Configure PRODIGY pipeline for WES execution with S3 and Harbor

tests/test_prodigy.py

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+import json
+import tarfile
+import tempfile
+from io import BufferedReader, TextIOWrapper
+from os.path import basename, splitext
+from pathlib import Path
+
+import pytest
+from Bio.PDB.Model import Model
+from Bio.PDB.PDBParser import PDBParser
+from Bio.PDB.Residue import Residue
+from Bio.PDB.Structure import Structure
+
+from prodigy_prot.modules.parsers import validate_structure
+from prodigy_prot.modules.prodigy import (
+    Prodigy,
+    analyse_contacts,
+    analyse_nis,
+    calculate_ic,
+)
+
+from . import TEST_DATA
+
+
+@pytest.fixture
+def input_model():
+    input_f = Path(TEST_DATA, "2oob.pdb")
+    parser = PDBParser()
+    structure = parser.get_structure(input_f.stem, input_f)
+    assert isinstance(structure, Structure)
+    return structure.child_list[0]
+
+
+@pytest.fixture
+def compressed_dataset_f():
+    return Path(TEST_DATA, "dataset.tgz")
+
+
+@pytest.fixture
+def expected_dataset_json():
+    return Path(TEST_DATA, "dataset.json")
+
+
+@pytest.fixture
+def prodigy_class(input_model):
+    yield Prodigy(input_model)
+
+
+def test_calculate_ic(input_model):
+
+    result = calculate_ic(model=input_model, d_cutoff=5.5)
+
+    assert len(result) == 78
+
+    first_hit: tuple[Residue, Residue] = result[0]
+
+    assert first_hit[0].get_resname() == "ASN"
+    assert first_hit[1].get_resname() == "LYS"
+
+
+def test_calculate_ic_with_selection(input_model):
+
+    result = calculate_ic(model=input_model, d_cutoff=5.5, selection={"A": 0, "B": 1})
+
+    assert len(result) == 78
+
+    first_hit: tuple[Residue, Residue] = result[0]
+
+    assert first_hit[0].get_resname() == "ASN"
+    assert first_hit[1].get_resname() == "LYS"
+
+
+def test_analyse_contacts(input_model):
+
+    res_a = input_model["A"][(" ", 931, " ")]
+    res_b = input_model["B"][(" ", 6, " ")]
+    contact = (res_a, res_b)
+
+    test_input = [contact]
+
+    result = analyse_contacts(test_input)
+
+    expected_output = {
+        "AA": 0.0,
+        "PP": 0.0,
+        "CC": 0.0,
+        "AP": 0.0,
+        "CP": 1.0,
+        "AC": 0.0,
+        "LL": 1.0,
+        "BL": 0.0,
+        "BB": 0.0
+    }
+    assert result == expected_output
+
+
+def test_analyse_nis():
+
+    test_input = {("B", "ARG", "72"): 0.9}
+    apolar, polar, charged = analyse_nis(test_input)
+
+    assert apolar == 0.0
+    assert polar == 100.0
+    assert charged == 0.0
+
+
+def test_prodigy_predict(prodigy_class):
+
+    prodigy_class.predict()
+
+    assert prodigy_class.nis_a == pytest.approx(35.5, abs=1.0)
+    assert prodigy_class.nis_c == pytest.approx(38.0, abs=1.0)
+    assert prodigy_class.ba_val == pytest.approx(-6.2, abs=1.0)
+
+    # This is the actual prediction
+    assert prodigy_class.kd_val == pytest.approx(2.7e-5, abs=1e-6)
+
+
+def test_prodigy_as_dict(prodigy_class):
+
+    result = prodigy_class.as_dict()
+
+    assert isinstance(result, dict)
+    # 14 'original' + 3 hydro + 1 %NIS
+    assert len(result) == 18
+
+
+def test_prodigy_print_prediction(prodigy_class):
+
+    outfile = tempfile.NamedTemporaryFile(delete=False)
+    assert Path(outfile.name).stat().st_size == 0
+
+    prodigy_class.print_prediction(outfile.name)
+    assert Path(outfile.name).stat().st_size != 0
+
+    Path(outfile.name).unlink()
+
+
+def test_prodigy_print_prediction_quiet(prodigy_class):
+
