tooluniverse-electron-microscopy

Compare original and translation side by side

🇺🇸

Original

English

🇨🇳

Translation

Chinese

Electron Microscopy Structure Analysis

电子显微镜结构分析

Pipeline for discovering and analyzing electron microscopy data across the full resolution spectrum: from 3D density maps (EMDB) to fitted atomic models (PDB), raw micrograph datasets (EMPIAR), and cryo-electron tomography volumes (CryoET Data Portal). Connects EM data to structural biology context via PDB and AlphaFold.

Guiding principles:

Resolution awareness -- always report and interpret map resolution; sub-4A enables atomic modeling, 4-8A enables domain fitting, >8A is shape-level
Map before model -- the density map is the primary experimental data; fitted models are interpretations
Method matters -- single particle analysis, tomography, 2D crystallography, and helical reconstruction have different strengths and limitations
Raw data value -- EMPIAR raw data enables reprocessing with newer algorithms; always note availability
Cross-reference structures -- connect EMDB maps to PDB entries and AlphaFold predictions for completeness
English-first queries -- use English terms in tool calls

EM resolution determines what you can see. TEM resolves individual protein complexes (~2nm). Cryo-EM achieves near-atomic resolution (<4Å) for large complexes. SEM shows surface topology. Choose the right EM modality for the question.

用于发现和分析全分辨率范围电子显微镜数据的流程：从3D密度图谱（EMDB）到拟合原子模型（PDB）、原始显微图像数据集（EMPIAR），再到冷冻电子断层扫描体积数据（CryoET Data Portal）。通过PDB和AlphaFold将电镜数据与结构生物学背景关联起来。

指导原则:

分辨率感知 —— 始终报告并解读图谱分辨率；低于4Å的分辨率支持原子建模，4-8Å支持结构域拟合，高于8Å仅能呈现形状层面信息
图谱优先于模型 —— 密度图谱是主要实验数据；拟合模型属于解读结果
方法决定特性 —— 单颗粒分析、断层扫描、2D晶体学和螺旋重构各有不同的优势与局限性
原始数据价值 —— EMPIAR原始数据支持使用更新算法重新处理；需始终注意数据可用性
结构交叉引用 —— 将EMDB图谱与PDB条目和AlphaFold预测结果关联，确保信息完整性
优先英文查询 —— 在工具调用中使用英文术语

电镜分辨率决定了可观测的内容：透射电子显微镜（TEM）可分辨单个蛋白质复合物（约2nm）；冷冻电镜（cryo-EM）对大型复合物可实现近原子分辨率（<4Å）；扫描电子显微镜（SEM）可呈现表面拓扑结构。需根据研究问题选择合适的电镜模式。

LOOK UP, DON'T GUESS

查资料，勿猜测

When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory. A database-verified answer is always more reliable than a guess.

当对任何科学事实不确定时，先搜索数据库，而非凭记忆推理。经数据库验证的答案永远比猜测更可靠。

COMPUTE, DON'T DESCRIBE

去计算，勿描述

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。不要描述你会做什么——直接执行并报告实际结果。使用ToolUniverse工具检索数据，再通过Python（pandas、scipy、statsmodels、matplotlib）进行分析。

When to Use

使用场景

Typical triggers:

"Find cryo-EM structures of [protein/complex]"
"What EMDB maps are available for [target]?"
"Get raw micrograph data for [structure]"
"Find tomography datasets for [organelle/cell type]"
"What is the resolution of [EMDB entry]?"
"Cross-reference this EM map with PDB models"
"Find cryo-ET datasets for [sample]"

Not this skill: For X-ray crystallography or NMR structures, use PDB search tools directly. For protein structure prediction, use

tooluniverse-protein-structure

典型触发需求：

"查找[蛋白质/复合物]的冷冻电镜结构"
"[目标物质]有哪些可用的EMDB图谱？"
"获取[结构]的原始显微图像数据"
"查找[细胞器/细胞类型]的断层扫描数据集"
"[EMDB条目]的分辨率是多少？"
"将该电镜图谱与PDB模型进行交叉引用"
"查找[样本]的冷冻电子断层扫描数据集"

不属于本技能范畴：如需X射线晶体学或NMR结构，直接使用PDB搜索工具；如需蛋白质结构预测，使用

tooluniverse-protein-structure

。

Core Databases

核心数据库

Database	Content	Best For
EMDB	3D EM density maps (>40K entries)	Finding processed maps, resolution data, fitting info
EMPIAR	Raw micrograph/tilt series datasets	Accessing original image data for reprocessing
CryoET Data Portal	Cryo-electron tomography data	Tomographic volumes, cellular context, in-situ structures
PDB (RCSB)	Atomic models fitted to EM maps	Structural models derived from EM data
AlphaFold	AI-predicted protein structures	Complementary models when EM resolution is limited

