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Correct the judgment of hydrogen bonds #370

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Correct the judgment of hydrogen bonds #370

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fb1d42d
Update distance.py
Linsastar Mar 2, 2024
4a9320b
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Mar 2, 2024
26c90bc
Fixed filtering of atoms that may form hydrogen bonds
Linsastar Mar 4, 2024
b717cf5
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Mar 4, 2024
d064829
Modified the code for judging hydrogen bonds
Linsastar Mar 4, 2024
6d3f706
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Mar 4, 2024
c53e688
Update distance.py
Linsastar Mar 4, 2024
a5a4a6a
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Mar 4, 2024
b244c37
Corrected hydrogen bond determination, now hydrogen bonds on the main…
Linsastar Mar 16, 2024
c8d591c
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Mar 16, 2024
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78 changes: 62 additions & 16 deletions graphein/protein/edges/distance.py
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Original file line number Diff line number Diff line change
Expand Up @@ -336,27 +336,58 @@ def add_disulfide_interactions(
def add_hydrogen_bond_interactions(
G: nx.Graph, rgroup_df: Optional[pd.DataFrame] = None
):
"""Add all hydrogen-bond interactions."""
# For these atoms, find those that are within 3.5A of one another.
if rgroup_df is None:
rgroup_df = G.graph["rgroup_df"]
rgroup_df = filter_dataframe(rgroup_df, "node_id", list(G.nodes()), True)
rgroup_df = G.graph["raw_pdb_df"]
HBOND_ATOMS = [
"ND", # histidine and asparagine
"NE", # glutamate, tryptophan, arginine, histidine
"NH", # arginine
"ND1", # histidine
"ND2", # asparagine
"NE", # arginine
"NE1", # tryptophan
"NE2", # glutamate, histidine
"NH1", # arginine
"NH2", # arginine
"NZ", # lysine
"OD1", # asparagine, aspartic
"OD2", # aspartic
"OE1", # glutamate
"OE2", # glutamate
"OG", # serine
"OG1", # threonine
"OH", # tyrosine, threonine, serine
"SD", # methionine
"SG", # cysteine
"N", # backbone
"O", # backbone
]
DONORS_ATOM: List[str] = [
"ND2", # asparagine
"NE", # arginine
"NE1", # tryptophan
"NE2", # glutamate, histidine
"NH1", # arginine
"NH2", # arginine
"NZ", # lysine
"OD1",
"OD2",
"OE",
"OG",
"OH",
"SD", # cysteine
"OH", # tyrosine, threonine, serine
"SD", # methionine,
"SG", # methionine
"N",
"O",
"OG", # serine
"OG1", # threonine
"N", # backbone
]
ACCEPTORS_ATOM: List[str] = [
"OD1", # asparagine, aspartic
"OD2", # aspartic
"OE1", # glutamate
"OE2", # glutamate
"O", # backbone
"ND1", # histidine
]
"""Add all hydrogen-bond interactions."""
# For these atoms, find those that are within 3.5A of one another.
# if rgroup_df is None:
# rgroup_df = G.graph["rgroup_df"]
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@a-r-j a-r-j Mar 25, 2024

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What is the purpose of not using the supplied rgroup_df?

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@Linsastar Linsastar Mar 28, 2024

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Yes, you're right. I commented out the code like that just to facilitate debugging and determine the hydrogen bonds on the main chain.

rgroup_df = filter_dataframe(rgroup_df, "node_id", list(G.nodes()), True)
hbond_df = filter_dataframe(rgroup_df, "atom_name", HBOND_ATOMS, True)

if len(hbond_df.index) > 0:
distmat = compute_distmat(hbond_df)
interacting_atoms = get_interacting_atoms(3.5, distmat)
Expand All @@ -371,6 +402,21 @@ def add_hydrogen_bond_interactions(
distmat = compute_distmat(hbond_df)
interacting_atoms = get_interacting_atoms(4.0, distmat)
add_interacting_resis(G, interacting_atoms, hbond_df, ["hbond"])
for r1, r2 in get_edges_by_bond_type(G, "hbond"):
# print(G.nodes.data())
condition1 = (
G.nodes[r1]["atom_type"] in ACCEPTORS_ATOM
and G.nodes[r2]["atom_type"] in DONORS_ATOM
)
condition2 = (
G.nodes[r2]["atom_type"] in ACCEPTORS_ATOM
and G.nodes[r1]["atom_type"] in DONORS_ATOM
)
is_ionic = condition1 or condition2
if not is_ionic:
G.edges[r1, r2]["kind"].remove("hbond")
if len(G.edges[r1, r2]["kind"]) == 0:
G.remove_edge(r1, r2)


def add_ionic_interactions(
Expand Down