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from __future__ import annotations
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from Bio.PDB import PDBParser
from Bio.PDB.vectors import calc_dihedral
from scipy.ndimage import gaussian_filter
ROOT = Path(__file__).resolve().parents[2]
BENCH = ROOT / "benchmark"
FIGDIR = BENCH / "figures"
FIGDATA = BENCH / "figure_data"
TARGET_DIRS = {
"PDL1": BENCH / "PDL1" / "D_peptide",
"MDM2": BENCH / "MDM2" / "D_peptide",
"IL23R": BENCH / "IL23R" / "D_peptide",
"TNFalpha": BENCH / "new_targets" / "designs" / "TNFalpha" / "D_peptide",
"CXCR2": BENCH / "new_targets" / "designs" / "CXCR2" / "D_peptide",
"CXCR4": BENCH / "new_targets" / "designs" / "CXCR4" / "D_peptide",
}
BENCHMARK_TARGETS = ["PDL1", "MDM2", "IL23R"]
PALETTE = {
"blue_main": "#0F4D92",
"orange": "#E28E2C",
"neutral_light": "#CFCECE",
"neutral_mid": "#767676",
"neutral_dark": "#4D4D4D",
}
def apply_nature_style(font_size: int = 7) -> None:
plt.rcParams["font.family"] = "sans-serif"
plt.rcParams["font.sans-serif"] = ["Arial", "DejaVu Sans", "Liberation Sans"]
plt.rcParams["svg.fonttype"] = "none"
plt.rcParams["pdf.fonttype"] = 42
plt.rcParams["axes.spines.right"] = False
plt.rcParams["axes.spines.top"] = False
plt.rcParams["axes.linewidth"] = 0.7
plt.rcParams["legend.frameon"] = False
plt.rcParams["font.size"] = font_size
plt.rcParams["axes.titlesize"] = font_size + 1
plt.rcParams["axes.labelsize"] = font_size + 1
plt.rcParams["xtick.labelsize"] = font_size
plt.rcParams["ytick.labelsize"] = font_size
plt.rcParams["legend.fontsize"] = font_size
plt.rcParams["xtick.direction"] = "out"
plt.rcParams["ytick.direction"] = "out"
plt.rcParams["xtick.major.width"] = 0.7
plt.rcParams["ytick.major.width"] = 0.7
plt.rcParams["xtick.major.size"] = 2.5
plt.rcParams["ytick.major.size"] = 2.5
def add_panel_label(ax, label: str) -> None:
ax.text(
-0.20,
1.10,
label,
transform=ax.transAxes,
fontsize=8,
fontweight="bold",
ha="left",
va="bottom",
)
def _binder_chain(structure):
chains = list(structure.get_chains())
if not chains:
raise ValueError("No chains found")
return sorted(chains, key=lambda chain: chain.id)[-1]
def _chain_phi_psi(path: Path) -> list[tuple[float | None, float | None]]:
parser = PDBParser(QUIET=True)
structure = parser.get_structure(path.stem, path)
chain = _binder_chain(structure)
residues = [
residue
for residue in chain
if residue.id[0] == " " and all(atom in residue for atom in ["N", "CA", "C"])
]
angles: list[tuple[float | None, float | None]] = []
for idx, residue in enumerate(residues):
phi = None
psi = None
if idx > 0:
prev = residues[idx - 1]
phi = calc_dihedral(
prev["C"].get_vector(),
residue["N"].get_vector(),
residue["CA"].get_vector(),
residue["C"].get_vector(),
)
if idx < len(residues) - 1:
nxt = residues[idx + 1]
psi = calc_dihedral(
residue["N"].get_vector(),
residue["CA"].get_vector(),
residue["C"].get_vector(),
nxt["N"].get_vector(),
)
angles.append((phi, psi))
return angles
def _paired_pose_files(base: Path) -> list[tuple[str, Path, Path]]:
pairs = []
for l_path in sorted(base.glob("len_*/Poses/Binder_L_pose_*.pdb")):
