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0613483 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 | """Supplementary figure: representative D-peptide backbones from distinct
surface-site clusters.
For each design target (MDM2, PDL1, IL23R) the two largest surface-site
clusters are selected and one representative backbone (the cluster centroid)
is rendered as a 3D Cα trace: receptor in light grey, designed peptide in the
target colour. This directly addresses the reviewer's request for
"representative structures from distinct surface-site clusters".
Rendering is done entirely in matplotlib (mplot3d) — no PyMOL dependency — and
the figure follows the same publication style (PALETTE, apply_nature_style,
save_pub_fig → PDF+PNG+SVG) as the rest of the SI.
"""
from __future__ import annotations
from pathlib import Path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # noqa: F401 (registers 3d projection)
from plot_ablation_dl_figures import (
PALETTE,
apply_nature_style,
save_pub_fig,
add_panel_label,
)
ROOT = Path(__file__).resolve().parents[2]
BENCH = ROOT / "benchmark"
FIGDATA = BENCH / "figure_data"
TARGETS = ["MDM2", "PDL1", "IL23R"]
TARGET_COLORS = {
"MDM2": PALETTE["blue_main"],
"PDL1": PALETTE["blue_secondary"],
"IL23R": PALETTE["orange"],
}
N_REPS_PER_TARGET = 2 # pick the N largest clusters per target
# --------------------------------------------------------------------------- #
# PDB parsing #
# --------------------------------------------------------------------------- #
def parse_ca_coords(pdb_path: Path) -> dict[str, np.ndarray]:
"""Return {chain_id: (N, 3) array of Cα coordinates} from a PDB file."""
coords: dict[str, list[list[float]]] = {}
with open(pdb_path) as f:
for line in f:
if not line.startswith(("ATOM", "HETATM")):
continue
atom = line[13:15].strip()
if atom != "CA":
continue
chain = line[21]
x = float(line[30:38])
y = float(line[38:46])
z = float(line[46:54])
coords.setdefault(chain, []).append([x, y, z])
return {ch: np.array(v) for ch, v in coords.items()}
# --------------------------------------------------------------------------- #
# Structure selection #
# --------------------------------------------------------------------------- #
def select_representatives() -> pd.DataFrame:
"""Pick the N largest-cluster representatives per target.
Reads the per-backbone cluster assignment CSV (produced by
plot_si_design_landscape.py) and the cluster-representative table, then
keeps the representatives belonging to the N largest clusters per target.
"""
clusters = pd.read_csv(FIGDATA / "fig_si_design_landscape_clusters.csv")
reps = pd.read_csv(
FIGDATA / "fig_si_design_landscape_cluster_representatives.csv")
# cluster sizes per target
sizes = (clusters.groupby(["target", "cluster"])
.size().reset_index(name="size"))
selected = []
for target in TARGETS:
tsizes = sizes[sizes["target"] == target].sort_values(
"size", ascending=False)
top_clusters = tsizes.head(N_REPS_PER_TARGET)["cluster"].tolist()
for cl in top_clusters:
row = reps[(reps["target"] == target)
& (reps["cluster"] == cl)].iloc[0]
sz = int(tsizes[tsizes["cluster"] == cl]["size"].iloc[0])
selected.append({
"target": target,
"cluster": int(cl),
"cluster_size": sz,
"pose": int(row["pose"]),
"length": int(row["length"]),
"sequence": row["sequence"],
"score": row["score"],
"pose_pdb": str(ROOT / row["pose_pdb"]),
})
return pd.DataFrame(selected)
# --------------------------------------------------------------------------- #
# 3D rendering #
# --------------------------------------------------------------------------- #
def plot_structure_3d(ax, pdb_path: Path, target: str, info: dict):
"""Render receptor (grey Cα trace) + peptide (coloured Cα trace) in 3D."""
coords = parse_ca_coords(pdb_path)
# Chain A = receptor, chain B = peptide (convention from the pipeline).
rec_chain = "A" if "A" in coords else sorted(coords.keys())[0]
pep_chain = "B" if "B" in coords else sorted(coords.keys())[-1]
rec = coords[rec_chain]
pep = coords[pep_chain]
# Receptor: thin, semi-transparent grey Cα trace.
ax.plot(rec[:, 0], rec[:, 1], rec[:, 2],
color=PALETTE["neutral_light"], linewidth=0.6, alpha=0.35,
zorder=1)
ax.scatter(rec[:, 0], rec[:, 1], rec[:, 2],
s=1.5, color=PALETTE["neutral_light"], alpha=0.25,
depthshade=True, zorder=1)
