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, } 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) assert len(result) == 14 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)