d_path = l_path.with_name(l_path.name.replace("Binder_L_pose_", "Binder_D_pose_"))
if d_path.exists():
pose_key = f"{l_path.parents[1].name}:{l_path.stem.split('_')[-1]}"
pairs.append((pose_key, l_path, d_path))
return pairs
def collect_phi_psi() -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
rama_rows = []
angle_rows = []
summary_rows = []
for target, base in TARGET_DIRS.items():
pairs = _paired_pose_files(base)
if not pairs:
raise FileNotFoundError(f"No paired L/D pose files found under {base}")
target_phi_errors: list[float] = []
target_psi_errors: list[float] = []
target_rama_rows = 0
for pose_key, l_path, d_path in pairs:
l_angles = _chain_phi_psi(l_path)
d_angles = _chain_phi_psi(d_path)
if len(l_angles) != len(d_angles):
raise ValueError(f"Residue count mismatch: {l_path} vs {d_path}")
for residue_idx, ((phi_l, psi_l), (phi_d, psi_d)) in enumerate(
zip(l_angles, d_angles),
start=1,
):
phi_l_deg = float(np.degrees(phi_l)) if phi_l is not None else None
psi_l_deg = float(np.degrees(psi_l)) if psi_l is not None else None
phi_d_deg = float(np.degrees(phi_d)) if phi_d is not None else None
psi_d_deg = float(np.degrees(psi_d)) if psi_d is not None else None
phi_err = None
psi_err = None
if phi_l_deg is not None and phi_d_deg is not None:
phi_err = abs(phi_l_deg + phi_d_deg)
target_phi_errors.append(phi_err)
angle_rows.append(
{
"target": target,
"pose_key": pose_key,
"residue_index": residue_idx,
"angle": "phi",
"L_angle": phi_l_deg,
"D_angle": phi_d_deg,
"abs_L_plus_D": phi_err,
}
)
if psi_l_deg is not None and psi_d_deg is not None:
psi_err = abs(psi_l_deg + psi_d_deg)
target_psi_errors.append(psi_err)
angle_rows.append(
{
"target": target,
"pose_key": pose_key,
"residue_index": residue_idx,
"angle": "psi",
"L_angle": psi_l_deg,
"D_angle": psi_d_deg,
"abs_L_plus_D": psi_err,
}
)
if (
phi_l_deg is not None
and psi_l_deg is not None
and phi_d_deg is not None
and psi_d_deg is not None
):
target_rama_rows += 1
rama_rows.append(
{
"target": target,
"pose_key": pose_key,
"residue_index": residue_idx,
"phi_L": phi_l_deg,
"psi_L": psi_l_deg,
"phi_D": phi_d_deg,
"psi_D": psi_d_deg,
"phi_D_sign_inverted": -phi_d_deg,
"psi_D_sign_inverted": -psi_d_deg,
"abs_phi_L_plus_phi_D": phi_err,
"abs_psi_L_plus_psi_D": psi_err,
}
)
summary_rows.append(
{
"target": target,
"n_pose_pairs": len(pairs),
"n_paired_phi": len(target_phi_errors),
"n_paired_psi": len(target_psi_errors),
"n_rama_coordinates": target_rama_rows,
"max_abs_phi_L_plus_phi_D": max(target_phi_errors),
"max_abs_psi_L_plus_psi_D": max(target_psi_errors),
}
)
return pd.DataFrame(rama_rows), pd.DataFrame(angle_rows), pd.DataFrame(summary_rows)
def _mode_pose_files(target: str, mode: str) -> list[Path]:
if mode == "L":
return sorted((BENCH / target / "L_peptide").glob("len_*/Poses/Binder_L_pose_*.pdb"))
if mode == "D":
return sorted((BENCH / target / "D_peptide").glob("len_*/Poses/Binder_D_pose_*.pdb"))
raise ValueError(f"Unknown mode: {mode}")
def collect_benchmark_ld_phi_psi() -> tuple[pd.DataFrame, pd.DataFrame]:
"""Collect raw phi/psi distributions from benchmark direct-L and final-D designs."""
