COSMO: COarse-grained Simulation of intrinsically disordered prOteins with openMM

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A coarse-grained simulation engine empowered by openMM

Currently, there are four models are supported:

1. hps_urry: Hydropathy according to Urry scale (default, Recommended).
2. hps_kr: Kapcha-Rossy scale. This model has parameters for nucleic acids and post-translational modification residues.
3. hps_ss: hps_urry with bonded potential.
4. mpipi: another model that using Wang-Frenkel short range potential instead of LJ 12-6
5. Other models can be easily implemented by defining them in cosmo/parameters/model_parameters.py

Models summary:

Model	components support	Implemented	Tested
hps_kr	protein, RNA, phosphorylation protein	protein, p-protein, RNA	protein
hps_urry	protein, DNA	protein	protein
hps_ss	protein	protein	protein
mpipi	protein, RNA	protein	protein

The package is ready for studying various problems such as, conformation dynamics of single chain, LLPS ...

Checkout documentation for more details: here. A simple example can be found here.

More information can be found in my personal blog: https://qvv5013.github.io/

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Requirements:

• OpenMM >=7.7^a,b (select cuda version that compatible with your nvidia driver)
• Parmed

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^a: function getStepCount() does not work as expected (or is not implemented in versions earlier than 7.7). This function is necessary when restarting simulations.

^b: I recommend to upgrade to openMM 8.0 for better performance.

How to use COSMO:

Linux:

The main requirements is openMM >= 7.7. Other packages are requires as well (see requirements.txt)

• Create conda environment: conda create -n py310 python=3.10
• activate py310 env : conda activate py310
• Install openMM 7.7 (or later): conda install -c conda-forge openmm=7.7 cudatoolkit=10.2
  • conda will try to install the latest version of cudatoolkit and sometime it will not work.
    You should select version that is compatible with your nvidia-driver (if you have NVIDIA GPU)
• Download folder and place in target location, for example:
  PATH_TO_CODE/cosmo/
• Add cosmo module in Python path so that Python know what cosmo is (in .bashrc file): export PYTHONPATH=$PYTHONPATH:PATH_TO_CODE/cosmo/

REMEMBER TO CHANGE PATH_TO_CODE TO YOUR SPECIFIC.

Example:

• The standard example can be found at example/standard_example. You will need a control parameter file (e.g md.ini). Check here for more information.

Example of md.ini:

[OPTIONS]
md_steps = 10_000 # number of steps
dt = 0.01 ; time step in ps
nstxout = 100 ; number of steps to write checkpoint = nstxout
nstlog = 100 ; number of steps to print log
nstcomm = 100 ; frequency for center of mass motion removal
; select model, available options: hps_kr, hps_urry, hps_ss or mpipi
model = mpipi

; control temperature coupling
tcoupl = yes
ref_t = 310 ; Kelvin- reference temperature
tau_t = 0.01 ; ps^-1

;pressure coupling
pcoupl = no
ref_p = 1
frequency_p = 25

; Periodic boundary condition: if pcoupl is yes then pbc must be yes.
pbc = yes
; if pbc=yes, then use box_dimension option to specify box_dimension = x or [x, y, z], unit of nanometer
box_dimension = 30 ; [30, 30, 60]

; input
protein_code = ASYN
pdb_file = asyn.pdb
; output
checkpoint = asyn.chk
;Use GPU/CPU
device = GPU
; If CPU is specified, then use ppn variable
ppn = 4
;Restart simulation
restart = no
minimize = yes ;if not restart, then minimize will be loaded, otherwise, minimize=False

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• Run simulation:
  • I give you two options to perform simulations:
    • First option:
      • goto example folder, e.g examples/standard_example/:
      • edit simulation config file: md.ini
      • execute command: python run_simulation.py -f md.ini
    • Second option:
      • add python environment created above in the beginning of cosmo-simulation.py script: /home/qvv5013/anaconda3/envs/py310/bin/python - point to the environment you created above
      • make the cosmo-simulation.py script to be executable: chmod +x /PATH_TO_COSMO/cosmo-simulation.py
      • Create an alias to cosmo-simulation.py. e.g: I modify my .bashrc: alias cosmo-simulation='/home/qvv5013/work3/code/cosmo/cosmo/cosmo-simulation.py '
      • in your simulation directory, prepare control file contains simulation parameters.
      • run simulation: cosmo-simulation -f md.ini

Windows:

• No idea (no time to test) !!!

MacOS:

• No money to test !!!

Notes:

• Note that in cluster, when submit job, the environment may not load .bashrc, so need to load conda environment in job file: source PATH_TO_ANNACONDA/anaconda3/etc/profile.d/conda.sh
• Activate environment (e.g py310): conda activate py310

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Bugs

• If you encounter any bugs, please report the issue to Quyen Vu (vuqv.phys@gmail.com).
• Please note that any bugs encountered are my responsibility and not that of the authors of the models. Therefore, I kindly request that you refrain from bothering them regarding any issues that may arise.

Acknowledgments:

Cite this work

This software is based on the original work of Prof. Jeetain Mittal's group (hps family) and Prof. Rosana Collepardo-Guevara's group (mpipi model). We have not published any paper using this software yet. If you have used it in your publications, please cite the source accordingly.

• hps family (hps-urry, hps-kr, and hps-ss):

  • (hps-kr) Dignon, G. L.; Zheng, W.; Kim, Y. C.; Best, R. B.; Mittal, J. Sequence Determinants of Protein Phase Behavior from a Coarse-Grained Model. PLoS Comput. Biol. 2018, 1–23. https://doi.org/10.1101/238170.
  • (hps-urry) Regy, R. M.; Thompson, J.; Kim, Y. C.; Mittal, J. Improved Coarse-Grained Model for Studying Sequence Dependent Phase Separation of Disordered Proteins. Protein Sci. 2021, 30 (7), 1371–1379. https://doi.org/10.1002/pro.4094.
  • (hps-ss) Rizuan, A.; Jovic, N.; Phan, T. M.; Kim, Y. C.; Mittal, J. Developing Bonded Potentials for a Coarse-Grained Model of Intrinsically Disordered Proteins. J. Chem. Inf. Model. 2022, 62 (18), 4474–4485. https://doi.org/10.1021/acs.jcim.2c00450.

• mpipi

  • (mpipi) Joseph, J. A.; Reinhardt, A.; Aguirre, A.; Chew, P. Y.; Russell, K. O.; Espinosa, J. R.; Garaizar, A.; Collepardo-Guevara, R. Physics-Driven Coarse-Grained Model for Biomolecular Phase Separation with near-Quantitative Accuracy. Nat. Comput. Sci. 2021, 1 (11), 732–743. https://doi.org/10.1038/s43588-021-00155-3.