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Simulation-Enabled Estimation of Kinetic Rates - Version 2
Fast and versatile multiscale milestoning to compute molecular thermodynamics and kinetics.
Prepare and run milestoning calculations in the OpenMM, NAMD, and/or Browndye2 simulation engines to obtain the kinetics and thermodynamics of molecular processes such as: ligand-receptor binding and unbinding, membrane permeability, internal molecular dynamics, and many other potential situations.
This README is only a quickstart guide to get SEEKR2 up and running as soon as possible. To see more detailed instructions and tutorials, please see https://seekr2.readthedocs.io/en/latest or the docs/ subfolder.
Many of the dependencies for SEEKR2 will be installed alongside SEEKR2, but some must be installed separately, and are installed first, before SEEKR2.
OpenMM is recommended for the molecular dynamics (MD) stage of SEEKR2. SEEKR2 also needs the SEEKR2 OpenMM Plugin in order to use OpenMM for MD simulations.
The easiest, quickest way to install the SEEKR2 OpenMM Plugin is to use Mamba. If you don't already have Mamba installed, Download Mamba from https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh and run the downloaded script and fill out the prompts.
With Mamba working, install the SEEKR2 OpenMM Plugin:
mamba install seekr2_openmm_plugin
One can test the installation by opening a Python terminal and typing:
import seekr2plugin
If there is a problem related to not being able to find libOpenMM8.1, one can try specifying the OpenMM version:
mamba install seekr2_openmm_plugin openmm=8.1
If there is an error such as "CUDA_ERROR_UNSUPPORTED_PTX_VERSION", one can see if a different version of CudaToolKit will work:
mamba install seekr2_openmm_plugin cudatoolkit=11.7
If there is an error such as "No module named seekr2plugin", one can always try installing an older version of OpenMM and CUDA:
mamba install seekr2_openmm_plugin cudatoolkit=10.2 openmm=7.7
Alternatively, NAMD2 may be used for MD if desired. See the NAMD2 section below for installation instructions.
SEEKR2 needs Browndye2 if Brownian dynamics (BD) simulations will be run (necessary for k-on calculations). Please see (https://browndye.ucsd.edu/) for Browndye2 installation instructions.
If OpenMM is not desirable or available for the MD simulations, SEEKR2 may use NAMD2 in order to run MD simulations. NAMD2 is frequently already available on shared computing resources like clusters and supercomputers. Not all SEEKR2 options may be available using NAMD2.
If you wish to install NAMD2 yourself, please see http://www.ks.uiuc.edu/Research/namd/ for instructions to install NAMD.
Once the dependencies are installed, we may install SEEKR2. First, clone this repository and install the package:
git clone https://github.com/seekrcentral/seekr2.git
cd seekr2
python -m pip install .
To test SEEKR2, run the following command in the seekr2/ directory:
pytest
Additional continuous integration tests may be run from the Python scripts in the seekr2/seekr2/continuous_integration/ directory.
A SEEKR2 calculation needs a "Model Input File" to run. Several examples may be found in seekr2/seekr2/data. Execute the following commands within the seekr2/seekr2/ directory to run a sample calculation on the host-guest system:
(It is assumed that both Browndye2 and the the SEEKR2 OpenMM Plugin have already been installed).
python prepare.py data/sample_input_mmvt_openmm.xml
python run.py any ~/test_mmvt_openmm/model.xml
python converge.py ~/test_mmvt_openmm/model.xml
python analyze.py ~/test_mmvt_openmm/model.xml
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In general, SEEKR2 programs can be run with the '-h' argument to see all available options. Please see https://seekr2.readthedocs.io/en/latest for a detailed description of programs and options.
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the run.py program accepts an INSTRUCTION as its first argument, and the following options are available:
- "any" - run any MD or BD calculation that still needs to be run.
- "any_md" - run any MD calculation that still needs to be run.
- "any_bd" - run any BD calculation that still needs to be run.
- INTEGER - run the MD anchor whose index is INTEGER. Examples: "0", "1", etc.
