# Structure Prediction Boltz 2 > Structure prediction using Boltz-2, an open biomolecular structure predictor. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Predicting protein-ligand complexes, (4) Using local GPU resources. For protein complex binding affinity evaluation, use prodigy. - Skill: `pharmolix/structure-prediction-boltz-2` (Agent Skill) - Install (CLI): `npx skillmds@latest add pharmolix/structure-prediction-boltz-2` - Raw SKILL.md: https://api.skillmd.com/api/skills/pharmolix/structure-prediction-boltz-2/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - License: MIT - Author: pharmolix (https://skillmd.com/u/pharmolix) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/pharmolix/structure-prediction-boltz-2 --- # Boltz-2 Structure Prediction ## Prerequisites | Requirement | Minimum | Recommended | |-------------|---------|-------------| | Python | 3.10+ | 3.10 | | CUDA | 12.0+ | 12.2 | | GPU VRAM | 24GB | 80GB (A800) | | RAM | 32GB | 64GB | ## How to run ### Local installation ```bash pip install boltz[cuda] -U -i ``` ### Predict protein complex structure ```python import os, yaml, subprocess def predict_protein_complex_structure(sequence_1, sequence_2, project_dir): """ :param sequence_1: sequence of the first protein :param sequence_2: sequence of the second protein :param project_dir: path to the project :return structure of the protein complex in PDB format """ # init project dir os.makedirs(project_dir, exist_ok=True) log_file = os.join(project_dir, 'log.txt') # init input yaml file data = { "sequences": [ { "protein": { "id": "A", "sequence": sequence_1, "msa": "empty" } }, { "protein": { "id": "B", "sequence": sequence_2, "msa": "empty" } }, ] } input_file = os.path.join(project_dir, "input.yaml") with open(input_file, "w") as f: yaml.dump(data, f) # init output file output_dir = os.path.join(project_dir, "boltz") # prediction command = [ 'boltz', 'predict', input_file, "--out_dir", output_dir, '--use_msa_server', '--output_format', "pdb", "--seed", "42" ] with open(log_file, 'a') as f: process = subprocess.Popen(command, stdout=f, stderr=f, env=self.env) process.communicate() process.terminate() try: process.wait(timeout=5) except subprocess.TimeoutExpired: process.kill() process.wait() # extract structure with open(os.path.join(output_dir, "boltz_results_input", "predictions", "input", "input_model_0.pdb"), 'r') as f: pred_struc = f.read() return pred_struc # Predict protein complex structure for sequence_1 and sequence_2 # pred_structure is the structure prediction in PDB format pred_structure = predict_protein_complex_structure(sequence_1, sequence_2, project_dir) ``` ### Predict protein ligand complex structure and IC50 ```python import os, yaml, subprocess def predict_protein_ligand_complex_affinity(sequence, smiles, project_dir): """ :param sequence: sequence of the first protein :param smiles: SMILES string of the ligand :param project_dir: path to the project :return pred_struct: structure of protein-ligand complex in PDB format :return pred_ic50: binding affinity prediction of the protein-ligand complex """ # init project dir os.makedirs(project_dir, exist_ok=True) log_file = os.join(project_dir, 'log.txt') # init input yaml file data = { "sequences": [ { "protein": { "id": "A", "sequence": sequence, "msa": "empty" } }, { "ligand":{ "id": "B", "smiles": smiles, } } ], "properties": [ { "affinity":{ "binder": "B" } } ] } input_file = os.path.join(project_dir, "input.yaml") with open(input_file, "w") as f: yaml.dump(data, f) # init output file output_dir = os.path.join(project_dir, "boltz") # prediction command = [ 'boltz', 'predict', input_file, "--out_dir", output_dir, '--use_msa_server', '--output_format', "pdb", "--seed", "42" ] with open(log_file, 'a') as f: process = subprocess.Popen(command, stdout=f, stderr=f, env=self.env) process.communicate() process.terminate() try: process.wait(timeout=5) except subprocess.TimeoutExpired: process.kill() process.wait() # extract affinity with open(os.path.join(output_dir, "boltz_results_input", "predictions", "input", "affinity_input.json"), 'r') as f: pred_ic50 = (6 - json.load(f)["affinity_pred_value"]) * 1.364 # extract structure with open(os.path.join(output_dir, "boltz_results_input", "predictions", "input", "input_model_0.pdb"), 'r') as f: pred_struc = f.read() return pred_struc, pred_ic50 # Predict protein-ligand complex structure and the corresponding binding affinity (IC50) # pred_structure is the structure prediction in PDB format # pred_ic50 is the binding affinity prediction in IC50 format pred_structure, pred_ic50 = predict_protein_ligand_complex_affinity(sequence, smiles, project_dir): ``` ## Output format ### Protein complex structure prediction ``` project_dir/boltz/boltz_results_input/predictions/input/ ├── input_model_0.pdb # structure prediction (PDB format) ├── confidence_input_model_0.json # pTM, ipTM ├── pae_input_model_0.npz # PAE matrix └── plddt_input_model_0.npz # pLDDT matrix ``` ### Protein-ligand complex structure prediction ``` project_dir/boltz/boltz_results_input/predictions/input/ ├── input_model_0.pdb # structure prediction (PDB format) └── affinity_input.json # affinity_pred_value ``` ## Decision tree ``` Should I use Boltz-2? │ └─ What are you predicting? ├─ Structure prediction for general protein-protein complex → boltz-2 ✓ ├─ Structure prediction for protein-ligand complex → boltz-2 ✓ ├─ Antibody and nanobody structure prediction → tfold └─ Antigen-antibody structure prediction → tfold ``` ## Typical performance | Campaign Size | Time (L40S) | Cost (Modal) | Notes | |---------------|-------------|--------------|-------| | 100 complexes | 30-45 min | ~$8 | Standard validation | | 500 complexes | 2-3h | ~$35 | Large campaign | | 1000 complexes | 4-6h | ~$70 | Comprehensive | **Per-complex**: ~15-30s for typical binder-target complex. **Next**: Evaluate protein complex binding affinity with `prodigy`.