This is a reimplementation of Pintilie et al., 2020 [1] using pure Python. It is also faster since it does not use Chimera and tries to vectorize as many things as possible. Please check the implementation to make sure the results are correct.
To use this, you can run the calculate_per_residue_q_scores function in q_score.py
From the repository root, create and activate a virtual environment, then install
the runtime dependencies and the qscore CLI:
python3 -m venv .venv source .venv/bin/activate python -m pip install --upgrade pip setuptools wheel python -m pip install -r requirements.txt python -m pip install -e .
The editable install (-e .) makes the qscore command available while still
using the source files in this checkout. For a non-editable install, use
python -m pip install . instead.
Check that the command is installed:
qscore --help qscore --version
Calculate per-residue Q-scores from a structure file and cryo-EM map:
qscore calculate \ --structure-path path/to/structure.pdb \ --volume-path path/to/map.mrc \ --output-path output.csv
The structure file can be PDB or mmCIF. The volume file should be an MRC file.
--output-path is optional and defaults to output.csv.
1 - Pintilie, G., Zhang, K., Su, Z. et al. Measurement of atom resolvability in cryo-EM maps with Q-scores. Nat Methods 17, 328–334 (2020). https://doi.org/10.1038/s41592-020-0731-1