数据库	内容	适用场景
EMDB	3D电镜密度图谱（超过4万条条目）	查找已处理图谱、分辨率数据、拟合信息
EMPIAR	原始显微图像/倾斜序列数据集	获取用于重新处理的原始图像数据
CryoET Data Portal	冷冻电子断层扫描数据	断层扫描体积数据、细胞背景信息、原位结构
PDB (RCSB)	拟合到电镜图谱的原子模型	从电镜数据衍生的结构模型
AlphaFold	AI预测的蛋白质结构	当电镜分辨率有限时的补充模型

Workflow Overview

工作流程概述

Phase 0: Query Parsing
  Identify target protein/complex, method preference, resolution needs
    |
Phase 1: Map & Image Search (EMDB)
  Find EM density maps, resolution, method, sample details
    |
Phase 2: Structure Fitting (EMDB + PDB)
  Identify fitted atomic models, fitting quality
    |
Phase 3: Raw Data Access (EMPIAR)
  Find raw micrographs, tilt series, particle stacks
    |
Phase 4: Tomography (CryoET Data Portal)
  Search cryo-ET datasets, reconstructed volumes
    |
Phase 5: Cross-Reference & Context (PDB + AlphaFold)
  Connect to atomic models, predicted structures, literature
    |
Phase 6: Report Synthesis
  Integrated EM data landscape for the target

Phase 0: Query Parsing
  Identify target protein/complex, method preference, resolution needs
    |
Phase 1: Map & Image Search (EMDB)
  Find EM density maps, resolution, method, sample details
    |
Phase 2: Structure Fitting (EMDB + PDB)
  Identify fitted atomic models, fitting quality
    |
Phase 3: Raw Data Access (EMPIAR)
  Find raw micrographs, tilt series, particle stacks
    |
Phase 4: Tomography (CryoET Data Portal)
  Search cryo-ET datasets, reconstructed volumes
    |
Phase 5: Cross-Reference & Context (PDB + AlphaFold)
  Connect to atomic models, predicted structures, literature
    |
Phase 6: Report Synthesis
  Integrated EM data landscape for the target

Phase Details

各阶段详情

Phase 0: Query Parsing

Phase 0: 查询解析

Identify from the user's request:

Target: protein name, complex name, or organism
Method preference: single particle, tomography, micro-ED, helical
Resolution needs: atomic modeling (<4A), domain fitting (4-8A), shape (>8A)
Data type: processed maps, raw data, fitted models, or all

从用户请求中识别：

目标对象：蛋白质名称、复合物名称或生物体
方法偏好：单颗粒、断层扫描、微晶电子衍射（micro-ED）、螺旋重构
分辨率需求：原子建模（<4Å）、结构域拟合（4-8Å）、形状层面（>8Å）
数据类型：已处理图谱、原始数据、拟合模型或全部类型

Phase 1: Map & Image Search (EMDB)

Phase 1: 图谱与图像搜索（EMDB）

Objective: Find EM density maps matching the query.

Tools:

```
EMDB_search_structures
```
-- search EMDB by keyword, organism, resolution
- Input:
```
query
```
  (search term), optional
```
resolution_min
```
  ,
```
resolution_max
```
  ,
```
method
```
  ,
```
limit
```
- Output: entries with EMDB ID, title, resolution, method, sample
```
EMDB_get_structure
```
-- get full details for an EMDB entry
- Input:
```
emdb_id
```
  (e.g., "EMD-1234")
- Output: map details, resolution, sample, processing info, citations
```
EMDB_get_map_info
```
-- get map-specific info (resolution, contour, dimensions)
- Input:
```
emdb_id
```
```
EMDB_get_sample_info
```
-- get sample preparation details
- Input:
```
emdb_id
```

Workflow:

Search EMDB for the target protein/complex
Sort results by resolution (best first)
For top entries, get full details including sample preparation and processing
Note the EM method used (single particle, tomography, helical, etc.)
Record associated PDB and EMPIAR accessions

Resolution interpretation:

< 2.5A: near-atomic; side chains visible
2.5-4.0A: atomic; backbone and large side chains traceable
4.0-8.0A: domain level; secondary structure elements visible
8.0A: shape; overall architecture only

