• Python3.6+
• NumPy
• SciPy
• cvxopt
• IBM ILOG CPLEX Optimization Studio (Community Edition)

Optional requirements used for installation

• git
• conda
• pip

As an open source alternative to IBM ILOG CPLEX Optimization Studio:

• MIOSQP

If you have access to the conda package manager (Python 3.7 64bit miniconda), installing all dependencies is straightforward:

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

Installation instructions using pip can be found in the docs folder. Instructions using miniconda can be found here.

qFit also requires an open-source MIQP implementation, MIOSQP, which is based on the OSQP package. Installation instructions for MIOSQP can be found on its GitHub Repository.

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

git clone https://github.com/excitedstates/qfit-3.0
cd qfit-3.0
python setup.py install

The qfit package comes with several command line tools to model alternate conformers into electron densities. You should select the command line tool that is most suited for your task. Please, refer below for a few use case examples to understand which tool is best suited for your needs.

To remove single-conformer model bias, qFit should be used with a composite omit map. One way of generating such map is using the Phenix software suite:

phenix.composite_omit_map input.mtz model.pdb omit-type=refine

An example test case (3K0N) can be found in the example directory.

To model alternate conformers for all residues in a protein of interest using qFit, the following command should be used:

qfit_protein [MAP_FILE] -l FWT,PHWT [PDB_FILE] -bb -sa -T -s 5 -rb

This command will produce a multiconformer model that spans the entirety of the input target protein. You will encounter two output files of interest in the directory where this command was run: multiconformer_model.pdb and multiconformer_model2.pdb.

If you wish to specify a different directory for the output, this can be done using the flag -d.

The multiconformer_model.pdb contains the output of running the qfit_residue routine for every protein residue. This file may contain up to five alternate conformers per residue. However, some of these conformers may be spurious as alternate conformers of neighboring residues are not consistent with regards to each other.

After calculating the conformers described in multiconformer_model.pdb, qfit_protein identifies consistent protein segments, where some of the spurious, overlapping conformers, are discarded. The final model, with the consistent multiconformer model is multiconformer_model2.pdb. This file should then be used as input to the refinement script provided in ./scripts.

By default, qFit expects the labels FWT,PHWT to be present in the input map. Different labels can be set accordingly using the flag -l.

Using the example 3K0N:

qfit_protein /path/to/3K0N.mtz -l 2FOFCWT,PH2FOFCWT /path/to/3K0N.pdb -bb -sa -T -s 5 -rb

After multiconformer_model2.pdb has been generated, refine this model using:

/path/to/qfit-3.0/scripts/post_refine_phenix.csh multiconformer_model2.pdb /path/to/3K0N.mtz IOBS SIGIOBS

Bear in mind that this final step currently depends on an existing installation of the Phenix software suite. We plan to remove this dependency in future releases.

qfit_residue [MAP_FILE] [PDB_FILE] [RESIDUE,CHAIN]

Using the example 3K0N:

qfit_residue /path/to/3K0N.mtz /path/to/3K0N.pdb A,113

This will produce a parsimonious model containing up to 5 alternate conformers for residue 113 of chain A of 3K0N.

qFit can also use ccp4 map files as input. To model alternate conformers using this type of map, it is also necessary to provide the resolution of the data, which can be achieved by using the flag -r.

qfit_residue [MAP_FILE] [PDB_FILE] [RESIDUE,CHAIN] -r [RESOLUTION]

Using the example 3K0N:

qfit_residue /path/to/3K0N.ccp4 /path/to/3K0N.pdb A,113 -r 1.39

In its default mode, qfit_residue only samples side chain conformations. While this is inherently faster, to take full advantage of qfit modeling capabilities, we recommend enabling backbone sampling using our KGS routine. This can be achieved using the -bb flag.

For optimal results, we also recommend sampling the bond angle N-CA-CB, which can be activated by means of the -sa flag.

Other useful sampling parameters that can be tweaked:

• Step size (in degrees) sampled around each rotamer: -s flag (default: 6)
• Rotamer neighborhood sampled: -rn flag (default: 40)
• Parsimonious selection of the number of conformers output by qFit using the Bayesian Information Criterion (BIC): -T flag.
• Randomize the b-factors of generated conformers: -rb flag (recommended).

Using the example 3K0N:

qfit_residue /path/to/3K0N.mtz /path/to/3K0N.pdb A,113 -bb -sa -s 5 -T -rb -rn 45

For a full list of options, run:

qfit_residue -h

Using the example 3K0N:

qfit_protein /path/to/3K0N.mtz /path/to/3K0N.pdb -bb -sa -s 5 -T -rn 45

The qfit_protein routine can be executed in parallel and the number of concurrent threads can be adjusted using the -p flag.

Using the example 3K0N, spawning 30 parallel threads:

qfit_protein /path/to/3K0N.mtz /path/to/3K0N.pdb -bb -sa -s 5 -T -rn 45 -p 30

Depending on the resolution, the default parameters for the identification of consistent protein segments may prove too strict, leading to the removal of perfectly valid alternate conformers. We are currently working towards calibration of the parameters in a resolution-dependent manner.

In the meantime, if you notice that your conformer of interest is present in multiconformer_model.pdb, but was subsequently removed by qfit_protein, you can re-process the initial model with less stringent parameters using the qfit_segment routine:

qfit_segment /path/to/3K0N.mtz /path/to/multiconformer_model.pdb -Ts -T 14 -f 3

To model alternate conformers of ligands, the command line tool qfit_ligand should be used:

qfit_ligand [MAP_FILE] [PDB_FILE] [LIGAND,CHAIN]

Where LIGAND corresponds to the numeric identifier of the ligand on the PDB (aka res. number).

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

Several modules were taken from 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.

The Xpleo software and LoopTK package have been major inspirations for the inverse kinematics functionality.