Build multi-conformer models for ligands
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README.md

qFit-ligand

Requirements

  • Python2.7
  • NumPy
  • SciPy
  • cvxopt
  • IBM ILOG CPLEX Optimization Studio (Community Edition)

Optional for MTZ to CCP4 file conversion

  • CCTBX or Phenix

Optional requirements used for installation

  • pip
  • conda
  • git

Installation

If you have access to the conda package manager, installing all dependencies is straightforward

conda install -c conda-forge numpy scipy cvxopt
conda install -c ibmdecisionoptimization cplex

If you prefer the more traditional pip tool, the requirements can be installed as follows

pip install numpy scipy cvxopt

Next, obtain a copy of CPLEX, the Community Edition will do, by registering and downloading it from the IBM website. After installing and unpacking the software, install the CPLEX Python interface

cd <CPLEX_ROOT>/cplex/python/2.7/x86_64_<PLATFORM>
python setup.py install

where <CPLEX_ROOT> is the directory where you installed CPLEX, and <PLATFORM> is a platform dependent string, such as linux for Linux systems and osx for macOSX.

You are now all set now to install qfit_ligand. Installation of qfit_ligand is as simple as

git clone https://github.com/excitedstates/qfit_ligand
cd qfit_ligand
python setup.py install

Usage

The main program that comes with installing qfit_ligand is the eponymously named qfit_ligand command line tool. It has two calling interfaces

qfit_ligand <CCP4-map> <resolution> <PDB-of-ligand> -r <PDB-of-receptor>
qfit_ligand <CCP4-map> <resolution> <PDB-of-ligand-and-receptor> --selection <chain>,<resi>

where <CCP4-map> is a 2mFo-DFc electron density map in CCP4 format, and <resolution> is its corresponding resolution in angstrom. In the first command,<PDB-of-ligand> is a PDB file containing solely the ligand and <PDB-of-receptor> a PDB file containing the receptor (and other ligands). In the second command, <PDB-of-ligand-and-receptor is a PDB file containing both the ligand and receptor, and --selection requires the chain and residue id of the ligand as a comma separated value, e.g. A,1234. Note that the receptor (and other ligands) are used to determine the scaling factor of the density map and used for collision detection during conformer sampling.

To see all options, type

qfit_ligand -h

The main options are -s to give the angular stepsize in degree, and -b to provide the number of degrees of freedom that are sampled simultaneously. Reasonably values are -s 1 -b 1, -s 6 -b 2, and -s 24 -b 3. Decreasing -s and especially increasing -b further will be RAM memory intensive and will take significantly longer.

Other useful options are -d to store the results in a directory to your chosing, and if -v to be more verbose about the output, especially when using it interactively in the shell.

The output of qfit_ligand consists of the following files:

  • multiconformer.pdb: Final occupancy weighted ligand multiconformer model.
  • conformer_N.pdb: Conformers found before the final rescoring round, where N is an integer.
  • qfit_ligand.log: Logging file of run.

Combining multiconformer ligand with receptor

To combine the output multiconformer ligand with the receptor use the following command

qfit_combine <multiconformer-ligand-pdbs> -r <receptor> --remove -o <output>

where <multiconformer-ligand-pdbs> is one or more PDB files of the ligand you want to recombine with the receptor, <receptor> the PDB file of the receptor, the --remove flag is optionally used the remove the ligand in the current <receptor> file, and <output> is the file name of the combined PDB structure.

Converting MTZ to CCP4

If you have access to CCTBX/Phenix use phenix.mtz2map to convert a MTZ file to CCP4. Make sure it outputs the 2mFo-DFc map. Read the documentation for available options.

License

The code is licensed under the MIT licence (see LICENSE).

The spacegroups.py module is based on the SpaceGroups.py module of the pymmlib package, originally licensed under the Artistic License 2.0. See the licenses directory for a copy of the original source code and its full license.

The elements.py is licensed under MIT, Copyright (c) 2005-2015, Christoph Gohlke. See file header.