Quantum Refinement Module
Quantum Chemistry can improve bio-macromolecular structures, especially when only low-resolution data derived from crystallographic or cryo-electron microscopy experiments are available. Quantum-based refinement utilizes chemical restraints derived from quantum chemical methods instead of the standard parameterized library-based restraints used in experimental refinement packages. The motivation for a quantum refinement is twofold: firstly, the restraints have the potential to be more accurate, and secondly, the restraints can be more easily applied to new molecules such as drugs or novel cofactors.
However, accurately refining bio-macromolecules using a quantum chemical method is challenging due to issues related to scaling. Quantum chemistry has proven to be very useful for studying bio-macromolecules by employing a divide and conquer type approach. We have developed a new fragmentation approach for achieving a quantum-refinement of bio-macromolecules.
Quickstart
Please first install PHENIX, see https://www.phenix-online.org/
Once you have PHENIX installed, go to the directory where you installed PHENIX.
source phenix_env.sh
phenix.python modules/cctbx_project/libtbx/auto_build/bootstrap.py --builder=qrefine --use-conda --nproc=8
Note: adding --nproc=N can speedup the compilation step.
Note: you may need to use sudo depending on the permissions of your PHENIX installation.
Using the Git repository of cctbx.
To remain up-to-date with the changes in the cctbx project that contains many of the functions used in Q|R, remove the cctbx_project directory in the modules directory. The above command will clone it from GitHub.
In case the quickstart command fails
Clone the qrefine repo in the modules directory of the Phenix installation.
phenix.python -m pip install ase==3.17.0
phenix.python -m pip install pymongo
git clone https://github.com/qrefine/qrefine.git
libtbx.configure qrefine
Run Tests
mkdir tests
cd tests
qr.test
If any of the tests fail, please raise an issue here: issue tracker
Documentation
Can be found at: https://qrefine.com/qr.html
Help
If you run into any trouble please ask for help:
qr.refine --help
Commandline options
If you want to see the available options and default values please type:
qr.refine --defaults or qr.refine --show
Contact us
The best way to get a hold of us is by sending us an email: [email protected]
Developers
- Min Zheng
- Pavel Afonine
- Mark Waller
- Nigel Moriarty
- Malgorzata Biczysko
- Xu Yanting
- Lum Wang
- Holger Kruse
Citations:
Min Zheng, Jeffrey Reimers, Mark P. Waller, and Pavel Afonine, Q|R: Quantum-based Refinement, (2017) Acta Cryst. D73, 45-52. DOI: 10.1107/S2059798316019847
Min Zheng, Nigel W. Moriarty, Yanting Xu, Jeffrey Reimers, Pavel Afonine, and Mark P. Waller, Solving the scalability issue in quantum-based refinement: Q|R#1 (2017). Acta Cryst. D73, 1020-1028. DOI: 10.1107/S2059798317016746
Min Zheng, Malgorzata Biczysko, Yanting Xu, Nigel W. Moriarty, Holger Kruse, Alexandre Urzhumtsev, Mark P. Waller, and Pavel V. Afonine, Including Crystallographic Symmetry in Quantum-based Refinement: Q|R#2 (2020). Acta Cryst. D76, 41-50. DOI: 10.1107/S2059798319015122
Lum Wang, Holger Kruse, Oleg V. Sobolev, Nigel W. Moriarty, Mark P. Waller, Pavel V. Afonine, and Malgorzata Biczysko, Real-space quantum-based refinement for cryo-EM: Q|R#3 (2020) bioRxiv 2020.05.25.115386; DOI:0.1101/2020.05.25.115386
Clustering
Min Zheng, Mark P. Waller, Yoink: An interaction‐based partitioning API, (2018) Journal of Computational Chemistry, 39, 799–806. DOI: 10.1002/jcc.25146
Min Zheng, Mark P. Waller, Toward more efficient density-based adaptive QM/MM methods, (2017)Int J. Quant. Chem e25336 DOI: 10.1002/qua.25336
Min Zheng, Mark P. Waller, Adaptive QM/MM Methods, (2016) WIREs Comput. Mol. Sci., 6, 369–385. DOI: 10.1002/wcms.1255
Mark P. Waller, Sadhana Kumbhar, Jack Yang, A Density‐Based Adaptive Quantum Mechanical/Molecular Mechanical Method (2014) ChemPhysChem 15, 3218–3225. DOI: 10.1002/cphc.201402105