Abstract

The state of a molecular system can be described in terms of collective variables. These low-dimensional descriptors of molecular structure can be used to monitor the state of the simulation, to calculate free energy profiles or to accelerate rare events by a bias potential or a bias force. Frequent calculation of some complex collective variables may slow down the simulation or analysis of trajectories. Moreover, many collective variables cannot be explicitly calculated for newly sampled structures. In order to address this problem, we developed a new package called anncolvar. This package makes it possible to build and train an artificial neural network model that approximates a collective variable. It can be used to generate an input for the open-source enhanced sampling simulation PLUMED package, so the collective variable can be monitored and biased by methods available in this program. The computational efficiency and the accuracy of anncolvar are demonstrated on selected molecular systems (cyclooctane derivative, Trp-cage miniprotein) and selected collective variables (Isomap, molecular surface area).

Highlights

  • Molecular dynamics simulation makes it possible to simulate any molecular process at the atomic level

  • Structural and thermodynamical properties of a protein can be predicted by simulation of its folding and unfolding

  • In this work we describe a new tool anncolvar for approximation of an arbitrary collective variables (CVs)

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Summary

Introduction

Molecular dynamics simulation makes it possible to simulate any molecular process at the atomic level. The program anncolvar is written in a way so that it requires a preprepared reference structure and a training trajectory. The program requires a set of precalculated values of collective variables for each snapshot of the training trajectory (option -c).

Results
Conclusion

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