| import json
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| import os
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| from collections import defaultdict
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| import matplotlib.pyplot as plt
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| import numpy as np
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| from matplotlib.ticker import MaxNLocator
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| THIS_SCRIPT_PATH = os.path.abspath(__file__)
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| DATA_PATH = os.path.join(os.path.dirname(THIS_SCRIPT_PATH), "data")
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| OUTPUT_FOLDER = os.path.join(os.path.dirname(THIS_SCRIPT_PATH), "output", "figure2")
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| def draw_scatter(name1, name2, x, y, output_path):
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| fig, ax = plt.subplots(figsize=(2.25, 1.75), dpi=600)
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| ax.scatter(x, y, alpha=0.4, s=10, edgecolor='none', color="#1f78b4")
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| ax.xaxis.set_major_locator(MaxNLocator(6))
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| ax.yaxis.set_major_locator(MaxNLocator(6))
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| plt.yticks(fontsize=8)
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| plt.xticks(fontsize=8)
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| plt.xlabel(name1, fontsize=11)
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| plt.ylabel(name2, fontsize=11)
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| p = np.poly1d(np.polyfit(x, y, 1))
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| x_lin_space = np.linspace(min(x), max(x), 100)
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| plt.plot(x_lin_space, p(x_lin_space), color="#ff7f00", linewidth=1, linestyle="--")
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| plt.gca().spines['top'].set_visible(False)
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| plt.gca().spines['right'].set_visible(False)
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|
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| equation = f'$\\rho = {np.corrcoef(x, y)[0][1]:.2f}$'
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| plt.annotate(equation, xy=(0.05, 0.9), xycoords='axes fraction', fontsize=8,
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| bbox=dict(boxstyle='square', facecolor='white', edgecolor="black", lw=0.5, pad=0.2))
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| plt.savefig(output_path, bbox_inches='tight', dpi=300)
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| def get_connectivity_groups(pairs_scores, all_names, dockq_th: float = 0.23, pae_th: float = 0):
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| subunits_groups = {subunit_name: i for i, subunit_name in enumerate(all_names)}
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| for subunit_pair, scores in pairs_scores.items():
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| subunit_name1, subunit_name2 = subunit_pair.split("_plus_")
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| if subunits_groups[subunit_name1] == subunits_groups[subunit_name2]:
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| continue
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| for score in scores:
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| if score["dockq"] > dockq_th and score["pae_score"] > pae_th:
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| group_to_change = subunits_groups[subunit_name2]
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| for subunit_name in subunits_groups.keys():
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| if subunits_groups[subunit_name] == group_to_change:
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| subunits_groups[subunit_name] = subunits_groups[subunit_name1]
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| break
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| group_to_subunit = defaultdict(list)
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| for subunit, group_id in subunits_groups.items():
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| group_to_subunit[group_id].append(subunit)
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| connectivity_groups = sorted(list(group_to_subunit.values()), key=len, reverse=True)
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| return connectivity_groups
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| def main():
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| os.makedirs(OUTPUT_FOLDER, exist_ok=True)
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| pdb_to_subunits = {}
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| benchmark_path = os.path.join(DATA_PATH, "benchmark1")
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| for filename in os.listdir(os.path.join(benchmark_path, "pdb_infos")):
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| jobname = filename.split(".")[0]
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| if not filename.endswith(".json"):
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| continue
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| pdb_to_subunits[jobname] = json.load(open(os.path.join(benchmark_path, "pdb_infos", filename), "rb"))
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| print("Benchmark size ", len(pdb_to_subunits), "PDB ids: ", list(pdb_to_subunits.keys()))
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| combfold_results = json.load(open(os.path.join(benchmark_path, "combfold_results.json"), "r"))
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| combfold_results = {k: v for k, v in combfold_results.items() if v is not None and k in pdb_to_subunits}
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| combfold_parsed_results = {}
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| for jobname, results in combfold_results.items():
