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Description:

proteinclusterconformers 1.2.0

Monomeric protein conformational state clustering
These scripts can be used to cluster a parsed set of monomeric protein chains via a global conformational change metric based on CA distances. Once the peptide chains destined for clustering have been specified, a pairwise CA distance matrix for each chain is produced. Distance difference matrices are then generated, again, pairwise but between CA distance matrices here. Therefore, for N unique peptide chains, N CA distance matrices and N^2 distance difference matrices are generated.
Additional scripts are provided to cluster the chains based on distance-based scores calculated from all pairwise distance difference matricies, as well as scripts to produce dendrograms of the clustering results, swarm plots of the scores, and heatmaps for each distance difference matrix.
Example input data is provided in the benchmark_data/examples folder, including scripts to download and save data from the PDBe-KB's benchmark conformational state dataset. Example scripts are included in examples, which run complete executions of the entire pipeline for a selection of structures from several difference UniProt accessions.
For intructions on importing protein-cluster-conformers into your own Python code, refer to /tutorials/instructions.ipynb.
Dependencies:
protein-cluster-conformers requires >=Python3.10 to run. Initialise virtual environment and install dependencies with:
$ cd protein-cluster-conformers]
$ python3.10 -m venv cluster_venv
$ source cluster_venv/bin/activate
$ python -m pip install -r requirements.txt

CLI: Clustering structures
To cluster a set of protein structures, run the find_clusters.py script:
$ python find_conformers.py [-h] [-v] -u UNIPROT -m MMCIF [MMCIF ...]
[-s PATH_CLUSTERS] -c PATH_CA [-d PATH_DD]
[-g PATH_DENDROGRAM [PATH_DENDROGRAM ...]]
[-w PATH_SWARM [PATH_SWARM ...]] [-o PATH_HISTOGRAM]
[-a PATH_ALPHA_FOLD]
[-0 FIRST_RESIDUE_POSITION] [-1 LAST_RESIDUE_POSITION]

The following parameters can be parsed:
required arguments:
-u UNIPROT, --uniprot UNIPROT
UniProt accession
-m MMCIF [MMCIF ...], --mmcif MMCIF [MMCIF ...]
Enter list of paths to mmCIFs that overlap a given UniProt segment
optional arguments:
-h, --help show this help message and exit
-v, --verbose Increase verbosity
-s PATH_CLUSTERS, --path_clusters PATH_CLUSTERS
Path to save clustering results
-c PATH_CA, --path_ca PATH_CA
Path to save CA distance matrices
-d PATH_DD, --path_dd PATH_DD
Path to save distance difference matrices
-g PATH_DENDROGRAM [PATH_DENDROGRAM ...], --path_dendrogram PATH_DENDROGRAM [PATH_DENDROGRAM ...]
Path to save dendrogram of clustering results
-w PATH_SWARM [PATH_SWARM ...], --path_swarm PATH_SWARM [PATH_SWARM ...]
Path to save swarm plot of scores
-o PATH_HISTOGRAM, --path_histogram PATH_HISTOGRAM
Path to save histograms of distance difference maps
-a PATH_ALPHA_FOLD, --path_alpha_fold PATH_ALPHA_FOLD
Path to save AlphaFold Database structure
-0 FIRST_RESIDUE_POSITION, --first_residue_position FIRST_RESIDUE_POSITION
First residue position in (UniProt) sequence
-1 LAST_RESIDUE_POSITION, --last_residue_position LAST_RESIDUE_POSITION
Last residue position in (UniProt) sequence

Run instructions
Option 1) Cluster only
To only cluster a set of monomeric protein structures that share part or all of the same UniProt sequence, run:
$ python find_clusters.py -u "A12345" \
-m /path/to/structure_1.cif [chains] \
-m /path/to/structure_2.cif [chains] \
... \
-m /path/to/structure_N.cif [chains] \
-s /path/to/save/clustering/results/

The paths to each structure are parsed using the -m flag.
Chain IDs (only struct_asym_id is currently recognised) should be given as space-delimited arguments after the path. Parse in multiple structures using consecutive -m flags. The UniProt accession must be parsed using the -u flag.
Example: O34926
$ python find_conformers.py -u "O34926" \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc3_updated.cif A B \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc5_updated.cif A B \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc6_updated.cif A B \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc7_updated.cif A B \
-s benchmark_data/examples/O34926/O34926_cluster_results/

By default, the pipeline only clusters the parsed mmCIFs (and specified chains), saving clustering results to a CSV file in -s specified directory.

Option 2) Save matrices only
To save the matrices produced in the pipeline, simply specify the path in which to save them using the -c flag for CA distance matrices and the -d flag for CA distance difference matrices:
$ python find_clusters.py -u "A12345" \
-m /path/to/structure_1.cif [chains] \
-m ... \
-s /path/to/save/cluster_results.csv \
-c /path/to/save/CA/distance/matices \
-d /path/to/save/distance/difference/matrices/

These flags are mutually exclusive, meaning either or both can be used at the same time.
Example: O34926
$ python find_clusters.py -u "O34926" \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc3_updated.cif A B \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc5_updated.cif A B \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc6_updated.cif A B \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc7_updated.cif A B \
-c benchmark_data/examples/O34926_CA_distances \
-d benchmark_data/examples/O34926_distance_differences/

