Please cite ColabFold: Making protein folding accessible to all. Nature Methods (2022) if you use these predictions. 21:43:05 Starting prediction on 2022-07-23 UTC time 21:43:05 Installing ColabFold on Google Colab virtual machine. Installing ColabFold Downloading alphafold2 weights to .: 100%|██████████| 3.47G/3.47G [00:19<00:00, 190MB/s] 21:44:16 Query 1/1: af1436 (length 1436) PENDING: 0%| | 0/150 [elapsed: 00:00 remaining: ?]21:44:16 Sleeping for 8s. Reason: PENDING RUNNING: 5%|▌ | 8/150 [elapsed: 00:08 remaining: 02:36]21:44:24 Sleeping for 7s. Reason: RUNNING RUNNING: 10%|█ | 15/150 [elapsed: 00:16 remaining: 02:25]21:44:32 Sleeping for 8s. Reason: RUNNING RUNNING: 15%|█▌ | 23/150 [elapsed: 00:24 remaining: 02:15]21:44:40 Sleeping for 9s. Reason: RUNNING RUNNING: 21%|██▏ | 32/150 [elapsed: 00:34 remaining: 02:04]21:44:50 Sleeping for 10s. Reason: RUNNING RUNNING: 28%|██▊ | 42/150 [elapsed: 00:44 remaining: 01:53]21:45:00 Sleeping for 9s. 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[elapsed: 04:25 remaining: 00:00] 21:48:51 Running model_1 22:16:32 model_1 took 1635.8s (3 recycles) with pLDDT 79.5 and ptmscore 0.645 22:18:32 Running model_2 22:44:32 model_2 took 1537.2s (3 recycles) with pLDDT 78.2 and ptmscore 0.684 22:46:33 Running model_3 --------------------------------------------------------------------------- ValueError Traceback (most recent call last) in () 393 remove_from_list(seq_list, 'prokaryote') # Obsolete "prokaryote" flag 394 --> 395 run_prediction(seq_list, use_templates = use_templates, energy_minimize = not dont_minimize) 3 frames in run_prediction(sequences, job_name, msa_mode, pair_mode, use_templates, custom_template_path, energy_minimize, model_type, num_recycles, dpi, install_log) 90 stop_at_score=100.0, 91 prediction_callback=prediction_callback(minimized_only=use_amber), ---> 92 dpi=dpi 93 ) 94 /usr/local/lib/python3.7/dist-packages/colabfold/batch.py in run(queries, result_dir, num_models, num_recycles, model_order, is_complex, num_ensemble, model_type, msa_mode, use_templates, custom_template_path, use_amber, keep_existing_results, rank_by, pair_mode, data_dir, host_url, random_seed, stop_at_score, recompile_padding, recompile_all_models, zip_results, prediction_callback, save_single_representations, save_pair_representations, training, use_gpu_relax, stop_at_score_below, dpi, max_msa) 1387 prediction_callback=prediction_callback, 1388 use_gpu_relax=use_gpu_relax, -> 1389 random_seed=random_seed, 1390 ) 1391 except RuntimeError as e: /usr/local/lib/python3.7/dist-packages/colabfold/batch.py in predict_structure(prefix, result_dir, feature_dict, is_complex, use_templates, sequences_lengths, crop_len, model_type, model_runner_and_params, do_relax, rank_by, random_seed, stop_at_score, stop_at_score_below, prediction_callback, use_gpu_relax) 357 # The original alphafold only returns the prediction_result, 358 # but our patched alphafold also returns a tuple (recycles,tol) --> 359 prediction_result, recycles = model_runner.predict(input_features) 360 361 prediction_time = time.time() - start /usr/local/lib/python3.7/dist-packages/alphafold/model/model.py in predict(self, feat, random_seed) 192 193 sub_feat["prev"] = result["prev"] --> 194 result, _ = self.apply(self.params, key, sub_feat) 195 confidences = get_confidence_metrics(result, multimer_mode=self.multimer_mode) 196 if self.config.model.stop_at_score_ranker == "plddt": ValueError: INTERNAL: Failed to launch CUDA kernel: fusion_92 with block dimensions: 256x1x1 and grid dimensions: 64441x1x1: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered