import itertools from functools import partial import pandas as pd from tqdm import tqdm import json import os from multiprocessing import Pool import traceback import subprocess import numpy as np from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor, as_completed,TimeoutError import pandas as pd from tqdm import tqdm import traceback import json import os OST_COMPARE_LIGAND_STRUCTURE = r""" ost compare-ligand-structures \ -m {model_file} \ -r {reference_file} \ --fault-tolerant \ --lddt-pli --rmsd \ -o {output_path} """ OST_COMPARE_STRUCTURE = r""" ost compare-structures \ -m {model_file} \ -r {reference_file} \ -o {output_path} \ --fault-tolerant \ --min-pep-length 4 \ --min-nuc-length 4 \ --lddt --rigid-scores --tm-score --dockq \ """ def get_structure_value(output_path,native_chain_id_1,native_chain_id_2): # Load the CSV file dockq = None irmsd = None lrmsd = None len_dockq = None lddt = None tm_score = None gdt_ts = None rmsd = None try: with open(output_path, 'r') as f: data = json.load(f) for i,interface in enumerate(data['dockq_interfaces']): if native_chain_id_1 in interface and native_chain_id_2 in interface: dockq = data['dockq'][i] irmsd = data['irmsd'][i] lrmsd = data['lrmsd'][i] # breakpoint() len_dockq = len(data['dockq']) lddt = data['lddt'] tm_score = data['tm_score'] gdt_ts = data['oligo_gdtts'] rmsd = data['rmsd'] except FileNotFoundError: print(f"FileNotFoundError: {output_path}") # Return None if the file does not exist finally: return dockq,irmsd,lrmsd,len_dockq,lddt,tm_score,gdt_ts,rmsd def get_ligand_value(output_path,native_chain_id_1,native_chain_id_2): # Load the CSV file rmsd = None lddt_lp = None lddt_pli = None try: with open(output_path, 'r') as f: data = json.load(f) for item in data['rmsd']['assigned_scores']: reference_ligand_name = item['reference_ligand'] # native_chain_id_2 is ligand by default if native_chain_id_2 == reference_ligand_name.split('.')[0]: rmsd = item['score'] lddt_lp = item['lddt_lp'] for item in data['lddt_pli']['assigned_scores']: reference_ligand_name = item['reference_ligand'] # native_chain_id_2 is ligand by default if native_chain_id_2 == reference_ligand_name.split('.')[0]: lddt_pli = item['score'] except FileNotFoundError: print(f"FileNotFoundError: {output_path}") # Return None if the file does not exist finally: return rmsd,lddt_lp,lddt_pli def evaluate_structure( pred, reference, outdir: str, executable: str = "/bin/bash", mode = 'ligand' # all, structure, ligand ) -> None: """Evaluate the structure.""" if mode == 'ligand': result = subprocess.run( OST_COMPARE_LIGAND_STRUCTURE.format( model_file=str(pred), reference_file=str(reference), output_path=os.path.join(outdir), ), shell=True, # noqa: S602 check=False, executable=executable, capture_output=True, ) elif mode == 'structure': result = subprocess.run( OST_COMPARE_STRUCTURE.format( model_file=str(pred), reference_file=str(reference), output_path=os.path.join(outdir), ), shell=True, # noqa: S602 check=False, executable=executable, capture_output=True, ) return result def ost_evaluation(args): row, ground_truth_path, detail_path, mode= args pdb_id = row["pdb_id"] seed = row["seed"] sample = row["sample"] prediction_path = row["prediction_path"] output_path = f'{detail_path}/{pdb_id}_{seed}_{sample}_{mode}_ost.json' if os.path.exists(output_path): return "exist" if not os.path.exists(prediction_path): print(f"prediction_path is None for {pdb_id} with seed {seed} and sample {sample}") return "prediction_path is None" native_path = os.path.join(ground_truth_path, f'{pdb_id}.cif') try: evaluate_structure( pred = prediction_path, reference = native_path, outdir = output_path, mode = mode ) except BaseException as e: print(f"Error when calculating dockq for {pdb_id} with seed {seed} and sample {sample}") print(traceback.format_exc()) return "Error when calculating dockq" def ost_get_result(args): row, ground_truth_path, detail_path, mode= args pdb_id = row["pdb_id"] if 'interface_chain_id_1' in row.keys() and 'interface_chain_id_2' in row.keys(): interface_chain_id_1 = row["interface_chain_id_1"] interface_chain_id_2 = row["interface_chain_id_2"] elif 'native_chain_id_1' in row.keys() and 'native_chain_id_2' in row.keys(): interface_chain_id_1 = row["native_chain_id_1"] interface_chain_id_2 = row["native_chain_id_2"] elif 'chain_id' in row.keys(): interface_chain_id_1 = row["chain_id"] interface_chain_id_2 = row["chain_id"] seed = row["seed"] sample = row["sample"] prediction_path = row["prediction_path"] output_path = f'{detail_path}/{pdb_id}_{seed}_{sample}_{mode}_ost.json' if not os.path.exists(prediction_path): print(f"prediction_path is None for {pdb_id} with seed {seed} and sample {sample}") return "prediction_path is None" result = { **row, } try: if mode == 'ligand': rmsd,lddt_lp, lddt_pli = get_ligand_value(output_path,interface_chain_id_1,interface_chain_id_2) result.update({ 'rmsd': rmsd, 'lddt-lp': lddt_lp, 'lddt-pli': lddt_pli, }) elif mode == 'structure': dockq_ost,irmsd,lrmsd,len_dockq,lddt,tm_score,gdt_ts,rmsd = get_structure_value(output_path,interface_chain_id_1,interface_chain_id_2) result.update({ 'dockq_score': dockq_ost, 'irmsd':irmsd, 'lrmsd':lrmsd, 'len_dockq':len_dockq, 'lddt':lddt, 'tm_score':tm_score, 'gdt_ts':gdt_ts, 'rmsd':rmsd }) return result except BaseException as e: print(f"Error when calculating dockq for {pdb_id} with seed {seed} and sample {sample}") print(traceback.format_exc()) return None def eval_by_ost(target_df,target_type,evaluation_dir,ground_truth_dir,max_workers = 64): detail_path = os.path.join(evaluation_dir, 'detail') if not os.path.exists(detail_path): os.makedirs(detail_path) mode = '' if target_type in ["interface_protein_protein","interface_antibody_antigen","interface_protein_peptide","interface_protein_dna","interface_protein_rna","monomer_dna","monomer_rna","monomer_protein"]: mode = "structure" elif target_type == "interface_protein_ligand": mode = "ligand" tasks = [] for index, row in target_df.iterrows(): tasks.append(( row, ground_truth_dir, detail_path, mode )) results_ost = [] # evaluation by ost with ThreadPoolExecutor(max_workers=max_workers) as executor: future_to_task = {executor.submit(ost_evaluation , task): task for task in tasks} for future in tqdm(as_completed(future_to_task), total=len(tasks)): try: result = future.result(timeout=20) if result is not None: results_ost.append(result) except TimeoutError: print("this took too long...") task = future_to_task[future] future.cancel() except Exception as e: task = future_to_task[future] print(f"Error occurred for task: {task}") print(traceback.format_exc()) future.cancel() results = [] with ThreadPoolExecutor(max_workers=max_workers) as executor: future_to_task = {executor.submit(ost_get_result, task): task for task in tasks} for future in tqdm(as_completed(future_to_task), total=len(tasks)): try: result = future.result(timeout=20) if result is not None: results.append(result) except TimeoutError: print("this took too long...") task = future_to_task[future] future.cancel()# except Exception as e: task = future_to_task[future] print(f"Error occurred for task: {task}") print(traceback.format_exc()) future.cancel() print(f"Total results for {target_type}: {len(results)}") df = pd.DataFrame(results) df.to_csv(os.path.join(evaluation_dir,'raw',f"{target_type}_ost.csv"), index=False)