Anncolvar: Approximation of complex collective variables by artificial neural networks for analysis and biasing of molecular simulations

Dalibor Trapl, Izabela Horvacanin, Vaclav Mareska, Furkan Ozcelik, Gozde Unal, Vojtech Spiwok*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

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).

Original languageEnglish
Article number25
JournalFrontiers in Molecular Biosciences
Volume6
Issue numberAPR
DOIs
Publication statusPublished - 2019

Bibliographical note

Publisher Copyright:
© 2019 Trapl, Horvacanin, Mareska, Ozcelik, Unal and Spiwok.

Keywords

  • Collective variables
  • Free energy simulations
  • Metadynamics
  • Molecular dynamics simulation
  • Neural networks

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