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.
Please first install Phenix, see https://www.phenix-online.org/
Note: Python 3.7 installer with the "modules" directory phenix-installer--intel-linux-x86_64.tar.xz starts from version 1.21rc1-4904.
Once you have Phenix installed, go to the directory where you installed Phenix.
source phenix_env.sh # source phenix_env.csh
phenix.python -m pip install ase==3.17.0
phenix.python -m pip install pymongo
cd modules
git clone https://github.com/qrefine/qrefine.git
cd ../build
libtbx.configure qrefine
source setpaths.sh # source setpaths.csh
Note: you may need to use sudo depending on the permissions of your Phenix installation.
NOT UP TO DATE
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.
mkdir tests
cd tests
qr.test
If any of the tests fail, please raise an issue here: issue tracker
Can be found at: https://qrefine.com/qr.html
If you run into any trouble please ask for help:
qr.refine --help
If you want to see the available options and default values please type:
qr.refine --defaults or qr.refine --show
The best way to get a hold of us is by sending us an email: qrefine@googlegroups.com
- Pavel Afonine
- Malgorzata Biczysko
- Mark Waller
- Nigel Moriarty
- Holger Kruse
- Min Zheng
- Xu Yanting
- Lum Wang
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) Acta Cryst. D76, 1184-1191.
DOI：10.1107/S2059798320013194
bioRxiv 2020.05.25.115386.
DOI:0.1101/2020.05.25.115386
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