tooluniverse-structural-proteomics
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ChineseStructural Proteomics for Drug Target Validation
用于药物靶点验证的结构蛋白质组学
Comprehensive structural data integration using ToolUniverse tools across PDB, AlphaFold, GPCRdb, SAbDab, and proteomics databases for drug target validation.
通过ToolUniverse工具整合PDB、AlphaFold、GPCRdb、SAbDab及蛋白质组学数据库的全面结构数据,以实现药物靶点验证。
LOOK UP DON'T GUESS
有据可依,勿凭空猜测
- PDB structures/resolutions: and
PDBeSIFTS_get_best_structuresRCSBGraphQL_get_structure_summary - AlphaFold confidence:
alphafold_get_summary - Ligands/affinities: and
PDBe_get_structure_ligandsBindingDB_get_ligands_by_uniprot - Druggability:
ProteinsPlus_predict_binding_sites
- PDB结构/分辨率:和
PDBeSIFTS_get_best_structuresRCSBGraphQL_get_structure_summary - AlphaFold置信度:
alphafold_get_summary - 配体/亲和力:和
PDBe_get_structure_ligandsBindingDB_get_ligands_by_uniprot - 成药性:
ProteinsPlus_predict_binding_sites
COMPUTE, DON'T DESCRIBE
执行计算,勿仅描述
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
当分析需要计算(统计、数据处理、评分、富集分析)时,通过Bash编写并运行Python代码。不要描述你会怎么做——直接执行并报告实际结果。使用ToolUniverse工具获取数据,再通过Python(pandas、scipy、statsmodels、matplotlib)进行分析。
Domain Reasoning
领域推理
Resolution determines valid conclusions: <2A = atom positions visible; 2-3A = side chains reliable, drug design supported; >3A = backbone only, binding site unreliable. Do not over-interpret low-resolution structures.
分辨率决定结论的有效性:<2Å = 可观测原子位置;2-3Å = 侧链可靠,支持药物设计;>3Å = 仅可见主链,结合位点不可靠。请勿过度解读低分辨率结构。
Tool Inventory
工具清单
PDB (RCSB)
PDB (RCSB)
RCSBAdvSearch_search_structuresRCSBData_get_entryRCSBGraphQL_get_structure_summaryRCSBGraphQL_get_ligand_infoRCSB_get_chemical_componentRCSBAdvSearch_search_structuresRCSBData_get_entryRCSBGraphQL_get_structure_summaryRCSBGraphQL_get_ligand_infoRCSB_get_chemical_componentPDB (PDBe)
PDB (PDBe)
pdbe_get_entry_summaryPDBe_get_structure_ligandsPDBe_get_bound_moleculesPDBeSearch_search_structuresPDBeSIFTS_get_best_structuresPDBeSIFTS_get_all_structuresPDBe_KB_get_ligand_sitesPDBe_KB_get_interface_residuesPDBeValidation_get_quality_scorespdbe_get_entry_summaryPDBe_get_structure_ligandsPDBe_get_bound_moleculesPDBeSearch_search_structuresPDBeSIFTS_get_best_structuresPDBeSIFTS_get_all_structuresPDBe_KB_get_ligand_sitesPDBe_KB_get_interface_residuesPDBeValidation_get_quality_scoresPDBe PISA
PDBe PISA
PDBePISA_get_interfacesPDBePISA_get_assembliesPDBePISA_get_interfacesPDBePISA_get_assembliesAlphaFold
AlphaFold
alphafold_get_predictionalphafold_get_summaryalphafold_get_annotationsalphafold_get_predictionalphafold_get_summaryalphafold_get_annotationsBinding Sites
结合位点
ProteinsPlus_predict_binding_sitesBindingDB_get_ligands_by_uniprotBindingDB_get_ligands_by_pdbBindingDB_get_targets_by_compoundProteinsPlus_predict_binding_sitesBindingDB_get_ligands_by_uniprotBindingDB_get_ligands_by_pdbBindingDB_get_targets_by_compoundFoldseek
Foldseek
