Simulation-Enabled Estimation of Kinetic Rates Tools
Seekrtools is a set of software tools that interfaces with SEEKR programs, most notably with SEEKR2, in order to prepare and facilitate multiscale milestoning calculations.
This README is only a quickstart guide to get Seekrtools up and running as soon as possible. To see more detailed instructions and tutorials, please see https://seekrtools.readthedocs.io/en/latest or the docs/ subfolder.
- SEEKR2
- OpenMM (see SEEKR2 documentation to install OpenMM alongside SEEKR2)
Make sure that you have installed SEEKR2 before Seekrtools. (Most Seekrtools programs have SEEKR2 as a dependency). You can find the SEEKR2 Github repostory at https://github.com/seekrcentral/seekr2.git and the SEEKR2 documentation at https://seekr2.readthedocs.io/en/latest.
If you are using Mamba/Conda (recommended) with SEEKR, make sure that the environment is activated before executing the following steps to install Seekrtools:
git clone https://github.com/seekrcentral/seekrtools.git
cd seekrtools
python -m pip install .
Optionally, run unit tests:
pytest
The following people have contributed directly to the coding and validation efforts of Seekrtools (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)
- Anand Ojha (developer)
- Andy Stokely (developer)
- Lane Votapka (lead developer)
If you use Seekrtools, please cite one or more of the following SEEKR papers:
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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. In Review. https://doi.org/10.33774/chemrxiv-2021-pplfs
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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) 2021, Lane Votapka
Project based on the Computational Molecular Science Python Cookiecutter version 1.3.