# Peptide: thick coloured Cα trace with residue markers.
color = TARGET_COLORS[target]
ax.plot(pep[:, 0], pep[:, 1], pep[:, 2],
color=color, linewidth=2.2, alpha=0.95, zorder=3)
ax.scatter(pep[:, 0], pep[:, 1], pep[:, 2],
s=22, color=color, edgecolors="white", linewidths=0.3,
depthshade=True, zorder=4)
# Mark N- and C-termini of the peptide.
ax.scatter(pep[0, 0], pep[0, 1], pep[0, 2], s=40, marker="v",
color=color, edgecolors="black", linewidths=0.4, zorder=5)
ax.scatter(pep[-1, 0], pep[-1, 1], pep[-1, 2], s=40, marker="^",
color=color, edgecolors="black", linewidths=0.4, zorder=5)
# Centre the view on the peptide centroid for a consistent framing.
centre = pep.mean(axis=0)
span = max(rec.ptp(axis=0).max(), 20.0) * 0.55
ax.set_xlim(centre[0] - span, centre[0] + span)
ax.set_ylim(centre[1] - span, centre[1] + span)
ax.set_zlim(centre[2] - span, centre[2] + span)
# Clean 3D axes: no ticks, light panes.
ax.set_xticks([])
ax.set_yticks([])
ax.set_zticks([])
for pane_axis in (ax.xaxis, ax.yaxis, ax.zaxis):
pane_axis.pane.set_facecolor((1, 1, 1, 0.0))
pane_axis.pane.set_edgecolor((0.9, 0.9, 0.9, 0.5))
pane_axis.line.set_color((0.8, 0.8, 0.8, 0.5))
ax.grid(False)
# Consistent viewing angle.
ax.view_init(elev=18, azim=-55)
# Title with target, cluster, pose, length.
ax.set_title(
f"{target} cluster {info['cluster']} (n={info['cluster_size']})\n"
f"pose {info['pose']} · {info['length']} aa",
fontsize=7, color=PALETTE["neutral_dark"], pad=2)
# --------------------------------------------------------------------------- #
# Main #
# --------------------------------------------------------------------------- #
def main():
apply_nature_style(font_size=8)
reps = select_representatives()
n_rows = N_REPS_PER_TARGET # 2 representatives per target
n_cols = len(TARGETS) # 3 targets
fig, axes = plt.subplots(n_rows, n_cols,
figsize=(8.4, 5.8),
subplot_kw={"projection": "3d"})
axes = np.atleast_2d(axes)
for row in range(n_rows):
for col, target in enumerate(TARGETS):
ax = axes[row, col]
idx = col * n_rows + row # column-major: target groups by column
info = reps.iloc[idx]
plot_structure_3d(ax, Path(info["pose_pdb"]),
target, info)
# Column header on the first row only.
if row == 0:
ax.text2D(0.5, 1.08, target, transform=ax.transAxes,
ha="center", va="bottom", fontsize=8.5,
fontweight="bold",
color=TARGET_COLORS[target])
# Panel label (use text2D because Axes3D.text requires 3D coordinates).
# Place it well above the 3D box and outside the subplot so tight bbox
# captures it cleanly without clipping.
axes[0, 0].text2D(0.0, 1.18, "a", transform=axes[0, 0].transAxes,
fontsize=10, fontweight="bold", color="black",
ha="left", va="bottom")
# Legend (receptor vs peptide vs termini) — placed below.
from matplotlib.lines import Line2D
legend_handles = [
Line2D([0], [0], color=PALETTE["neutral_light"], lw=1.2,
alpha=0.6, label="receptor (Cα trace)"),
Line2D([0], [0], color=PALETTE["blue_main"], lw=2.2,
label="designed D-peptide"),
Line2D([0], [0], marker="v", color="w", markerfacecolor=PALETTE["blue_main"],
markersize=7, label="N-terminus"),
Line2D([0], [0], marker="^", color="w", markerfacecolor=PALETTE["blue_main"],
markersize=7, label="C-terminus"),
]
fig.legend(handles=legend_handles, loc="lower center",
ncol=4, fontsize=7, bbox_to_anchor=(0.5, -0.01),
handletextpad=0.4, columnspacing=1.2)
fig.subplots_adjust(left=-0.02, right=1.02, top=0.92, bottom=0.06,
wspace=0.02, hspace=0.15)
save_pub_fig(fig, "fig_si_representative_structures")
# Export the selection table.
reps_out = reps.drop(columns=["pose_pdb"]).copy()
reps_out["pose_pdb"] = reps["pose_pdb"].str.replace(
str(ROOT) + "/", "", regex=False)
reps_out.to_csv(FIGDATA / "fig_si_representative_structures.csv",
index=False)
# Console summary.
print("\n=== Representative structures (selected) ===")
print(reps_out[["target", "cluster", "cluster_size", "pose", "length",
"sequence", "score"]].to_string(index=False))
if __name__ == "__main__":
main()
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