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Add an optional ligand chain index to run_on_chains and calc_DockQ. #67

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Add an optional ligand chain index to run_on_chains and calc_DockQ. #67

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14 changes: 11 additions & 3 deletions src/DockQ/DockQ.py
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Original file line number Diff line number Diff line change
Expand Up @@ -275,6 +275,7 @@ def calc_DockQ(
alignments,
capri_peptide=False,
low_memory=False,
ligand_chain_idx=None,
):

fnat_threshold = FNAT_THRESHOLD if not capri_peptide else FNAT_THRESHOLD_PEPTIDE
Expand Down Expand Up @@ -356,22 +357,27 @@ def calc_DockQ(
# assign which chains constitute the receptor, ligand
ref_group1_size = len(ref_chains[0])
ref_group2_size = len(ref_chains[1])
if ligand_chain_idx is None:
ligand_last = ref_group1_size > ref_group2_size
else:
ligand_last = (ligand_chain_idx == 1)
receptor_chains = (
(aligned_ref_1, aligned_sample_1)
if ref_group1_size > ref_group2_size
if ligand_last
else (aligned_ref_2, aligned_sample_2)
)
ligand_chains = (
(aligned_ref_1, aligned_sample_1)
if ref_group1_size <= ref_group2_size
if not ligand_last
else (aligned_ref_2, aligned_sample_2)
)
class1, class2 = (
("receptor", "ligand")
if ref_group1_size > ref_group2_size
if ligand_last
else ("ligand", "receptor")
)


receptor_atoms_native, receptor_atoms_sample = subset_atoms(
receptor_chains[0],
receptor_chains[1],
Expand Down Expand Up @@ -553,6 +559,7 @@ def run_on_chains(
capri_peptide=False,
small_molecule=True,
low_memory=False,
ligand_chain_idx=None,
):
# realign each model chain against the corresponding native chain
alignments = []
Expand All @@ -572,6 +579,7 @@ def run_on_chains(
alignments=tuple(alignments),
capri_peptide=capri_peptide,
low_memory=low_memory,
ligand_chain_idx=ligand_chain_idx,
)
else:
info = calc_sym_corrected_lrmsd(
Expand Down