| |
| |
|
|
| import numpy as np |
| import pandas as pd |
| import xarray as xr |
|
|
| from motifbench_prosite.structure_analysis import RMSDAnalysisConfig, compute_motif_stats |
|
|
|
|
| def _make_dummy_dataset(): |
| coords = np.array( |
| [ |
| [[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [2.0, 0.0, 0.0]], |
| [[0.0, 0.0, 0.0], [1.1, 0.0, 0.0], [2.1, 0.0, 0.0]], |
| ], |
| dtype=np.float32, |
| ) |
| ds = xr.Dataset( |
| { |
| "motif_entry": ("instance", ["E1", "E1"]), |
| "pattern": ("instance", ["G", "G"]), |
| "pdb": ("instance", ["TEST", "TEST"]), |
| "chain": ("instance", ["A", "A"]), |
| "start": ("instance", [1, 1]), |
| "end": ("instance", [3, 3]), |
| "length": ("instance", [3, 3]), |
| "motif_len": ("instance", [3, 3]), |
| "coords": ( |
| ("instance", "res_idx", "xyz"), |
| coords, |
| ), |
| }, |
| coords={ |
| "instance": [0, 1], |
| "res_idx": [0, 1, 2], |
| "xyz": ["x", "y", "z"], |
| }, |
| ) |
| return ds |
|
|
|
|
| def test_compute_motif_stats_basic(): |
| ds = _make_dummy_dataset() |
| cfg = RMSDAnalysisConfig(min_instances=1, rmsd_threshold=5.0, cluster_threshold=5.0) |
|
|
| stats = compute_motif_stats(ds, cfg, max_workers=1) |
|
|
| assert not stats.empty |
| assert set(["motif_entry", "n_instances", "common_len", "median_pairwise_rmsd"]).issubset(stats.columns) |
| assert stats.loc[0, "motif_entry"] == "E1" |
| assert stats.loc[0, "n_instances"] == 2 |
| assert stats.loc[0, "common_len"] == 3 |
| assert stats.loc[0, "median_pairwise_rmsd"] >= 0.0 |
|
|
|
|
| class _FakeFsCfg: |
| def __init__(self, conservation_ratio=0.8): |
| self.foldseek_conservation = True |
| self.foldseek_conservation_ratio = conservation_ratio |
| self.foldseek_conservation_workers = 1 |
|
|
|
|
| class _FakeFsRunner: |
| def __init__(self, clusters): |
| |
| self._clusters = clusters |
| self.cfg = type("Cfg", (), {"threads": 1}) |
|
|
| @property |
| def enabled(self): |
| return True |
|
|
| def easy_cluster(self, frag_pdb_dir, out_dir, threads_override=None): |
| out_dir.mkdir(parents=True, exist_ok=True) |
| tsv = out_dir / "db_cluster.tsv" |
| lines = [] |
| for idx, members in enumerate(self._clusters): |
| for m in members: |
| lines.append(f"{idx}\t{m}\n") |
| tsv.write_text("".join(lines)) |
| return tsv |
|
|
|
|
| def test_compute_motif_stats_foldseek_positive(tmp_path): |
| ds = _make_dummy_dataset() |
| cfg = RMSDAnalysisConfig(min_instances=1, rmsd_threshold=5.0, cluster_threshold=5.0) |
|
|
| |
| runner = _FakeFsRunner(clusters=[["E1_0.pdb", "E1_1.pdb"]]) |
| fs_cfg = _FakeFsCfg(conservation_ratio=0.8) |
|
|
| stats = compute_motif_stats( |
| ds, |
| cfg, |
| max_workers=1, |
| foldseek_runner=runner, |
| foldseek_cfg=fs_cfg, |
| tmpdir=tmp_path, |
| ) |
|
|
| assert "foldseek_max_cluster_ratio" in stats.columns |
| assert stats.loc[0, "foldseek_max_cluster_ratio"] == 1.0 |
| |
| assert stats.loc[0, "motif_entry"] == "E1" |
|
|
|
|
| def test_compute_motif_stats_foldseek_negative(tmp_path): |
| ds = _make_dummy_dataset() |
| cfg = RMSDAnalysisConfig(min_instances=1, rmsd_threshold=0.1, cluster_threshold=5.0) |
|
|
| |
| runner = _FakeFsRunner(clusters=[["E1_0.pdb"], ["E1_1.pdb"]]) |
| fs_cfg = _FakeFsCfg(conservation_ratio=0.8) |
|
|
| stats = compute_motif_stats( |
| ds, |
| cfg, |
| max_workers=1, |
| foldseek_runner=runner, |
| foldseek_cfg=fs_cfg, |
| tmpdir=tmp_path, |
| ) |
|
|
| assert "foldseek_max_cluster_ratio" in stats.columns |
| assert stats.loc[0, "foldseek_max_cluster_ratio"] == 0.5 |
| |
| assert stats.loc[0, "motif_entry"] == "E1" |
|
|