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| if not results["scores"]:
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| continue
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| tm_and_scores = [(result['weighted_trans_score'], result['tm_score']) for result in results["scores"].values()]
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| combfold_parsed_results[jobname] = [i[1] for i in sorted(tm_and_scores, reverse=True)]
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| pairs_validation = json.load(open(os.path.join(benchmark_path, "pairs_validation.json"), "r"))
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| print("CombFold able to assemble ", len(combfold_parsed_results))
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| print("CombFold able to assemble correctly (TM-score > 0.7) ", len([i for i in combfold_parsed_results.values()
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| if max(i) > 0.7]))
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| af2_results_path = os.path.join(benchmark_path, "AFMv2_results.json")
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| af2_results = {}
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| validation_results = json.load(open(af2_results_path, "r"))
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| for jobname in validation_results:
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| if jobname not in pdb_to_subunits:
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| continue
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| af2_results[jobname] = []
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| for pdb_name in sorted(validation_results[jobname].keys()):
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| af2_results[jobname].append(validation_results[jobname][pdb_name]["tm_score"])
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| print("AFMv2 able to assemble ", len([i for i in af2_results.values() if i]))
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| name1, name2 = "CombFold", "AFMv2"
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| th_high, th_accept = 0.8, 0.7
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| bar_width = 0.3
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| fig, ax = plt.subplots()
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| labels = []
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| for count, max_t in enumerate([1, 5, 10]):
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| labels.append(f"Top {max_t}")
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| results1 = [max(v[:max_t], default=0) for v in combfold_parsed_results.values()]
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| results2 = [max(v[:max_t], default=0) for v in af2_results.values()]
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| res1_high = len([i for i in results1 if i >= th_high])
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| res1_acceptable = len([i for i in results1 if th_high > i >= th_accept])
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| res2_high = len([i for i in results2 if i >= th_high])
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| res2_acceptable = len([i for i in results2 if th_high > i >= th_accept])
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| print(name1, max_t, res1_high, res1_acceptable)
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| print(name2, max_t, res2_high, res2_acceptable)
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| ax.bar(count, [res1_high / len(pdb_to_subunits)], color='#1f78b4', width=bar_width, edgecolor='grey',
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| label=f"{name1} High")
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| ax.bar(count, [res1_acceptable / len(pdb_to_subunits)], color='#1f78b4', alpha=0.5, width=bar_width,
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| edgecolor='grey', bottom=[res1_high / len(pdb_to_subunits)], label=f"{name1} Acceptable")
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| ax.bar(count + bar_width, [res2_high / len(pdb_to_subunits)], color='#ff7f00', width=bar_width,
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| edgecolor='grey',
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| label=f"{name2} High")
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| ax.bar(count + bar_width, [res2_acceptable / len(pdb_to_subunits)], color='#ff7f00', alpha=0.5, width=bar_width,
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| edgecolor='grey', bottom=[res2_high / len(pdb_to_subunits)], label=f"{name2} Acceptable")
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| plt.ylabel('Success rate', fontsize=11)
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| plt.xticks([i + bar_width / 2 for i in range(len(labels))], labels, fontsize=8)
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| plt.yticks(np.arange(0.1, 0.9, 0.1), fontsize=8)
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| handles, labels = plt.gca().get_legend_handles_labels()
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| new_labels, new_handles = [], []
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| for handle, label in zip(handles, labels):
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| if label not in new_labels:
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| new_labels.append(label)
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| new_handles.append(handle)
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| ax.legend(new_handles, new_labels, bbox_to_anchor=(-0.35, -0.7), loc='lower left', fontsize=8, ncol=2,
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| handletextpad=0.2, columnspacing=0.25)
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| fig.set_size_inches(2, 1.5)
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| fig.set_dpi(300)
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| plt.gca().spines['top'].set_visible(False)
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| plt.gca().spines['right'].set_visible(False)