Foldseek_search_structureFoldseek_get_resultFoldseek_search_structureFoldseek_get_resultGPCRdb
GPCRdb
GPCRdb_get_proteinGPCRdb_get_structuresGPCRdb_get_ligandsGPCRdb_get_mutations{symbol.lower()}_humanGPCRdb_get_proteinGPCRdb_get_structuresGPCRdb_get_ligandsGPCRdb_get_mutations{symbol.lower()}_humanSAbDab
SAbDab
SAbDab_search_structuresSAbDab_get_structureTheraSAbDab_search_therapeuticsTheraSAbDab_search_by_targetSAbDab_search_structuresSAbDab_get_structureTheraSAbDab_search_therapeuticsTheraSAbDab_search_by_targetDomains
结构域
InterPro_get_protein_domainsPfam_get_protein_annotationsUniProt_get_entry_by_accessionInterPro_get_protein_domainsPfam_get_protein_annotationsUniProt_get_entry_by_accessionProteomics
蛋白质组学
ProteomeXchange_search_datasetsProteomeXchange_get_datasetProteomeXchange_search_datasetsProteomeXchange_get_datasetWorkflow 1: Find All Structures for a Drug Target
工作流1:查找药物靶点的所有结构
Phase 0: Resolve protein → UniProt ID, gene symbol, organism
Phase 1: PDBeSIFTS_get_best_structures → RCSBGraphQL_get_structure_summary → PDBeValidation
Phase 2: alphafold_get_prediction/summary → compare pLDDT with experimental coverage
Phase 3: IF GPCR → GPCRdb; IF antibody target → SAbDab/TheraSAbDab
Phase 4: InterPro/Pfam domain mapping → identify unresolved regions
Phase 5: Summary table (PDB ID, method, resolution, ligands, coverage, quality)Decisions: Resolution <2.5A for drug design. X-ray > Cryo-EM > NMR > AlphaFold for binding sites. Holo > apo structures.
Phase 0: 解析蛋白质 → UniProt ID、基因符号、物种
Phase 1: PDBeSIFTS_get_best_structures → RCSBGraphQL_get_structure_summary → PDBeValidation
Phase 2: alphafold_get_prediction/summary → 对比pLDDT与实验覆盖范围
Phase 3: 若为GPCR → GPCRdb;若为抗体靶点 → SAbDab/TheraSAbDab
Phase 4: InterPro/Pfam结构域映射 → 识别未解析区域
Phase 5: 汇总表格(PDB ID、方法、分辨率、配体、覆盖范围、质量)决策依据:药物设计需分辨率<2.5Å。结合位点优先级:X射线 > 冷冻电镜 > NMR > AlphaFold。结合态结构 > apo结构。
Workflow 2: Identify Binding Pocket Ligands
工作流2:识别结合口袋配体
Phase 1: PDBe_get_structure_ligands + RCSBGraphQL_get_ligand_info + PDBe_KB_get_ligand_sites
Phase 2: ProteinsPlus_predict_binding_sites → druggability score, pocket residues
Phase 3: BindingDB_get_ligands_by_pdb/uniprot → Ki, Kd, IC50
Phase 4: RCSB_get_chemical_component for key ligandsFilter artifacts: GOL, EDO, SO4, PEG, ACT, CL, NA. Keep cofactors (ATP, NAD, HEM) and catalytic metals (ZN, MG) if relevant.
Phase 1: PDBe_get_structure_ligands + RCSBGraphQL_get_ligand_info + PDBe_KB_get_ligand_sites
Phase 2: ProteinsPlus_predict_binding_sites → 成药性评分、口袋残基
Phase 3: BindingDB_get_ligands_by_pdb/uniprot → Ki、Kd、IC50
Phase 4: RCSB_get_chemical_component 获取关键配体信息过滤人工产物:过滤GOL、EDO、SO4、PEG、ACT、CL、NA。若相关则保留辅因子(ATP、NAD、HEM)和催化金属(ZN、MG)。
Workflow 3: Cross-Validate Drug Binding
工作流3:交叉验证药物结合
Phase 1: Find co-crystal structures → filter for drug/analogs
Phase 2: BindingDB affinity data (Ki, Kd, IC50)
Phase 3: ProteinsPlus + PDBe-KB binding site characterization
Phase 4: PDBeValidation quality → binding site well-resolved?
