CCParams

Optimal data-driven parameterization of coiled coils

Requirements

pyRMSD, scipy, numpy, sklearn, Biopython, PyRosetta (optional).

Description of files

cc_dataset_mmol_all

List of parallel dimeric CC structures exported from the CC+ database, used in this study.

ExtractDimerACoords.py

Extraction of 15-residue CC fragments, here limited to the a-cluster.

HelixTemplate.py

Parameterization of an a-helix.

CCParamsLib.py

Extended parameterization and reconstruction procedures.

ProcessDimerACoords.py

Generation of an extended parameterisation vector for every CC fragment, preparing the dataset for PCA.

CCParamsStats.py

PCA of the extended dataset and respective parameterizations with varying number of parameters.

CCParamsInteractive.py

Interactive visualisation of the CC parameters in PyMOL. Requires PyQT5, PyRosetta, and the PyMOL-PyRosetta link must be established prior to running the app (see PyRosetta documentation).

helix_template.pkl and pca_dimer_a.pkl

Pickled PCA objects with parameterizations of an a-helical and a dimeric CC (a-cluster) fragments.

Reproducing results of the paper

Here we focus only on the a-cluster parameterization. The procedure is, however, the same if one wishes to process fragments in other registers, or all at once.

1. Extract PDB coordinates from MMOL.tar.xz.
2. Run ExtractDimerACoords.py. This script will go through records in the cc_dataset_mmol_all file, locate all listed CC structures and extract regular fragments which start from the a heptad position. Expected script output:

Number of coiled coil records: 1866
Total number of fragments collected: 4118
Number of unique fragments: 2624
Number of unique fragments excluding outliers: 2538

Two files should appear, dimer_a_all.pkl and dimer_a_unique_0.2.pkl. The first one contains coordinates of all collected CC fragments from the a-cluster, the second one -- only non-redundant subset (in which no structure is similar with RMSD less than 0.2).

3. Run HelixTemplate.py. Note that in the paper we used coordinates of all fragments for this step, while here we take only the a-cluster as the input. This has no impact on the obtained parameterization and reconstruction statistics.

Expected script output (cf. Fig. 1d):

Total number of helices: 8236
Number of unique helices: 2967
Helix reconstruction stats, RMSD:
min: 0.0779318211206
max: 0.792185424998
mean: 0.229877830959
median: 0.205617162334

4. Run ProcessDimerACoords.py. This step will convert CC fragment coordinates from file dimer_a_unique_0.2.pkl into 'extended' parameterization and test the reconstruction accuracy. The results will be stored in the file dimer_a_params_coords.pkl.

Expected output (cf. Fig 3c):

CC reconstruction stats, RMSD:
min: 0.0899008249849
max: 0.680252038429
mean: 0.232043925963
median: 0.212188994827

5. Run CCParamsStats.py. This script will go through CC parameterizations with the number of parameters varying from 1 to 10 and output reconstruction statistics. Every reconstruction's parameters are optimised to minimize RMSD to the target structures, so it'll take a while.

Expected output, the first and the last batches are shown (cf. Fig 3c):

Number of components: 1
Fraction of structures under 1A RMSD: 0.982269503546
CC reconstruction stats, RMSD:
min: 0.190088304232
max: 1.10620154112
mean: 0.517878054167
median: 0.48013578893
...

Number of components: 10
Fraction of structures under 1A RMSD: 1.0
CC reconstruction stats, RMSD:
min: 0.0968117019343
max: 0.685145833173
mean: 0.250808975295
median: 0.231687949811

6. Launch PyMOL, Open File-> Run Script..., find and select PyMOL-RosettaServer.py (PyRosetta is required) Run CCParamsInteractive.py. If everything went well, you should be able to modify parameters of CCs (first 10 are shown) and see the geometry changing in PyMOL in real time. cf. Fig. 3d-f