# Scientific Alphafold Structures > AlphaFold 構造予測スキル。AlphaFold Protein Structure Database REST API による予測構造取得・pLDDT 信頼度解析・PAE 残基間 距離予測・構造カバレッジ分析。ToolUniverse 連携: alphafold。 - Skill: `nahisaho/scientific-alphafold-structures-2` (Agent Skill) - Install (CLI): `npx skillmds@latest add nahisaho/scientific-alphafold-structures-2` - Raw SKILL.md: https://api.skillmd.com/api/skills/nahisaho/scientific-alphafold-structures-2/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Integrations & APIs - Author: nahisaho (https://skillmd.com/u/nahisaho) - Updated: 2026-09-21 - Page: https://skillmd.com/skills/nahisaho/scientific-alphafold-structures-2 --- # Scientific AlphaFold Structures AlphaFold Protein Structure Database REST API を活用した 構造予測取得・信頼度解析パイプラインを提供する。 ## When to Use - UniProt ID から AlphaFold 予測構造を取得するとき - pLDDT スコアで構造信頼度を評価するとき - PAE (Predicted Aligned Error) で残基間予測精度を分析するとき - 構造カバレッジ (モデル化割合) を確認するとき - AlphaFold 構造を実験構造と比較するとき - 大規模プロテオーム構造データをバッチ取得するとき --- ## Quick Start ## 1. AlphaFold 予測構造取得 ```python import requests import pandas as pd import numpy as np from io import StringIO AFDB_BASE = "https://alphafold.ebi.ac.uk/api" def alphafold_get_prediction(uniprot_id, version=4): """ AlphaFold DB — 予測構造取得。 Parameters: uniprot_id: str — UniProt アクセッション (例: "P00533") version: int — AlphaFold モデルバージョン """ url = f"{AFDB_BASE}/prediction/{uniprot_id}" resp = requests.get(url, timeout=30) resp.raise_for_status() entries = resp.json() if not entries: print(f"No AlphaFold prediction for {uniprot_id}") return None entry = entries[0] if isinstance(entries, list) else entries result = { "uniprot_id": uniprot_id, "entry_id": entry.get("entryId", ""), "gene": entry.get("gene", ""), "organism": entry.get("organismScientificName", ""), "tax_id": entry.get("taxId", ""), "sequence_length": entry.get("uniprotEnd", 0) - entry.get("uniprotStart", 0) + 1, "model_url": entry.get("cifUrl", ""), "pdb_url": entry.get("pdbUrl", ""), "pae_url": entry.get("paeImageUrl", ""), "global_plddt": entry.get("globalMetricValue", None), "model_version": entry.get("latestVersion", version), } print(f"AlphaFold {uniprot_id}: pLDDT={result['global_plddt']}, " f"length={result['sequence_length']}") return result ``` ## 2. pLDDT 信頼度プロファイル解析 ```python import biotite.structure.io.pdbx as pdbx import biotite.structure as struc def alphafold_plddt_profile(uniprot_id, output_dir="results"): """ AlphaFold — pLDDT 残基別信頼度プロファイル。 Parameters: uniprot_id: str — UniProt アクセッション output_dir: str — 出力ディレクトリ """ from pathlib import Path output_dir = Path(output_dir) output_dir.mkdir(parents=True, exist_ok=True) # CIF 取得 pred = alphafold_get_prediction(uniprot_id) if not pred: return pd.DataFrame() cif_url = pred["model_url"] resp = requests.get(cif_url, timeout=60) resp.raise_for_status() cif_path = output_dir / f"AF-{uniprot_id}.cif" cif_path.write_bytes(resp.content) # pLDDT = B-factor in AlphaFold structures pdbx_file = pdbx.CIFFile.read(str(cif_path)) structure = pdbx.get_structure(pdbx_file, model=1) ca_mask = structure.atom_name == "CA" ca_atoms = structure[ca_mask] residues = [] for i, atom in