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项目作者： qrefine

项目描述 ：
Quantum Refinement Module
高级语言： Python
项目地址: git://github.com/qrefine/qrefine.git
创建时间： 2017-04-04T04:32:01Z
项目社区：https://github.com/qrefine/qrefine
开源协议：Apache License 2.0
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Quantum Refinement Module

CI pipeline on Mamba
License
Dockerhub

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.

Installation

Depending on your use case, installation of qrefine follows 3 paths:

Requirements:

  • python >= 3.9
  • conda binary, e.g., miniconda. (A conda environment is not needed for Phenix)
  • For Apple Silicon architecture please see the additional notes!

AQuaRef notes (aimnet2):
To use AQuaRef follow the installation instructions above and request installation of aimnet2.
A few extra notes, also for performance, are provided here: AQuaRef notes

Apple Silicon

We cannot recommend to run qrefine with clustering on Apple Silicon machines as the pair interaction is unreliable (unknown cause).
When following the cctbx installation route use the following to create the env:

  1.     conda env create -n qrefine -f config/arm64-osx.yaml

For Phenix installations we recommend to switch the blas implementation to apple’s accelerate in 

  1.     conda install -p <phenix_conda> libblas=*=*accelerate

Run Tests

Tests need to be run in an empty directory.

  1.  mkdir tests
  2.  cd tests
  3.  qr.test

If any of the tests fail, please raise an issue here: issue tracker

Documentation

Unfortunately the HTML documentation has not been updated yet.
It can be found at: https://qrefine.com/qr.html

Commandline options

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

  1.  qr.refine --show-defaults

Example

command line options are added like this:

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

for AQuaRef run

  1. qr.refine your_pdb.pdb your_map.map mode=refine engine=aimnet2

Contact us

The best way to get a hold of us is by sending us an email: qrefine@googlegroups.com

Developers

Citations:

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

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