PARNA is a parameterization workflow for nucleotides and their derivatives.
mamba create -n mdtools \
MDAnalysis openmm cudatoolkit==11.8.0 mdtraj ambertools \
parmed rdkit psi4 Jupyter matplotlib "numpy<2" scipy \
scikit-learn openmmtools pytorch-cuda pytorch biopython python=3.11 \
-c conda-forge -c pytorch -c nvidia -y
source activate mdtools
# install xtb
mamba install xtb -c conda-forge
PATH="xxx/xtb-IFF:$PATH"
ulimit -s unlimited
export OMP_STACKSIZE=3G
export OMP_NUM_THREADS=48,1
# install multiwfn
# then setup env var for multiwfn
ulimit -s unlimited
export OMP_STACKSIZE=200M
export DISPLAY=":0"
export Multiwfnpath=xxx/Multiwfn_3.8_dev_bin_Linux_noGUI
export PATH=$PATH:$Multiwfnpath
Refer to the example folder for detailed usage.
Project Link: https://github.com/yanze039/parna
For FEP/TI calculations, please also refer to the amberti wrapper.
https://github.com/yanze039/amberti
PARNA is built on the following packages.
(1) Mlýnský, V.; Kührová, P.; Pykal, M.; Krepl, M.; Stadlbauer, P.; Otyepka, M.; Banáš, P.; Šponer, J. Can We Ever Develop an Ideal RNA Force Field? Lessons Learned from Simulations of the UUCG RNA Tetraloop and Other Systems. J. Chem. Theory Comput. 2025, acs.jctc.4c01357. https://doi.org/10.1021/acs.jctc.4c01357.
(2) Anstine, D. M.; Zubatyuk, R.; Isayev, O. AIMNet2: A Neural Network Potential to Meet Your Neutral, Charged, Organic, and Elemental-Organic Needs. Chem. Sci. 2025, 16 (23), 10228–10244. https://doi.org/10.1039/D4SC08572H.
(3) Chen, J.; Liu, H.; Cui, X.; Li, Z.; Chen, H.-F. RNA-Specific Force Field Optimization with CMAP and Reweighting. J. Chem. Inf. Model. 2022, 62 (2), 372–385. https://doi.org/10.1021/acs.jcim.1c01148.
(4) Ivani, I.; Dans, P. D.; Noy, A.; Pérez, A.; Faustino, I.; Hospital, A.; Walther, J.; Andrio, P.; Goñi, R.; Balaceanu, A.; Portella, G.; Battistini, F.; Gelpí, J. L.; González, C.; Vendruscolo, M.; Laughton, C. A.; Harris, S. A.; Case, D. A.; Orozco, M. Parmbsc1: A Refined Force Field for DNA Simulations. Nat Methods 2016, 13 (1), 55–58. https://doi.org/10.1038/nmeth.3658.
The authors thank Yingze Wang (UCB), Dr. Xinyan Wang (DPTechnology) and Weiliang Luo (MIT) for the discussion about implementation details.