| import json
|
| import os
|
|
|
| import matplotlib.pyplot as plt
|
| import numpy as np
|
| from matplotlib.ticker import MaxNLocator
|
|
|
| THIS_SCRIPT_PATH = os.path.abspath(__file__)
|
| DATA_PATH = os.path.join(os.path.dirname(THIS_SCRIPT_PATH), "data")
|
| OUTPUT_FOLDER = os.path.join(os.path.dirname(THIS_SCRIPT_PATH), "output", "figureS6")
|
|
|
|
|
| def score_to_pae(score):
|
|
|
| return np.sqrt((100 - score) * 4)
|
|
|
|
|
| def to_pae_scores(scores):
|
| return [score_to_pae(score) for score in scores]
|
|
|
|
|
| def draw_scatter(name1, name2, x, y, output_path, y_lim=None):
|
| fig, ax = plt.subplots(figsize=(2.25, 1.75), dpi=600)
|
| ax.scatter(x, y, alpha=0.4, s=10, edgecolor='none', color="#1f78b4")
|
| ax.xaxis.set_major_locator(MaxNLocator(6))
|
| ax.yaxis.set_major_locator(MaxNLocator(6))
|
| plt.yticks(fontsize=8)
|
| plt.xticks(fontsize=8)
|
| plt.xlabel(name1, fontsize=11)
|
| plt.ylabel(name2, fontsize=11)
|
| p = np.poly1d(np.polyfit(x, y, 1))
|
| x_lin_space = np.linspace(min(x), max(x), 100)
|
| plt.plot(x_lin_space, p(x_lin_space), color="#ff7f00", linewidth=1, linestyle="--")
|
| plt.gca().spines['top'].set_visible(False)
|
| plt.gca().spines['right'].set_visible(False)
|
|
|
| if y_lim is not None:
|
| plt.ylim(y_lim)
|
|
|
| equation = f'$\\rho = {np.corrcoef(x, y)[0][1]:.2f}$'
|
|
|
|
|
| plt.annotate(equation, xy=(0.7, 0.9), xycoords='axes fraction', fontsize=8,
|
| bbox=dict(boxstyle='square', facecolor='white', edgecolor="black", lw=0.5, pad=0.2))
|
|
|
| plt.savefig(output_path, bbox_inches='tight', dpi=300)
|
|
|
|
|
| def main():
|
|
|
| benchmark_path = os.path.join(DATA_PATH, "benchmark2")
|
| scores_by_pdb = json.load(open(os.path.join(benchmark_path, "rmsd_validate.json"), "r"))
|
| os.makedirs(OUTPUT_FOLDER, exist_ok=True)
|
|
|
| rmsds = []
|
| iptms = []
|
| iplddt = []
|
| weighted_iplddt = []
|
| full_pae = []
|
| processed_pae = []
|
| """
|
| {'pdb': 'HDF_NDF_unrelaxed_rank_001_alphafold2_multimer_v3_model_2_seed_000.pdb', 'iptm': 0.55,
|
| 'rmsd': 9.05883774873516, 's1_plddt': 76.62620098039216, 's1_iplddt': 76.20222222222222,
|
| 's1_pae': 10.695484849577085, 's2_plddt': 89.00289827255278, 's2_iplddt': 74.16875, 's2_pae': 5.845915539656869,
|
| 'full_pae': 17.015451902450042, 'interface_pae': 8.971527777777778, 'interface1_size': 9, 'interface2_size': 8}
|
| """
|
| for pdb_id, scores in scores_by_pdb.items():
|
| for score in scores:
|
| rmsds.append(score["rmsd"])
|
| iptms.append(score["iptm"])
|
| iplddt.append((score["s1_iplddt"] + score["s2_iplddt"]) / 2)
|
| weighted_iplddt.append((score["interface1_size"] * score["s1_iplddt"]
|
| + score["interface2_size"] * score["s2_iplddt"])
|
| / (score["interface1_size"] + score["interface2_size"]))
|
| processed_pae.append(max([1, 100 - (1 / 4) * (score["full_pae"] ** 2)]))
|
| full_pae.append(score["full_pae"])
|
|
|
| draw_scatter("RMSD", "ipTM", rmsds, iptms, os.path.join(OUTPUT_FOLDER, "FigS6a.png"), y_lim=(0, 1))
|
| draw_scatter("RMSD", "ipLDDT", rmsds, iplddt, os.path.join(OUTPUT_FOLDER, "FigS6b.png"), y_lim=(0, 100))
|
| draw_scatter("RMSD", "Normalized ipLDDT", rmsds, weighted_iplddt, os.path.join(OUTPUT_FOLDER, "FigS6c.png"),
|
| y_lim=(0, 100))
|
| draw_scatter("RMSD", "Full PAE", rmsds, full_pae, os.path.join(OUTPUT_FOLDER, "FigS6d.png"), y_lim=(0, 35))
|
| draw_scatter("RMSD", "CombFold Score", rmsds, processed_pae, os.path.join(OUTPUT_FOLDER, "FigS6e.png"),
|
| y_lim=(0, 100))
|
|
|
|
|
| all_pae_scores = json.load(open(os.path.join(DATA_PATH, "benchmark2", "pae_data", "all_pae_scores.json"), "r"))
|
| used_pae_scores = json.load(open(os.path.join(DATA_PATH, "benchmark2", "pae_data", "used_pae_scores.json"), "r"))
|
| top1_used_pae_scores = json.load(open(os.path.join(DATA_PATH, "benchmark2", "pae_data",
|
| "top1_used_pae_scores.json"), "r"))
|
|
|
| data_to_plot = [to_pae_scores(all_pae_scores), to_pae_scores(top1_used_pae_scores)]
|
|
|
| fig, ax = plt.subplots(figsize=(2.25, 1.75), dpi=600)
|
| ax.xaxis.set_major_locator(MaxNLocator(6))
|
| ax.yaxis.set_major_locator(MaxNLocator(6))
|
| plt.yticks(fontsize=8)
|
| plt.xticks(fontsize=8)
|
|
|
| ax.violinplot(data_to_plot, showmedians=True)
|
|
|
| ax.set_xticks([1, 2])
|
| ax.set_xticklabels(["All Generated", "Used in Top1 Results"])
|
| plt.gca().spines['top'].set_visible(False)
|
| plt.gca().spines['right'].set_visible(False)
|
| plt.ylabel('PAE scores', fontsize=11)
|
|
|
| print("medians: ", np.median(all_pae_scores), np.median(top1_used_pae_scores))
|
| print("means: ", np.mean(all_pae_scores), np.mean(top1_used_pae_scores))
|
|
|
| plt.savefig(os.path.join(OUTPUT_FOLDER, "FigS6f.png"), bbox_inches='tight', dpi=300)
|
|
|
|
|
| if __name__ == "__main__":
|
| main() |