+    outfile = tempfile.NamedTemporaryFile(delete=False)
+    assert Path(outfile.name).stat().st_size == 0
+
+    prodigy_class.print_prediction(outfile.name, True)
+    assert Path(outfile.name).stat().st_size != 0
+
+    Path(outfile.name).unlink()
+
+
+def test_prodigy_print_contacts(input_model, prodigy_class):
+
+    res_a = input_model["A"][(" ", 931, " ")]
+    res_b = input_model["B"][(" ", 6, " ")]
+    prodigy_class.ic_network = [(res_a, res_b)]
+
+    outfile = tempfile.NamedTemporaryFile(delete=False)
+    assert Path(outfile.name).stat().st_size == 0
+
+    prodigy_class.print_contacts(outfile.name)
+    assert Path(outfile.name).stat().st_size != 0
+
+    Path(outfile.name).unlink()
+
+
+def test_print_pymol_script(input_model, prodigy_class):
+    res_a = input_model["A"][(" ", 931, " ")]
+    res_b = input_model["B"][(" ", 6, " ")]
+    prodigy_class.ic_network = [(res_a, res_b)]
+
+    outfile = tempfile.NamedTemporaryFile(delete=False)
+    assert Path(outfile.name).stat().st_size == 0
+
+    prodigy_class.print_pymol_script(outfile.name)
+    assert Path(outfile.name).stat().st_size != 0
+
+    Path(outfile.name).unlink()
+
+
+@pytest.mark.integration
+def test_dataset_prediction(compressed_dataset_f, expected_dataset_json):
+    """
+    Test method to compare prediction for 80 dataset cases with
+        expected values.
+    """
+    # load expected data from json
+    with open(expected_dataset_json) as fh:
+        expected_data = json.load(fh)
+
+    # load dataset PDBs
+    dataset = tarfile.open(compressed_dataset_f)
+    parser = PDBParser(QUIET=True)
+
+    keys_equal = ["AA", "PP", "CC", "AP", "CP", "AC"]
+    diffs = {"ba_val": [], "nis_a": [], "nis_c": []}
+
+    # run prodigy for each dataset in the PDB
+    for entry in dataset:
+        s_name, s_ext = splitext(basename(entry.name))
+
+        # skip system files in archive
+        if not s_name.isalnum() or s_ext != ".pdb":
+            continue
+
+        handle = dataset.extractfile(entry)
+
+        # Wrap filehandle to ensure string file handle in Python 3
+        handle = TextIOWrapper(BufferedReader(handle))  # type: ignore
+
+        parsed_structure = parser.get_structure(s_name, handle)
+        assert isinstance(parsed_structure, Structure)
+
+        models = validate_structure(parsed_structure, selection=["A", "B"])
+
+        # Test for structure object
+        # Check if it's a list and all elements are Model objects
+        assert isinstance(models, list) and all(
+            isinstance(item, Model) for item in models
+        )
+        # assert isinstance(s, list[Model])
+
+        #  run prediction and retrieve result dict
+        for m in models:
+            prod = Prodigy(m, selection=["A", "B"])
+            prod.predict()
+            results = prod.as_dict()
+
+            # check for equality of prdicted interface residues
+            for k in keys_equal:
+                observed_value = results[k]
+                expected_value = expected_data[s_name][k]
+                assert observed_value == pytest.approx(expected_value)
+
+            # check that NIS and binding afinity values are within 2% of
+            #  expected values and add diffs for summary
+            for k in diffs.keys():
+                delta = abs(results[k] / expected_data[s_name][k] - 1)
+                # assume a difference of less then 2%
+                assert delta == pytest.approx(0, abs=0.02)
+                diffs[k].append(delta)