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.

1. Install conda. We highly recommend the conda-forge setup https://github.com/conda-forge/miniforge#miniforge3.

2. Clone this repo and enter it's directory.

git clone https://github.com/qrefine/qrefine.git qrefine && cd qrefine

3. Use the provided environment.yaml to generate a new conda environment called qrefine. After installation activate the enviroment. The activation (unless set to automatic) has to be done for every new shell:

   • full installation with pytorch/cuda

     mamba env create -n qrefine -f environment.yaml
     mamba activate qrefine
     

4. We need to install the reduce and probe programs. This is handled by a bash script that downloads and installs the programs into the correct path in the conda installation.

bash ./build_into_conda.sh

5. run the given source <path>/setpaths.sh command at the end of the script. Also this needs to sourced for every new shell.

6. (Optional) Check if the cuda AEV version of torchani was installed:

   mamba list | grep torchani
   ls $(python -c 'import site; print(site.getsitepackages()[0])')/torchani/cuaev
   

   It should say torchani=*=cuda... and the cuaev directory is present. If not you can try the the pip/wheel installation:

   mamba remove torchani
   pip install torchani
   

   Expert users proficient with installing cctbx via bootstrap.py have to extract the needed snippets from the bash script to install the missing parts.

Please make sure to use a python3 installation of Phenix: https://www.phenix-online.org/

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 pymongo
 git clone https://github.com/qrefine/qrefine.git modules/qrefine
 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.

A conda package is provided for qrefine. We currently make use of the nightly build of cctbx. Use conda >=23.10 or mamba The conda-forge setup https://github.com/conda-forge/miniforge#miniforge3 is recommended.

During quick development cycles the conda packages will lag behind as they are build manually.

conda create -n QR qrefine -c qrefine -c cctbx-nightly
conda activate QR
qr.test

Tests need to be run in an empty directory.

 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 want to see the available options and default values please type:

 qr.refine --show-defaults

Options are straightforward added to the command line arguments:

qr.refine tests/unit/data_files/helix.pdb engine=mopac clustering=0 gradient_only=1

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
• Lum Wang

Min Zheng, Jeffrey Reimers, Mark P. Waller, and Pavel V. 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 V. 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

Yaru Wang, Holger Kruse, Nigel W. Moriarty, Mark P. Waller, Pavel V. Afonine, and Malgorzata Biczysko, Optimal clustering for quantum refinement of biomolecular structures: Q|R#4 (2023) Theor. Chem. Acc. 142, 100. DOI: 10.1007/s00214-023-03046-0 bioRxiv 2022.11.24.517825 DOI:10.1101/2022.11.24.517825

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