jqscore

JAX reimplementation of Q-score — a per-atom cryo-EM map-to-model fit metric.

Q-score measures how well each atom in an atomic model fits into a cryo-EM density map by comparing the observed radial density profile around each atom to a theoretical Gaussian profile (Pearson correlation).

Pintilie et al., Nature Methods 2020. Reference implementation: gregdp/mapq

Installation

pip install jqscore            # CPU
pip install "jqscore[gpu]"     # NVIDIA GPU: CUDA 13 build of JAX, driver ≥ 580

With an older driver (≥ 525), use the CUDA 12 build of JAX: pip install jqscore "jax[cuda12]". The development version: pip install git+https://github.com/aozalevsky/jqscore.

Python API

import jqscore

# From file paths (PDB/mmCIF + MRC/CCP4)
result = jqscore.run("structure.cif", "map.mrc")

# Access results
print(result.atom_qscores)          # per-atom Q-scores (H: nearest heavy atom's in the residue)
print(jqscore.chain_qscores(result))    # per-chain averages
print(jqscore.residue_qscores(result))  # per-residue breakdown

# Lower-level: supply parsed atoms and map arrays
atoms = jqscore.read_cif("structure.cif")
data, origin, voxel_size = jqscore.read_mrc("map.mrc")
result = jqscore.compute_qscores(atoms, data, origin, voxel_size, sigma=0.4)

# A document already parsed with gemmi (import gemmi): one Q-score per _atom_site row,
# written back into the same document (no second read, no per-atom objects)
doc = gemmi.cif.read("structure.cif")
q = jqscore.block_qscores(doc[0], data, origin, voxel_size, sigma=0.4)
jqscore.add_qscore_column(doc[0], q)
doc.write_file("structure.cif__Q__map.mrc.cif")

# Or atoms as arrays: positions, hydrogen flags and residue codes
q, min_d, max_d = jqscore.qscores_from_arrays(coords, heavy, residue, data, origin, voxel_size)

Computation modes

# Fast mode
result = jqscore.run("structure.cif", "map.mrc", use_retry=False)

# Check JAX backend
print(jqscore.backend())  # 'cpu', 'gpu', or 'tpu'

Atoms are processed in batches (batch_size, default 32768); the map stays on the GPU throughout. Lower batch_size if the GPU runs out of memory.

On an RTX A4000, jqscore 36ZA.cif emd_76130.map (11.2 M atoms, 2888 chains, 800³ map) takes 1 min 40 s, over half of it reading the 1.2 GB mmCIF. jqscore-cmd, which reads the mmCIF once and writes the annotated copy, takes 1 min 15 s (14 GB peak memory).

CLI

jqscore structure.cif map.mrc [options]

options:
  --sigma FLOAT       Gaussian sigma in Å (default 0.4; use 0.6 for older datasets)
  --num-pts INT       Sample points per shell (default 8)
  --to-rad FLOAT      Max radial distance in Å
  --d-rad FLOAT       Radial shell spacing in Å
  --no-retry          Use fast fixed-budget mode
  --max-retries INT   Max retry attempts per shell (default 50)
  --n-gen INT         Sphere-point budget for --no-retry mode
  --no-mapq-compat    Interpolate the map directly instead of replicating
                      MapQ's trimmed-map resampling
  --batch-size INT    Atoms per batch (default 32768; lower for small GPUs)
  --verbose           Print progress and per-atom Q-scores
  --output FILE       Write per-residue TSV to file (default: stdout summary)

MapQ-compatibility mode

The jqscore-cmd entry point matches the argument style of the original MapQ Chimera plugin’s mapq_cmd.py, so tools like the VA validation package can use jqscore as a drop-in replacement without changes.

How to run:

jqscore-cmd <chimera_root> map=<mapfile> cif=<cif1> [cif=<cif2> ...] [np=<n>] [res=<resolution>] [sigma=<sigma>]

• Required: map=<path> — path to the MRC/CCP4 density map.
• Required: cif=<path> — path to an mmCIF structure; repeat for multiple models.
• Optional: first positional argument is the Chimera root path (accepted but unused; jqscore does not require Chimera).

All parameters other than the map path, structure path(s), and sigma are accepted for compatibility but ignored (e.g. np=, res=, Chimera root). The optional sigma=<float> sets the Gaussian width in Å (default 0.4). Output CIFs with a Q-score column are written to the current working directory as {cif_basename}__Q__{map_basename}.cif, matching the naming expected by VA.

Container

container/jqscore.def builds an Apptainer/Singularity image with the CUDA build of JAX (the host needs only an NVIDIA driver, ≥ 580 for CUDA 13). From the repository root:

apptainer build --fakeroot jqscore.sif container/jqscore.def
# driver older than 580: --build-arg JAX="jax[cuda12]==0.9.2"

apptainer run --nv jqscore.sif structure.cif map.mrc
apptainer exec --nv jqscore.sif jqscore-cmd /opt/chimera map=map.mrc cif=structure.cif

The build ends by running the test suite on the CPU. apptainer run-help jqscore.sif lists the commands.

Benchmarking against MapQ

The included benchmark script (needs pip install "jqscore[benchmark]" for matplotlib) downloads real PDB+EMDB structures, runs both the original MapQ (via Singularity) and jqscore, and produces per-atom scatter plots, a summary comparison, and a markdown report.

# Run with default structures (7 entries):
python -m jqscore.benchmark

# Run on specific PDB IDs:
python -m jqscore.benchmark 9XZK 9UX2

# Custom output directory:
python -m jqscore.benchmark -o my_benchmark

# After changing jqscore: re-run it only, keeping MapQ results and times
python -m jqscore.benchmark --rerun-jqscore

The MapQ reference is invoked via the MAPQ_CMD environment variable (expects a Singularity container by default). A full benchmark run with all 7 default structures takes approximately 30 minutes.

Results are saved to jqscore_mapq_benchmark/ (plots, output CIFs, and README.md); the latest run is in docs/jqscore_vs_mapq/. Of the 750,165 non-H atoms of its 7 entries, 15 differ from MapQ by more than 1e-3: 5 in 3J7O where the density around the atom is flat (MapQ gives ~0.1, jqscore 0), and 10 with a sample point within ~1e-7 Å of the exclusion cutoff, a tie that MapQ also breaks differently from a float64 reference in 3 of the 10. See spec/benchmark.md for the full specification.

License

MIT