Approximation of Collective Variables by anncolvar
- University of Chemistry and Technology, Prague, Czech Republik
- Faculty of Science, University of Zagreb, Croatia
- Istanbul Technical University, Turkey
DOI10.24435/materialscloud:2019.0014/v1 (version v1, submitted on 23 April 2019)
How to cite this entry
Dalibor Trapl, Izabela Horvacanin, Vaclav Mareska, Furkan Ozcelik, Gozde Unal, Vojtech Spiwok, Approximation of Collective Variables by anncolvar, Materials Cloud Archive (2019), doi: 10.24435/materialscloud:2019.0014/v1.
Biomolecular simulations are computationally expensive. This limits their application in drug or protein design and related fields. Several methods have been developed to address this problem. These methods often use an artificial force or potential acting on selected degrees of freedom known as collective variables. This requires explicit calculation of a collective variable (and its derivatives) from molecular structure. For collective variables that cannot be calculated explicitly or such calculations is slow we developed anncolvar package (https://github.com/spiwokv/anncolvar). This package approximates collective variables using artificial neural networks. It was tested on Isomap low dimensional representation of cyclooctane derivative or solvent-accessible surface area of Trp-cage miniprotein.
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|14.1 MiB||Input files (input structure, topology, Plumed input) for simulations used to demonstrate functionality of anncolvar (https://github.com/spiwokv/anncolvar). Input files for metadynamics simulation of cyclooctane derivative in vacuum with three Isomap CVs, metadynamics (mtd), parallel tempering (ptmd) and parallel tempering metadynamics (ptmtd) of Trp-cage in water with solvent-accessible surface area and alpha-RMSD CVs are provided. Tested on OpenMPI4.0.0, Gromacs 2018.5 and Plumed2.5.0. Scripts provided.|
|4.2 KiB||README.txt file with descriptions.|
23 April 2019 [This version]