Robosample: A software using constrained molecular dynamics coupled with Gibbs sampling to investigate macromolecules.

Abstract

An ongoing challenge for the simulation of macromolecules is their complex potential energy surface which is a result of high dimensionality and chemical complexity. The surface contains high energy barriers usually difficult to overcome by existing simulation methods. We present here Robosample, a software combining the advantages of robot mechanics for fast constrained simulations and of Gibbs sampling for achieving ergodicity. While energy calculation is the main computational bottleneck for macromolecule simulations, a big part of the work can be short-circuited by increasing the trajectory integration timestep. Usually this is achieved by modifying the system's mass matrix. The way this is implemented in Robosample is by using constraints in the framework of reduced coordinates. A good example is torsional dynamics, which transforms the system in Bond-Angle-Torsion coordinates and then ignores the motion of the bonds and the angles. Robosample sampling method is Hamiltonian Monte Carlo coupled with Gibbs sampling (GCHMC), with random variables divided among molecular degrees of freedom. With GCHMC, certain molecule segments are considered rigid and sub-sampled, while focusing on soft degrees of freedom as Gibbs blocks. Ergodicity is achieved by interleaving fully flexible Cartesian dynamics Monte Carlo proposals. The validity of the method was first proven on small systems correctly reproducing alanine dipeptide free energy landscape and showed to increase sampling efficiency of macrocycle containing small molecules which are notoriously difficult to simulate. The software was subsequently extended to include spherical and cylindrical robotic joints and proved to be efficient on medium sized biological molecules, such as glycans. Now we have reached a stage where we can successfully sample big macromolecules such as antibodies on graphical processing units.