational graphs.
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