FLYING GAUSSIAN METHOD: NEW APPLICATIONS

Zoran Šućur, Nikola Ďásková, Vojtěch Spiwok

University of Chemistry and Technology Prague,

Technicka 5, Prague 6, Czech Republic

spiwokv@vscht.cz

 

The time necessary to overcome significant energy barriers in complex free energy surfaces (FES) can be considered as a major drawback in molecular dynamics simulations. This issue can be tackled by developing enhanced sampling techniques. Metadynamics is one of those methods and it has been successfully used for more than 15 years now [1]. In metadynamics, the history-dependent bias potential acts on preselected collective variables (CV) and by doing so, it discourages the system from visiting previously sampled states. The bias potential is defined as a sum of Gaussian “hills” (with preselected height and width) which are added to the system and accumulate during the simulation, forcing the system to visit new areas of FES. Selection of CVs is of crucial importance primarily in distinguishing important states of the studied system.

           

Flying Gaussian method [2] was inspired by multiple walkers metadynamics. The system is simulated in multiple walkers (replicas), but the bias potential does not accumulate during the simulation. Instead, with the new value of CV being calculated for each walker in every microscopic step of the simulation, the position of the “hill” for each walker is only updated. This means that during the whole simulation, the number of “hills” is the same as the number of the walkers. The filling of the free-energy minima is achieved by walkers concentrating in them.

 

This method was successfully applied to exploration of CV space of selected biomolecules, showed good performance in the terms of time saving and efficiency in exploring FES. FES were reconstructed using on-the-fly reweighting, as the bias potential is too dynamic to directly estimate it. Furthermore, the method was tested for cases where choice of CVs is particularly difficult, by aligning the starting replicas to a reference structure and applying the bias based on the calculations of components of position of preselected atoms of the replicas. We also present preliminary results of using flaying Gaussian method in docking simulations.

 

1.       Laio A, Parrinello M. Escaping free-energy minima. Proceedings of the National Academy of Sciences of the United States of America. 2002, 99 (20):12562-12566.

 

2.       Sucur Z, Spiwok V. Sampling Enhancement and Free Energy Prediction by the Flying Gaussian Method. J. Chem. Theory Comput., 2016, 12 (9):4644–4650