Option 3) Render distance difference maps only
2D histograms (heatmaps) can be rendered and saved for each CA distance difference matrix by specifying the save directory using the -o flag:
$ python find_clusters.py -u "A12345" \
-m /path/to/structure_1.cif [chains] \
-m ... \
-o /path/to/save/distance/difference/2D/histograms/

To save the histogram in the default directory (test/ouputs/dd_histograms), simply parse the -o flag without a path.
The resulting plots are saved in PNG format (to save render time). E.g:

Example: O34926
$ python find_clusters.py -u "O34926" \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc3_updated.cif A B \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc5_updated.cif A B \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc6_updated.cif A B \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc7_updated.cif A B \
-o ./benchmark_data/examples/O34926/O34926_distance_difference_maps/

Option 4) Render dendrogram only
From the clustering results, a dendrogram can be rendered to show the relationships between all clustered chains. To save a dendrogram of the hierarchical clustering results, run:
$ python find_clusters.py -u "A12345" \
-m /path/to/structure_1.cif [chains] \
-m ... \
-g /path/to/save/dendrogram/ [png svg]

where either a png or svg file type is saved. E.g.

Example: O34926
$ python find_clusters.py -u "O34926" \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc3_updated.cif A B \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc5_updated.cif A B \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc6_updated.cif A B \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc7_updated.cif A B \
-g benchmark_data/examples/O34926/O34926_cluster_results/ png svg

Option 5) Render swarm plot
The scores generated between pairwise structure comparisons can be plotted as a swarm plot by parsing the -w flag:
$ python find_clusters.py -u "A12345" \
-m /path/to/structure_1.cif [chains] \
-m ... \
-w /path/to/save/swarm_plot/ [png svg]

Like rendering the dendrogram, the swarm plot can either be saved as png, svg or both. E.g.

Example: O34926
$ python find_clusters.py -u "O34926" \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc3_updated.cif A B \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc5_updated.cif A B \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc6_updated.cif A B \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc7_updated.cif A B \
-w benchmark_data/examples/O34926/O34926_cluster_results/ png svg

Option 6) Include AlphaFold Database structure(s)
By parsing in the -a flag, the script will attempt to download and cluster the pre-generated AlphaFold structure, stored on the AlphaFold Database. You do not need to have downloaded the predicted AlphaFold structure already but must be connected to the internet. The structure will be saved
$ python find_clusters.py -u "A12345" \
-m /path/to/structure_1.cif [chains] \
-m ... \
-a

Example: O34926
$ python find_clusters.py -u "O34926" \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc3_updated.cif A B \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc5_updated.cif A B \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc6_updated.cif A B \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc7_updated.cif A B \
-a

Option 7) Run all
Example #1: O34926
$ python find_conformers.py -u "O34926" \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc3_updated.cif A B \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc5_updated.cif A B \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc6_updated.cif A B \
-m benchmark_data/examples/O34926/O34926_updated_mmcif/3nc7_updated.cif A B \
-c benchmark_data/examples/O34926/O34926_ca_distances/ \
-d benchmark_data/examples/O34926/O34926_distance_differences/ \
-s benchmark_data/examples/O34926/O34926_cluster_results/ \
-o benchmark_data/examples/O34926/O34926_distance_difference_maps/ \
-g benchmark_data/examples/O34926/O34926_cluster_results/ png svg
-a

or use the run_O34926.sh script.
$ ./run_O34926.sh

Example #2: P15291
python3 find_conformers.py -u "P15291" \
-m benchmark_data/examples/P15291/P15291_updated_mmcif/2fy7_updated.cif A \
-m benchmark_data/examples/P15291/P15291_updated_mmcif/2fya_updated.cif A \
-m benchmark_data/examples/P15291/P15291_updated_mmcif/2fyb_updated.cif A \
-m benchmark_data/examples/P15291/P15291_updated_mmcif/6fwu_updated.cif A B \
-m benchmark_data/examples/P15291/P15291_updated_mmcif/2fyc_updated.cif A B \
-m benchmark_data/examples/P15291/P15291_updated_mmcif/2fyd_updated.cif A B \
-c benchmark_data/examples/P15291/P15291_ca_distances/ \
-d benchmark_data/examples/P15291/P15291_distance_differences/ \
-s benchmark_data/examples/P15291/P15291_cluster_results/ \
-o benchmark_data/examples/P15291/P15291_distance_difference_maps/ \
-g benchmark_data/examples/P15291/P15291_cluster_results/ png svg
-w benchmark_data/examples/P15291/P15291_cluster_results/ png svg
-a

or execute the run_P15291.sh script.
$ ./run_P15291.sh

When imported into Orc, the arguments required to execute the clustering process correctly will be parsed into the class instance and related methods as lists generated from the preceding functions called by the existing protein-superpose pipeline.

Run on benchmark dataset
The scripts above are called by the run_benchmark.py wrapper. To generate conformational clustering results for the included benchmark dataset, run:
$ python cluster_benchmark.py

This will call the run_benchmark(...) functions included in ca_distance.py, distance_difference.py, cluster.py, plot_distance_difference.py, plot_dendrogram.py and plot_swarm_plot.py. No arguments need parsing into the script.
Results will be saved in the ./benchmark_data/ folder.

Contributing
Install developer dependencies:
pip install -r dev-requirements.txt

To run unit tests on the package, the Pytest framework is recommented and can be performed with:
pytest --cov=cluster_conformers --cov-report=html -v

The following dependencies will be required:

pytest-cov
pytest-forked
purest-xdist (optional)

They are installed along with the main package dependencies in requirements.txt.