File size: 3,997 Bytes
23a728e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 | # Copyright 2026 Emile de Bruyn
# SPDX-License-Identifier: Apache-2.0
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):
# clusters: List[List[str]] of member basenames
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)
# all instances in one cluster -> ratio 1.0 >= 0.8
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
# conserved by Foldseek
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)
# split into two clusters -> ratio 0.5 < 0.8
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
# Should not be marked conserved under Foldseek ratio threshold
assert stats.loc[0, "motif_entry"] == "E1"
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