| import json
|
| import math
|
| import os
|
|
|
| import matplotlib.pyplot as plt
|
| import numpy as np
|
| import scipy
|
|
|
|
|
| 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", "figureS4")
|
|
|
|
|
| def main():
|
| os.makedirs(OUTPUT_FOLDER, exist_ok=True)
|
|
|
|
|
| pdb_to_subunits = {}
|
| benchmark_path = os.path.join(DATA_PATH, "benchmark2")
|
|
|
| for filename in os.listdir(os.path.join(benchmark_path, "pdb_infos")):
|
| jobname = filename.split(".")[0]
|
| if not filename.endswith(".json"):
|
| continue
|
| pdb_to_subunits[jobname] = json.load(open(os.path.join(benchmark_path, "pdb_infos", filename), "rb"))
|
|
|
| print("Benchmark size ", len(pdb_to_subunits), "PDB ids: ", list(pdb_to_subunits.keys()))
|
|
|
|
|
| afm_ics = {'7QRU': 0.8875607022786702, '7OZN': 0, '8BEL': 0.7880794701986755, '7ZKQ': 0.3887775551102204, '7E8T': 0, '7T3B': 0.6454709058188361, '7OBA': 0, '8ADN': 0, '7ARC': 0.4630650496141124, '7XHO': 0, '8CTE': 0, '8A3T': 0, '7T2R': 0.8136942675159236, '8E9G': 0, '8ADL': 0, '7UIC': 0, '8HIL': 0.663960306720794, '7PKN': 0.735445836403832, '7USE': 0.823474615742897, '8FEE': 0.7976314872866598, '7P2Y': 0, '7QVE': 0, '7WFF': 0, '8A5O': 0.7162534435261708, '8F5O': 0}
|
| combfold_ics = {'7QRU': 0.8370890284447728, '7OZN': 0, '8BEL': 0.6606606606606606, '7ZKQ': 0.1755062680810029, '7E8T': 0.26416168516937094, '7T3B': 0.659914712153518, '7OBA': 0.49898511502029774, '8ADN': 0.39416432045047345, '7ARC': 0.360079387581514, '7XHO': 0.12848370335380258, '8CTE': 0.31272210376687987, '8A3T': 0.19494769073743298, '7T2R': 0.7802621434078643, '8E9G': 0.5614627285513363, '8ADL': 0, '7UIC': 0.16527390900649955, '8HIL': 0.35519387266634755, '7PKN': 0.22074074074074074, '7USE': 0.5253557095950383, '8FEE': 0.06477409036385445, '7P2Y': 0.34454358270418667, '7QVE': 0.2577530719719134, '7WFF': 0.5515331355093966, '8A5O': 0.24546424759871932, '8F5O': 0.0031237797735259665}
|
|
|
|
|
| afm_results, combfold_results = [], []
|
| for pdb_id in afm_ics:
|
| afm_results.append(afm_ics[pdb_id])
|
| combfold_results.append(combfold_ics[pdb_id])
|
|
|
| fig, ax = plt.subplots(figsize=(2.25, 1.75), dpi=600)
|
| ax.scatter(combfold_results, afm_results, alpha=0.4, s=10, edgecolor='none')
|
| ax.set_xlabel("CombFold ICS", fontsize=11)
|
| ax.set_ylabel("AFMv3 ICS", fontsize=11)
|
| plt.yticks(np.arange(0, 1.2, 0.2), fontsize=8)
|
| plt.xticks(np.arange(0, 1.2, 0.2), fontsize=8)
|
|
|
| plt.gca().spines['top'].set_visible(False)
|
| plt.gca().spines['right'].set_visible(False)
|
| plt.plot([0, 1], [0, 1], 'r--')
|
|
|
| plt.savefig(os.path.join(OUTPUT_FOLDER, "FigS4a.png"), bbox_inches='tight', dpi=300)
|
|
|
|
|
| prodigy_scores = json.load(open(os.path.join(benchmark_path, "prodigy_results.json"), "r"))
|
|
|
| joined_scores_targets, joined_scores_models = [], []
|
| for pdb_id, scores in prodigy_scores.items():
|
| if scores is None or len(scores) == 0:
|
| continue
|
| for score_key, score in scores.items():
|
| if score[1] is None:
|
| continue
|
|
|
| if min(score[0], score[1]) < 1e-30:
|
| print("Skipping ", score[0], score[1], "for ", pdb_id, "as it is too small")
|
| continue
|
| if abs(math.log(score[0], 10) - math.log(score[1], 10)) > 10:
|
| print("Large diff ", pdb_id, score_key, score[0], score[1])
|
| joined_scores_targets.append(score[0])
|
| joined_scores_models.append(score[1])
|
|
|
| print("values", len(scores), joined_scores_targets[-1 * len(scores):], joined_scores_models[-1 * len(scores):])
|
| print("correlation for ", pdb_id, np.corrcoef(joined_scores_targets[-1 * len(scores):],
|
| joined_scores_models[-1 * len(scores):])[0][1])
|
|
|
| fig, ax = plt.subplots(figsize=(2.25, 1.75), dpi=600)
|
| ax.scatter(joined_scores_targets, joined_scores_models, alpha=0.4, s=10, edgecolor='none')
|
| ax.set_xlabel("Experimental structure", fontsize=11)
|
| ax.set_ylabel("CombFold Top-1 model", fontsize=11)
|
|
|
| plt.gca().spines['top'].set_visible(False)
|
| plt.gca().spines['right'].set_visible(False)
|
| plt.plot([0, 1], [0, 1], 'r--', lw=0.5)
|
|
|
| plt.xscale('log')
|
| plt.yscale('log')
|
|
|
| plt.xlim(10 ** -30, 1)
|
| plt.ylim(10 ** -30, 1)
|
|
|
| corr = scipy.stats.spearmanr(joined_scores_targets, joined_scores_models)[0]
|
|
|
| equation = f'$\\rho = {corr:.2f}$'
|
|
|
| plt.annotate(equation, xy=(0.05, 0.9), xycoords='axes fraction', fontsize=8,
|
| bbox=dict(boxstyle='square', facecolor='white', edgecolor="black", lw=0.5, pad=0.2))
|
|
|
| plt.savefig(os.path.join(OUTPUT_FOLDER, "FigS4b.png"), bbox_inches='tight', dpi=300)
|
|
|
|
|
| not_relaxed, relaxed = [], []
|
| for line in open(os.path.join(benchmark_path, "clashscores.csv"), "r").read().split("\n")[1:]:
|
| if not line:
|
| continue
|
| pdb_id, score_not_relaxed, score_relaxed = line.split(",")[:3]
|
| not_relaxed.append(float(score_not_relaxed))
|
| relaxed.append(float(score_relaxed))
|
|
|
| data_to_plot = [not_relaxed, relaxed]
|
|
|
| fig, ax = plt.subplots(figsize=(2.25, 1.75), dpi=600)
|
| plt.yticks(fontsize=11)
|
| plt.xticks(fontsize=11)
|
|
|
| ax.violinplot(data_to_plot, showmedians=True)
|
|
|
| ax.set_xticks([1, 2])
|
| ax.set_xticklabels(["No relaxation", "Relaxed"])
|
| plt.gca().spines['top'].set_visible(False)
|
| plt.gca().spines['right'].set_visible(False)
|
| plt.ylabel('MolProbity clashscore', fontsize=11)
|
|
|
| plt.savefig(os.path.join(OUTPUT_FOLDER, "FigS4c.png"), bbox_inches='tight', dpi=300)
|
|
|
|
|
| if __name__ == "__main__":
|
| main() |