Phase 5: AlphaFold + Foldseek structural comparison
Phase 6: GPCR-specific (if applicable) → active/inactive states, pharmacology, resistance mutations
Phase 7: Antibody-specific (if applicable) → epitope mapping
Phase 8: Evidence integrationPhase 1: 查找共晶结构 → 筛选药物/类似物
Phase 2: BindingDB亲和力数据(Ki、Kd、IC50)
Phase 3: ProteinsPlus + PDBe-KB结合位点特征分析
Phase 4: PDBeValidation质量评估 → 结合位点是否解析良好?
Phase 5: AlphaFold + Foldseek结构对比
Phase 6: 若为GPCR特异性 → 活性/非活性状态、药理学、耐药突变
Phase 7: 若为抗体特异性 → 表位定位
Phase 8: 整合证据Tool Parameter Gotchas
工具参数注意事项
| Tool | Mistake | Correct |
|---|---|---|
| | |
| | |
| gene symbol | |
| | |
| | |
| | |
| 工具 | 常见错误 | 正确用法 |
|---|---|---|
| 使用 | 使用 |
| 使用 | 使用 |
| 使用基因符号 | 使用 |
| 使用 | 使用 |
| 使用 | 使用 |
| 使用 | 使用 |
Evidence Grading
证据分级
| Tier | Confidence |
|---|---|
| T1 | Co-crystal (<2.5A) + binding affinity data |
| T2 | Experimental structure + computational prediction |
| T3 | AlphaFold + pocket analysis + known ligand analogs |
| T4 | Homology model or low-resolution only |
| 等级 | 置信度 |
|---|---|
| T1 | 共晶结构(<2.5Å)+ 结合亲和力数据 |
| T2 | 实验结构 + 计算预测 |
| T3 | AlphaFold + 口袋分析 + 已知配体类似物 |
| T4 | 仅同源模型或低分辨率结构 |
Interpretation
解读标准
| Metric | High | Acceptable | Caution |
|---|---|---|---|
| Resolution | <2.0A (X-ray) / <3.0A (cryo-EM) | 2.0-2.5A / 3.0-4.0A | >3.0A / >4.5A |
| R-free | <0.25 | 0.25-0.30 | >0.30 |
| AlphaFold pLDDT | >90 | 70-90 | <70 (disordered) |
DoGSiteScorer >0.6 = druggable; <0.4 = unlikely druggable. PISA assemblies should be cross-validated with SEC-MALS/native MS.
| 指标 | 优秀 | 可接受 | 需谨慎 |
|---|---|---|---|
| 分辨率 | <2.0Å(X射线)/ <3.0Å(冷冻电镜) | 2.0-2.5Å / 3.0-4.0Å | >3.0Å / >4.5Å |
| R-free | <0.25 | 0.25-0.30 | >0.30 |
| AlphaFold pLDDT | >90 | 70-90 | <70(无序区域) |
DoGSiteScorer得分>0.6 = 具有成药性;<0.4 = 成药性低。PISA组装体需通过SEC-MALS/天然质谱交叉验证。
Limitations
局限性
- BindingDB: 60s+ for popular targets
- AlphaFold: lacks ligand context
- GPCRdb: Class A-F GPCRs only
- PDBePISA: is internal, not a public parameter
operation
- BindingDB:热门靶点查询需60秒以上
- AlphaFold:缺乏配体相关上下文
- GPCRdb:仅支持A-F类GPCR
- PDBePISA:为内部参数,非公开参数
operation