rows = []
summary_rows = []
for target in BENCHMARK_TARGETS:
for mode in ["L", "D"]:
files = _mode_pose_files(target, mode)
n_rama = 0
for path in files:
angles = _chain_phi_psi(path)
pose_key = f"{path.parents[1].name}:{path.stem.split('_')[-1]}"
for residue_idx, (phi, psi) in enumerate(angles, start=1):
if phi is None or psi is None:
continue
n_rama += 1
rows.append(
{
"target": target,
"mode": mode,
"pose_key": pose_key,
"residue_index": residue_idx,
"phi": float(np.degrees(phi)),
"psi": float(np.degrees(psi)),
}
)
summary_rows.append(
{
"target": target,
"mode": mode,
"n_pose_backbones": len(files),
"n_rama_coordinates": n_rama,
}
)
return pd.DataFrame(rows), pd.DataFrame(summary_rows)
def _draw_reference_axes(ax) -> None:
ax.axhline(0, color=PALETTE["neutral_light"], lw=0.6, zorder=0)
ax.axvline(0, color=PALETTE["neutral_light"], lw=0.6, zorder=0)
ax.set_xlim(-180, 180)
ax.set_ylim(-180, 180)
ax.set_aspect("equal", adjustable="box")
ax.set_xticks([-180, 0, 180])
ax.set_yticks([-180, 0, 180])
def _density_contour(ax, x, y, color, label, linestyle="-", linewidth=1.3):
hist, xedges, yedges = np.histogram2d(
x,
y,
bins=90,
range=[[-180, 180], [-180, 180]],
)
z = gaussian_filter(hist.T, sigma=1.2)
positive = z[z > 0]
if len(positive) == 0:
return None
levels = np.quantile(positive, [0.70, 0.85, 0.94])
levels = np.unique(levels)
xc = (xedges[:-1] + xedges[1:]) / 2
yc = (yedges[:-1] + yedges[1:]) / 2
return ax.contour(
xc,
yc,
z,
levels=levels,
colors=[color],
linewidths=linewidth,
linestyles=linestyle,
)
def _summary_table(ax, summary: pd.DataFrame) -> None:
ax.axis("off")
display = summary.copy()
display["target"] = display["target"].replace({"TNFalpha": "TNF-alpha"})
rows = []
for _, row in display.iterrows():
rows.append(
[
row["target"],
f"{int(row['n_pose_pairs'])}",
f"{int(row['n_paired_phi'])}",
f"{int(row['n_paired_psi'])}",
f"{row['max_abs_phi_L_plus_phi_D']:.3f}",
f"{row['max_abs_psi_L_plus_psi_D']:.3f}",
]
)
rows.append(
[
"All",
f"{int(summary['n_pose_pairs'].sum())}",
f"{int(summary['n_paired_phi'].sum())}",
f"{int(summary['n_paired_psi'].sum())}",
f"{summary['max_abs_phi_L_plus_phi_D'].max():.3f}",
f"{summary['max_abs_psi_L_plus_psi_D'].max():.3f}",
]
)
table = ax.table(
cellText=rows,
colLabels=[
"Target",
"Pose\npairs",
"Paired\nphi",
"Paired\npsi",
"Max phi\nerror (deg)",
"Max psi\nerror (deg)",
],
loc="center",
cellLoc="center",
colLoc="center",
colWidths=[0.17, 0.14, 0.15, 0.15, 0.19, 0.19],
)
table.auto_set_font_size(False)
table.set_fontsize(6.8)
table.scale(1.0, 1.42)
for (row_idx, col_idx), cell in table.get_celld().items():
cell.set_edgecolor("#D7D7D7")
cell.set_linewidth(0.55)
if row_idx == 0:
cell.set_facecolor(PALETTE["blue_main"])
cell.set_text_props(color="white", weight="bold")
