FirstData/firstdata/sources/academic/biology/alphafold-db.json at main · firstdata-dev/FirstData · GitHub

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{
  "id": "alphafold-db",
  "name": {
    "en": "AlphaFold Protein Structure Database",
    "zh": "AlphaFold蛋白质结构数据库"
  },
  "description": {
    "en": "AlphaFold Protein Structure Database provides open access to over 200 million protein structure predictions powered by AlphaFold AI system. Developed by Google DeepMind in partnership with EMBL's European Bioinformatics Institute (EMBL-EBI), it offers nearly all catalogued proteins known to science with unprecedented accuracy and speed. The database enables scientists to understand protein structures computationally, dramatically accelerating biological research, drug discovery, and our understanding of life itself.",
    "zh": "AlphaFold蛋白质结构数据库提供超过2亿个由AlphaFold人工智能系统预测的蛋白质结构的开放访问。由Google DeepMind与EMBL欧洲生物信息学研究所(EMBL-EBI)合作开发，涵盖几乎所有已知科学的蛋白质，具有前所未有的准确性和速度。该数据库使科学家能够通过计算理解蛋白质结构，极大地加速生物学研究、药物发现以及对生命本身的理解。"
  },
  "website": "https://www.ebi.ac.uk",
  "data_url": "https://alphafold.com",
  "api_url": "https://alphafold.com/api-docs",
  "country": null,
  "domains": [
    "structural biology",
    "proteomics",
    "drug discovery",
    "molecular biology",
    "biotechnology",
    "bioinformatics"
  ],
  "geographic_scope": "global",
  "update_frequency": "irregular",
  "tags": [
    "protein structure",
    "structural biology",
    "AI prediction",
    "machine learning",
    "proteomics",
    "drug discovery",
    "bioinformatics",
    "AlphaFold",
    "DeepMind",
    "EMBL-EBI",
    "computational biology",
    "3D structure",
    "protein folding"
  ],
  "data_content": {
    "en": [
      "Protein structure predictions for over 200 million proteins",
      "Human proteome (23,586 structures)",
      "Model organism proteomes (48 organisms)",
      "Global health proteomes (30 organisms)",
      "Swiss-Prot curated proteins (550,122 structures)",
      "Predicted Aligned Error (PAE) data",
      "Per-residue confidence scores (pLDDT)",
      "AlphaMissense pathogenicity predictions",
      "3D structural coordinates",
      "Sequence annotations",
      "UniProt cross-references"
    ],
    "zh": [
      "超过2亿个蛋白质的结构预测",
      "人类蛋白质组(23,586个结构)",
      "模式生物蛋白质组(48种生物)",
      "全球健康蛋白质组(30种生物)",
      "Swiss-Prot精选蛋白质(550,122个结构)",
      "预测对齐误差(PAE)数据",
      "每残基置信度评分(pLDDT)",
      "AlphaMissense致病性预测",
      "3D结构坐标",
      "序列注释",
      "UniProt交叉引用"
    ]
  },
  "authority_level": "international"
}