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| plt.savefig(os.path.join(OUTPUT_FOLDER, "Fig2A.png"), bbox_inches='tight', dpi=300)
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| confidence, tm_results = [], []
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| for jobname, results in combfold_results.items():
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| if len(results["scores"]) == 0:
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| continue
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| conf_key = "weighted_trans_score"
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| best_confidence = max(list(results["scores"].values()), key=lambda x: x[conf_key])
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| confidence.append(best_confidence[conf_key])
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| tm_results.append(best_confidence["tm_score"])
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| print("confidence correlation:", np.corrcoef(confidence, tm_results)[0][1])
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| draw_scatter("Predicted confidence", "TM-score", confidence, tm_results, os.path.join(OUTPUT_FOLDER, "Fig2B.png"))
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| tm_results = []
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| pairwise_connectivity = []
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| MAX_T = 10
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| for jobname in sorted(pdb_to_subunits.keys()):
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| if jobname not in pairs_validation:
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| continue
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| tm_results.append(max(combfold_parsed_results.get(jobname, [0])[:MAX_T]))
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|
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| connectivity_groups = get_connectivity_groups(pairs_validation[jobname]["pairs_scores"],
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| list(pairs_validation[jobname]["domains_scores"].keys()),
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| dockq_th=0.23, pae_th=0)
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| total_count = connected_count = 0
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| for domain_name in pairs_validation[jobname]["domains_scores"].keys():
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| res_num = len(pdb_to_subunits[jobname][domain_name.split("_")[0]]["sequence"])
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| total_count += res_num
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| if domain_name in connectivity_groups[0] \
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| and pairs_validation[jobname]["domains_scores"][domain_name]["rmsd"] < 10:
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| connected_count += res_num
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| pairwise_connectivity.append(connected_count / total_count)
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| if pairwise_connectivity[-1] >= 0.6 and tm_results[-1] < 0.7:
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| print("****", jobname, pairwise_connectivity[-1], tm_results[-1])
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| print(np.corrcoef(pairwise_connectivity, tm_results)[0][1])
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| print("probably possible", len([i for i in pairwise_connectivity if i >= 0.6]))
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| draw_scatter("Pairwise Connectivity", "TM-score", pairwise_connectivity, tm_results,
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| os.path.join(OUTPUT_FOLDER, "Fig2C.png"))
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| print("Pairwise Connectivity correlation:", np.corrcoef(pairwise_connectivity, tm_results)[0][1])
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|
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| MAX_T = 1
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| combfold_tm_results, afm2_tm_results = [], []
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| for jobname in pdb_to_subunits:
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| combfold_tm_results.append(max(combfold_parsed_results.get(jobname, [])[:MAX_T], default=0))
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| afm2_tm_results.append(max(af2_results.get(jobname, [])[:MAX_T], default=0))
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| if afm2_tm_results[-1] > 0.7 > combfold_tm_results[-1]:
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| print(jobname, "better on AFMv2", afm2_tm_results[-1], combfold_tm_results[-1])
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|
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| fig, ax = plt.subplots(figsize=(2.25, 1.75), dpi=600)
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| ax.scatter(combfold_tm_results, afm2_tm_results, alpha=0.4, s=10, edgecolor='none')
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| ax.set_xlabel("CombFold TM-score", fontsize=11)
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| ax.set_ylabel("AFMv2 TM-score", fontsize=11)
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| plt.yticks(np.arange(0, 1.2, 0.2), fontsize=8)
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| plt.xticks(np.arange(0, 1.2, 0.2), fontsize=8)
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|
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| plt.gca().spines['top'].set_visible(False)
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| plt.gca().spines['right'].set_visible(False)
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| plt.plot([0, 1], [0, 1], 'r--')
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| plt.savefig(os.path.join(OUTPUT_FOLDER, "Fig2D.png"), bbox_inches='tight', dpi=300)
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|
|
| if __name__ == "__main__":
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| main() |