enumerate(ca_atoms): residues.append({ "residue_index": i + 1, "residue_name": atom.res_name, "chain": atom.chain_id, "plddt": atom.b_factor, }) df = pd.DataFrame(residues) # 信頼度カテゴリ df["confidence"] = pd.cut( df["plddt"], bins=[0, 50, 70, 90, 100], labels=["Very low", "Low", "Confident", "Very high"], ) n_high = (df["plddt"] >= 70).sum() print(f"pLDDT profile {uniprot_id}: {len(df)} residues, " f"{n_high}/{len(df)} confident (≥70)") return df ``` ## 3. PAE (Predicted Aligned Error) 解析 ```python def alphafold_pae_analysis(uniprot_id): """ AlphaFold — PAE マトリクス解析。 Parameters: uniprot_id: str — UniProt アクセッション """ url = (f"https://alphafold.ebi.ac.uk/files/" f"AF-{uniprot_id}-F1-predicted_aligned_error_v4.json") resp = requests.get(url, timeout=30) resp.raise_for_status() data = resp.json() pae_data = data[0] if isinstance(data, list) else data pae_matrix = np.array(pae_data.get("predicted_aligned_error", pae_data.get("pae", []))) # ドメイン境界推定 (低 PAE ブロック検出) n_res = pae_matrix.shape[0] mean_pae = pae_matrix.mean() low_pae_mask = pae_matrix < mean_pae * 0.5 # 行ごとの低 PAE 連続領域 domain_scores = [] for i in range(n_res): row = low_pae_mask[i] domain_scores.append(row.sum() / n_res) result = { "uniprot_id": uniprot_id, "pae_matrix_shape": pae_matrix.shape, "mean_pae": float(mean_pae), "median_pae": float(np.median(pae_matrix)), "min_pae": float(pae_matrix.min()), "max_pae": float(pae_matrix.max()), "domain_scores": domain_scores, } print(f"PAE {uniprot_id}: {n_res}x{n_res} matrix, " f"mean={mean_pae:.1f} Å") return result, pae_matrix ``` ## 4. AlphaFold 統合パイプライン ```python def alphafold_pipeline(uniprot_ids, output_dir="results"): """ AlphaFold 構造解析統合パイプライン。 Parameters: uniprot_ids: list[str] — UniProt IDs output_dir: str — 出力ディレクトリ """ from pathlib import Path output_dir = Path(output_dir) output_dir.mkdir(parents=True, exist_ok=True) all_predictions = [] all_plddt = [] for uid in uniprot_ids: # 1) 予測取得 pred = alphafold_get_prediction(uid) if pred: all_predictions.append(pred) # 2) pLDDT プロファイル plddt = alphafold_plddt_profile(uid, output_dir=output_dir) if not plddt.empty: plddt["uniprot_id"] = uid all_plddt.append(plddt) # サマリ pred_df = pd.DataFrame(all_predictions) pred_df.to_csv(output_dir / "predictions.csv", index=False) if all_plddt: plddt_df = pd.concat(all_plddt, ignore_index=True) plddt_df.to_csv(output_dir / "plddt_profiles.csv", index=False) print(f"AlphaFold pipeline: {len(all_predictions)} structures") return {"predictions": pred_df} ``` --- ## ToolUniverse 連携 | TU Key | ツール名 | 連携内容 | |--------|---------|---------| | `alphafold` | AlphaFold Database | 予測構造・pLDDT・PAE 取得 | ## パイプライン統合 ``` protein-structure-analysis → alphafold-structures → protein-design (PDB 実験構造) (AlphaFold 予測) (de novo 設計) │ │ ↓ structural-proteomics ─────────┘ molecular-docking (EMDB/PDBe) │ (結合予測) ↓ variant-effect-prediction (構造ベース変異評価) ``` ## パイプライン出力 | ファイル | 説明 | 次スキル | |---------|------|---------| | `results/predictions.csv` | 予測メタデータ | → protein-structure-analysis | | `results/plddt_profiles.csv` | 残基別 pLDDT | → protein-design | | `results/AF-*.cif` | 予測構造ファイル | → molecular-docking |