elif row_idx == len(rows):
cell.set_facecolor("#EEF2F6")
cell.set_text_props(weight="bold")
elif row_idx % 2 == 0:
cell.set_facecolor("#F7F7F7")
else:
cell.set_facecolor("white")
add_panel_label(ax, "a")
def _scatter_cloud(ax, x, y, color, label, marker="o", face=True, alpha=0.20, size=5):
facecolor = color if face else "none"
edgecolor = "none" if face else color
linewidth = 0.35 if not face else 0.0
ax.scatter(
x,
y,
s=size,
marker=marker,
alpha=alpha,
linewidths=linewidth,
facecolors=facecolor,
edgecolors=edgecolor,
color=color if face else None,
rasterized=True,
label=label,
)
def _sample_points(df: pd.DataFrame, n: int = 4500) -> pd.DataFrame:
if len(df) <= n:
return df
return df.sample(n=n, random_state=7)
def plot(rows: pd.DataFrame, summary: pd.DataFrame) -> None:
apply_nature_style(font_size=7)
fig, axes = plt.subplots(1, 2, figsize=(6.5, 2.9), sharex=True, sharey=True)
plot_rows = _sample_points(rows, n=4500)
ax_raw = axes[0]
_draw_reference_axes(ax_raw)
_scatter_cloud(
ax_raw,
plot_rows["phi_L"],
plot_rows["psi_L"],
PALETTE["blue_main"],
"L backbone",
alpha=0.26,
size=5,
)
_scatter_cloud(
ax_raw,
plot_rows["phi_D"],
plot_rows["psi_D"],
PALETTE["orange"],
"Final D backbone",
alpha=0.24,
size=5,
)
_density_contour(
ax_raw,
rows["phi_L"],
rows["psi_L"],
PALETTE["blue_main"],
"L backbone",
linewidth=1.2,
)
_density_contour(
ax_raw,
rows["phi_D"],
rows["psi_D"],
PALETTE["orange"],
"Final D backbone",
linewidth=1.2,
)
ax_raw.set_xlabel("φ (°)")
ax_raw.set_ylabel("ψ (°)")
add_panel_label(ax_raw, "a")
ax_inv = axes[1]
_draw_reference_axes(ax_inv)
_scatter_cloud(
ax_inv,
plot_rows["phi_L"],
plot_rows["psi_L"],
PALETTE["blue_main"],
"L backbone",
alpha=0.25,
size=5,
)
_scatter_cloud(
ax_inv,
plot_rows["phi_D_sign_inverted"],
plot_rows["psi_D_sign_inverted"],
PALETTE["orange"],
"Sign-inverted D backbone",
face=False,
alpha=0.28,
size=8,
)
_density_contour(
ax_inv,
rows["phi_L"],
rows["psi_L"],
PALETTE["blue_main"],
"L backbone",
linewidth=1.2,
)
_density_contour(
ax_inv,
rows["phi_D_sign_inverted"],
rows["psi_D_sign_inverted"],
PALETTE["neutral_dark"],
"Sign-inverted D",
linestyle="--",
linewidth=1.1,
)
ax_inv.set_xlabel("φ (°)")
ax_inv.set_ylabel("ψ (°)")
add_panel_label(ax_inv, "b")
handles = [
plt.Line2D(
[0],
[0],
marker="o",
color="w",
markerfacecolor=PALETTE["blue_main"],
markersize=5,
label="L backbone",
),
plt.Line2D(
[0],
[0],
marker="o",
color="w",
markerfacecolor=PALETTE["orange"],
markersize=5,
label="Final D backbone",
),
plt.Line2D(
[0],
[0],
color=PALETTE["neutral_dark"],
lw=1.2,
ls="--",
label="Sign-inverted D backbone",
),
]
fig.legend(handles=handles, loc="upper center", ncol=3, bbox_to_anchor=(0.5, 1.05))
FIGDIR.mkdir(parents=True, exist_ok=True)
fig.tight_layout(rect=[0.02, 0.03, 1.0, 0.92], w_pad=1.0)
