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new tests
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@clami66
clami66 committed Mar 25, 2024
1 parent 48fc838 commit a084434
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Showing 4 changed files with 10 additions and 198 deletions.
Binary file added examples/6qwn-assembly1.cif.gz
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Binary file added examples/6qwn-assembly2.cif.gz
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15 changes: 10 additions & 5 deletions run_test.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -9,15 +9,10 @@ else
binary="DockQ"
fi

# Test that cython version behaves the same as nocython
$binary examples/1A2K_r_l_b.model.pdb examples/1A2K_r_l_b.pdb > test
diff test testdata/1A2K.dockq
python src/DockQ/DockQ.py examples/1A2K_r_l_b.model.pdb examples/1A2K_r_l_b.pdb > test
diff test testdata/1A2K.dockq
$binary examples/1A2K_r_l_b.model.pdb examples/1A2K_r_l_b.pdb --no_align > test
diff test testdata/1A2K.dockq
python src/DockQ/DockQ.py examples/1A2K_r_l_b.model.pdb examples/1A2K_r_l_b.pdb --no_align > test
diff test testdata/1A2K.dockq

# Multiple interfaces
$binary examples/dimer_dimer.model.pdb examples/dimer_dimer.pdb --short > test
Expand All @@ -40,3 +35,13 @@ $binary examples/1EXB_r_l_b.model.pdb examples/1EXB_r_l_b.pdb --mapping DH:AE >
diff test testdata/1EXB_DH.AE.dockq
$binary examples/1EXB_r_l_b.model.pdb examples/1EXB.cif.gz --mapping DH:AE > test
diff test testdata/1EXB_DH.AE_cif.dockq

# Peptide measures
$binary examples/6qwn-assembly1.cif.gz examples/6qwn-assembly2.cif.gz --capri_peptide > test
diff test testdata/6q2n_peptide.dockq

# Test that cython version behaves the same as nocython
python src/DockQ/DockQ.py examples/1A2K_r_l_b.model.pdb examples/1A2K_r_l_b.pdb > test
diff test testdata/1A2K.dockq
python src/DockQ/DockQ.py examples/1A2K_r_l_b.model.pdb examples/1A2K_r_l_b.pdb --no_align > test
diff test testdata/1A2K.dockq
193 changes: 0 additions & 193 deletions src/DockQ/DockQ.py
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Original file line number Diff line number Diff line change
Expand Up @@ -120,24 +120,6 @@ def get_aligned_residues(chainA, chainB, alignment):
return tuple(aligned_resA), tuple(aligned_resB)


@lru_cache
def list_atoms_per_residue2(chain, what):
n_atoms_per_residue = []
atoms_ids = []
for residue in chain:
# important to remove duplicate atoms (e.g. alternates) at this stage
atom_ids = tuple([a.id for a in residue.get_unpacked_list()])
atoms_ids.append(atom_ids)
n_atoms_per_residue.append(len(set(atom_ids)))
return tuple(n_atoms_per_residue), tuple(atoms_ids)


@lru_cache
def get_atoms(chain, what):
atoms = np.array([atom.get_coord() for res in chain for atom in res.get_atoms()])
return atoms


@lru_cache
def get_residue_distances(chain1, chain2, what, all_atom=True):
if all_atom:
Expand Down Expand Up @@ -174,148 +156,6 @@ def get_residue_distances(chain1, chain2, what, all_atom=True):
return model_res_distances


@lru_cache
def get_aligned_atoms(n_atoms_per_res_chainA, n_atoms_per_res_chainB, alignment):

if alignment[0] == alignment[2]: # no need to align
return (
np.ones(sum(list(n_atoms_per_res_chainA))).astype(bool),
np.ones(sum(list(n_atoms_per_res_chainB))).astype(bool),
[i for i in range(len(alignment[0]))],
[i for i in range(len(alignment[1]))],
)
atom_index_A = np.zeros(sum(list(n_atoms_per_res_chainA))).astype(bool)
atom_index_B = np.zeros(sum(list(n_atoms_per_res_chainB))).astype(bool)
res_index_A = []
res_index_B = []

n_atoms_per_res_chainA = iter(n_atoms_per_res_chainA)
n_atoms_per_res_chainB = iter(n_atoms_per_res_chainB)
countA = iA = 0
countB = iB = 0

for A, match, B in zip(*alignment):
if A != "-":
nA = next(n_atoms_per_res_chainA)
countA += nA
iA += 1
if B != "-":
nB = next(n_atoms_per_res_chainB)
countB += nB
iB += 1