- "b_surface" - run BD simulations starting at the b-surface.
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When an option must be modified, it is usually necessary to make the change in the input file and not the model file, and then re-run prepare.py to make a new model file. In some exceptions, the model file may be directly (and carefully) modified.
The following people have contributed directly to the coding and validation efforts of SEEKR2 (listed an alphabetical order of last name). Thanks also to everyone who has helped or will help improve this project by providing feedback, bug reports, or other comments.
- Rommie Amaro (principal investigator)
- Ilker Deveci (developer)
- Hilliary Frank (contributor)
- Sasha Heyneman (contributor)
- Ben Jagger (developer)
- Anand Ojha (developer)
- Andy Stokely (developer)
- Natalie Timms (contributor)
- Lane Votapka (lead developer)
- Jeff Wagner (contributor)
For BibTex files of any or all of the following citations, please visit: https://seekr2.readthedocs.io/en/latest/citations.html
If you use SEEKR2, please cite the following paper:
- Votapka, L. W.; Stokely, A. M.; Ojha, A. A.; Amaro, R. E. SEEKR2: Versatile Multiscale Milestoning Utilizing the OpenMM Molecular Dynamics Engine. J. Chem. Inf. Mod. 2022 62 (13), 3253-3262. DOI: 10.1021/acs.jcim.2c00501
One should also cite SEEKR2's dependencies:
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Van Der Walt, S., Colbert, S.C. & Varoquaux, G., 2011. The NumPy array: a structure for efficient numerical computation. Computing in Science & Engineering, 13(2), pp.22–30.
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Jones, E. et al., 2001. SciPy: Open source scientific tools for Python.
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Hunter, J.D., 2007. Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3), pp.90–95.
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T.D. Swinburne and D.J. Wales, Defining, Calculating, and Converging Observables of a Kinetic Transition Network, J. Chemical Theory and Computation (2020), https://doi.org/10.1021/acs.jctc.9b01211
You may also optionally cite the following papers related to SEEKR2:
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Ojha, A. A., Votapka L. W., Amaro, R. E. QMrebind: incorporating quantum mechanical force field reparameterization at the ligand binding site for improved drug-target kinetics through milestoning simulations. Chemical Science 14 (45), 13159-13175
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Ojha A. A., Srivastava A., Votapka L. W., and Amaro R. E. Selectivity and Ranking of Tight-Binding JAK-STAT Inhibitors Using Markovian Milestoning with Voronoi Tessellations. Journal of Chemical Information and Modeling 2023 63 (8), 2469-2482. DOI: 10.1021/acs.jcim.2c01589
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Votapka, L. W.; Jagger, B. R.; Heyneman, A. L.; Amaro, R. E. SEEKR: Simulation Enabled Estimation of Kinetic Rates, A Computational Tool to Estimate Molecular Kinetics and Its Application to Trypsin–Benzamidine Binding. J. Phys. Chem. B 2017, 121 (15), 3597–3606. https://doi.org/10.1021/acs.jpcb.6b09388.
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Jagger, B. R.; Ojha, A. A.; Amaro, R. E. Predicting Ligand Binding Kinetics Using a Markovian Milestoning with Voronoi Tessellations Multiscale Approach. J. Chem. Theory Comput. 2020. https://doi.org/10.1021/acs.jctc.0c00495.
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Jagger, B. R.; Lee, C. T.; Amaro, R. E. Quantitative Ranking of Ligand Binding Kinetics with a Multiscale Milestoning Simulation Approach. J. Phys. Chem. Lett. 2018, 9 (17), 4941–4948. https://doi.org/10.1021/acs.jpclett.8b02047.
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Votapka LW, Amaro RE (2015) Multiscale Estimation of Binding Kinetics Using Brownian Dynamics, Molecular Dynamics and Milestoning. PLOS Computational Biology 11(10): e1004381. https://doi.org/10.1371/journal.pcbi.1004381
Copyright (c) 2024, Lane Votapka
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