for ext in ["svg", "pdf"]:
fig.savefig(FIGDIR / f"fig_phi_psi_mirror_qc.{ext}", bbox_inches="tight")
fig.savefig(FIGDIR / "fig_phi_psi_mirror_qc.png", dpi=300, bbox_inches="tight")
plt.close(fig)
def plot_benchmark_ld(rows: pd.DataFrame) -> None:
apply_nature_style(font_size=7)
fig, axes = plt.subplots(1, 3, figsize=(7.0, 2.55), sharex=True, sharey=True)
target_labels = {"PDL1": "PD-L1", "MDM2": "MDM2", "IL23R": "IL-23R"}
for idx, target in enumerate(BENCHMARK_TARGETS):
ax = axes[idx]
_draw_reference_axes(ax)
tdf = rows[rows["target"] == target]
l_all = tdf[tdf["mode"] == "L"]
d_all = tdf[tdf["mode"] == "D"]
l_plot = _sample_points(l_all, n=1800)
d_plot = _sample_points(d_all, n=1800)
_scatter_cloud(
ax,
l_plot["phi"],
l_plot["psi"],
PALETTE["blue_main"],
"Direct L-peptide",
alpha=0.26,
size=5,
)
_scatter_cloud(
ax,
d_plot["phi"],
d_plot["psi"],
PALETTE["orange"],
"Mirror-derived D-peptide",
alpha=0.24,
size=5,
)
_density_contour(
ax,
l_all["phi"],
l_all["psi"],
PALETTE["blue_main"],
"Direct L-peptide",
linewidth=1.2,
)
_density_contour(
ax,
d_all["phi"],
d_all["psi"],
PALETTE["orange"],
"Mirror-derived D-peptide",
linewidth=1.2,
)
ax.set_xlabel(f"{target_labels[target]} φ (°)")
if idx == 0:
ax.set_ylabel("ψ (°)")
add_panel_label(ax, "abc"[idx])
handles = [
plt.Line2D(
[0],
[0],
marker="o",
color="w",
markerfacecolor=PALETTE["blue_main"],
markersize=5,
label="Direct L-peptide design",
),
plt.Line2D(
[0],
[0],
marker="o",
color="w",
markerfacecolor=PALETTE["orange"],
markersize=5,
label="Mirror-derived D-peptide design",
),
]
fig.legend(handles=handles, loc="upper center", ncol=2, bbox_to_anchor=(0.5, 1.04))
FIGDIR.mkdir(parents=True, exist_ok=True)
fig.tight_layout(rect=[0.02, 0.02, 1.0, 0.90], w_pad=0.8)
for ext in ["svg", "pdf"]:
fig.savefig(FIGDIR / f"fig_phi_psi_mirror_qc.{ext}", bbox_inches="tight")
fig.savefig(FIGDIR / "fig_phi_psi_mirror_qc.png", dpi=300, bbox_inches="tight")
plt.close(fig)
def main() -> None:
FIGDATA.mkdir(parents=True, exist_ok=True)
rows, angle_rows, summary = collect_phi_psi()
rows.to_csv(FIGDATA / "fig_phi_psi_mirror_qc_rama_coordinates.csv", index=False)
angle_rows.to_csv(FIGDATA / "fig_phi_psi_mirror_qc_angles.csv", index=False)
summary.to_csv(FIGDATA / "fig_phi_psi_mirror_qc_summary.csv", index=False)
benchmark_rows, benchmark_summary = collect_benchmark_ld_phi_psi()
benchmark_rows.to_csv(
FIGDATA / "fig_phi_psi_benchmark_ld_rama_coordinates.csv", index=False
)
benchmark_summary.to_csv(
FIGDATA / "fig_phi_psi_benchmark_ld_summary.csv", index=False
)
plot_benchmark_ld(benchmark_rows)
print(summary.to_string(index=False))
print(benchmark_summary.to_string(index=False))
if __name__ == "__main__":
main()
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