if match == "|":
atom_index_A[countA - nA : countA] = 1
atom_index_B[countB - nB : countB] = 1
res_index_A.append(iA - 1)
res_index_B.append(iB - 1)

return atom_index_A, atom_index_B, res_index_A, res_index_B


class NpWrapper:
def __init__(self, x: np.array) -> None:
self.values = x
# here you can use your own hashing function
self.h = hashlib.sha224(x.view()).hexdigest()

def __hash__(self) -> int:
return hash(self.h)

def __eq__(self, __value: object) -> bool:
return __value.h == self.h


def dist_memoizer(get_atom_distances):
@lru_cache()
def cached_wrapper(np1, np2, n_atoms, what):
return get_atom_distances(np1.values, np2.values, n_atoms, what)

@wraps(get_atom_distances)
def wrapper(x: np.array, y: np.array, n_atoms, what):
np1 = NpWrapper(x)
np2 = NpWrapper(y)
return cached_wrapper(np1, np2, n_atoms, what)

return wrapper


@dist_memoizer
def get_atom_distances(chain1_atoms, chain2_atoms, n_atoms_per_res, what):
n_atoms_per_res_chain1, n_atoms_per_res_chain2 = n_atoms_per_res

model_res_distances = residue_distances(
chain1_atoms,
chain2_atoms,
np.array(n_atoms_per_res_chain1).astype(int),
np.array(n_atoms_per_res_chain2).astype(int),
)
return model_res_distances


def sup_memoizer(superimpose):
@lru_cache()
def cached_wrapper(np1, np2):
return superimpose(np1.values, np2.values)

@wraps(superimpose)
def wrapper(x: np.array, y: np.array):
np1 = NpWrapper(x)
np2 = NpWrapper(y)
return cached_wrapper(np1, np2)

return wrapper


@sup_memoizer
def superimpose(from_atoms, to_atoms):
super_imposer = SVDSuperimposer()
# Set to align on receptor
super_imposer.set(to_atoms, from_atoms)
super_imposer.run()
rot, tran = super_imposer.get_rotran()
return rot, tran, super_imposer


@lru_cache
def filter_atoms(model_info, native_info, filter=("CA", "C", "N", "O", "P")):
model_bools = []
native_bools = []
if "CA" in filter: # backbone filter
model_atom_ids = model_info
native_atom_ids = native_info

n_atoms_per_res = []
for model_ids, native_ids in zip(model_atom_ids, native_atom_ids):
model_select = [
1 if atom in filter and atom in native_ids else 0
for atom in list(dict.fromkeys(model_ids))
]
native_select = [
1 if atom in filter and atom in model_ids else 0
for atom in list(dict.fromkeys(native_ids))
]
model_bools.extend(model_select)
native_bools.extend(native_select)
n_atoms_per_res.append(sum(native_select))
if sum(model_select) != sum(native_select):
print(model_ids, model_select)
print(native_ids, native_select)
print("ERROR!")
else: # filter by index
n_atoms_per_res = model_info

for i, n_atoms in enumerate(n_atoms_per_res):
if i in filter:
model_bools.extend([1 for i in range(n_atoms)])
else:
model_bools.extend([0 for i in range(n_atoms)])
native_bools = model_bools
model_bools = np.array(model_bools).astype(bool)
native_bools = np.array(native_bools).astype(bool)
return model_bools, native_bools, tuple(n_atoms_per_res)


# @profile
def calc_DockQ(
sample_chains,
Expand Down Expand Up @@ -544,39 +384,6 @@ def format_alignment(aln):
return alignment


def remove_hetatms(model):
chains = [chain.id for chain in model.get_chains()]
residues_to_delete = []

for chain in chains:
residues = model[chain].get_residues()

for res in residues:
if res.id[0] != " ":
residues_to_delete.append(res.get_full_id())
for _, _, chain, res in residues_to_delete:
model[chain].detach_child(res)

for chain in chains:
if not model[chain]:
model.detach_child(chain)


def remove_h(model):
chains = [chain.id for chain in model.get_chains()]
atoms_to_delete = []

for chain in chains:
residues = model[chain].get_residues()
for residue in residues:
for atom in residue.get_atoms():
if atom.element == "H":
atoms_to_delete.append(atom.get_full_id())

for _, _, chain, res, atom in atoms_to_delete:
model[chain][res].detach_child(atom[0])


@lru_cache
def list_atoms_per_residue(chain, what):
n_atoms_per_residue = []
Expand Down

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