QMzyme 0.1.1

QM-based enzyme model generation and validation.

QMzyme is currently under-development. Please note the user interface may change! The first stable API version will be released as QMzyme==1.0.0 on PyPi.

QMzyme is a Python toolkit to facilitate (quantum mechanical) QM-based enzyme calculations. The GenerateModel module guides the process of generating calculation ready truncated or partitioned molecule regions. Any input file(s) accepted by MDAnalysis to create a Universe object can be used to start working in QMzyme. From there, the code relies on more flexible QMzyme objects: QMzymeAtom, QMzymeResidue, QMzymeRegion and QMzymeModel.

Full documentation with installation instructions, technical details and examples can be found in Read the Docs.

Contributing to QMzyme

For suggestions and improvements of the code (greatly appreciated!), please reach out through the issues and pull requests options of Github. See documentation about contributing guidelines.

Code Contributors/Developers

• Heidi Klem, LSU (main developer)
• Young Woo Kim, LSU
• Arkanil Roy, LSU
• Demian Riccardi, NIST

References

QMzyme has not been formally published. Please refer back here for the proper citation once it has.

QMzyme's main dependency is MDAnalysis. When using QMzyme please cite MDAnalysis accordingly:

• R. J. Gowers, M. Linke, J. Barnoud, T. J. E. Reddy, M. N. Melo, S. L. Seyler, D. L. Dotson, J. Domanski, S. Buchoux, I. M. Kenney, and O. Beckstein. MDAnalysis: A Python package for the rapid analysis of molecular dynamics simulations. In S. Benthall and S. Rostrup, editors, Proceedings of the 15th Python in Science Conference, pages 98-105, Austin, TX, 2016. SciPy, doi:10.25080/majora-629e541a-00e.

• N. Michaud-Agrawal, E. J. Denning, T. B. Woolf, and O. Beckstein. MDAnalysis: A Toolkit for the Analysis of Molecular Dynamics Simulations. J. Comput. Chem. 32 (2011), 2319-2327, doi:10.1002/jcc.21787. PMCID:PMC3144279

If you use QMzyme to write QM software calculation input files, please include this citation for AQME:

• Alegre-Requena, J. V.; Sowndarya, S.; Pérez-Soto, R.; Alturaifi, T.; Paton, R. AQME: Automated Quantum Mechanical Environments for Researchers and Educators. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2023, 13, e1663. (DOI: 10.1002/wcms.1663).

Acknowledgements

MolSSI for providing the CMS Cookiecutter that informed initial project architecture.

We really THANK all the testers for their feedback, including:

• Helena Giramé (2024, Feixas and Garcia-Borràs groups at University of Girona, IQCC)
• Cristina Berga (2024, Feixas and Garcia-Borràs groups at University of Girona, IQCC)
• Aqza Elza John (2024, Feixas and Garcia-Borràs groups at University of Girona, IQCC)
• Hande Abeş (2024, Feixas and Garcia-Borràs groups at University of Girona, IQCC)
• Raul Perez-Soto (2024, Kim group at Colorado State University)
• Alex Platt (2024, Paton group at Colorado State University)

We greatly acknowledge HPC resources obtained through ACCESS:

• Timothy J. Boerner, Stephen Deems, Thomas R. Furlani, Shelley L. Knuth, and John Towns. 2023. ACCESS: Advancing Innovation: NSF’s Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support. “In Practice and Experience in Advanced Research Computing (PEARC ’23)”, July 23–27, 2023, Portland, OR, USA. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3569951.3597559.
• This work used Expanse at SDSC through allocation BIO230144 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.

Copyright

Copyright (c) 2024, Heidi Klem