目标：查找匹配查询条件的电镜密度图谱。

工具:

```
EMDB_search_structures
```
-- 通过关键词、生物体、分辨率搜索EMDB
- 输入：
```
query
```
  （搜索词），可选参数
```
resolution_min
```
  、
```
resolution_max
```
  、
```
method
```
  、
```
limit
```
- 输出：包含EMDB ID、标题、分辨率、方法、样本信息的条目
```
EMDB_get_structure
```
-- 获取EMDB条目的完整详情
- 输入：
```
emdb_id
```
  （例如："EMD-1234"）
- 输出：图谱详情、分辨率、样本信息、处理信息、引用文献
```
EMDB_get_map_info
```
-- 获取图谱特定信息（分辨率、等高线、尺寸）
- 输入：
```
emdb_id
```
```
EMDB_get_sample_info
```
-- 获取样本制备详情
- 输入：
```
emdb_id
```

工作流程:

在EMDB中搜索目标蛋白质/复合物
按分辨率排序结果（优先高分辨率）
对排名靠前的条目，获取包括样本制备和处理流程的完整详情
记录所使用的电镜方法（单颗粒、断层扫描、螺旋重构等）
记录关联的PDB和EMPIAR编号

分辨率解读:

< 2.5Å：近原子分辨率；可见侧链
2.5-4.0Å：原子分辨率；可追踪主链和大型侧链
4.0-8.0Å：结构域层面；可见二级结构元件
8.0Å：形状层面；仅能呈现整体架构

Phase 2: Structure Fitting (EMDB + PDB)

Phase 2: 结构拟合（EMDB + PDB）

Objective: Find atomic models fitted into EM maps and assess fitting quality.

Tools:

```
EMDB_get_validation
```
-- get fitting/validation data for an EMDB entry
- Input:
```
emdb_id
```
- Output: fitted PDB models, fitting statistics, validation scores
```
RCSBData_get_entry
```
-- get PDB entry details
- Input:
```
entry_id
```
  (PDB ID)
- Output: structure details, resolution, method, citation
```
RCSBAdvSearch_search_structures
```
-- advanced PDB search
- Input:
```
query
```
  (search term), optional
```
experimental_method
```
  ,
```
resolution_max
```
  ,
```
limit
```
- Output: PDB entries matching criteria

Workflow:

For each EMDB entry from Phase 1, check for fitted atomic models
Get fitting statistics (cross-correlation, real-space R-factor if available)
Retrieve the PDB entry for structural details
If no model is fitted, search PDB for related structures by name

Fitting quality indicators:

Cross-correlation coefficient > 0.7 suggests reasonable fit
Multiple independently fitted models increase confidence
Map-model FSC consistency check validates the fit

目标：查找拟合到电镜图谱中的原子模型并评估拟合质量。

工具:

```
EMDB_get_validation
```
-- 获取EMDB条目的拟合/验证数据
- 输入：
```
emdb_id
```
- 输出：拟合的PDB模型、拟合统计数据、验证分数
```
RCSBData_get_entry
```
-- 获取PDB条目详情
- 输入：
```
entry_id
```
  （PDB ID）
- 输出：结构详情、分辨率、方法、引用文献
```
RCSBAdvSearch_search_structures
```
-- PDB高级搜索
- 输入：
```
query
```
  （搜索词），可选参数
```
experimental_method
```
  、
```
resolution_max
```
  、
```
limit
```
- 输出：符合条件的PDB条目

工作流程:

对Phase 1中找到的每个EMDB条目，检查是否存在拟合的原子模型
获取拟合统计数据（交叉相关性、实空间R因子（若可用））
检索PDB条目以获取结构详情
若未拟合模型，按名称在PDB中搜索相关结构

拟合质量指标:

交叉相关系数>0.7表示拟合效果合理
多个独立拟合的模型可提升置信度
图谱-模型FSC一致性检验可验证拟合效果

Phase 3: Raw Data Access (EMPIAR)

Phase 3: 原始数据获取（EMPIAR）

Objective: Locate raw micrograph data for potential reprocessing.

Tools:

```
EMPIAR_search_entries
```
-- search EMPIAR archive
- Input:
```
query
```
  (search term), optional
```
limit
```
- Output: entries with EMPIAR ID, title, data type, size
```
EMPIAR_get_entry
```
-- get detailed entry information
- Input:
```
empiar_id
```
  (e.g., "EMPIAR-10028")
- Output: data description, file formats, associated EMDB entries, download links

Workflow:

Search EMPIAR for entries related to the target
Cross-reference with EMDB entries found in Phase 1 (many EMDB entries link to EMPIAR)
Note data types: micrographs, particle stacks, tilt series, gain references
Record dataset size (can be 100s of GB to TBs)

Data types in EMPIAR:

Micrographs: raw detector frames or motion-corrected images
Particle stacks: extracted particle images
Tilt series: serial images at different tilt angles (for tomography)
Reconstructed volumes: 3D volumes from tomographic reconstruction

目标：定位可用于重新处理的原始显微图像数据。

工具:

```
EMPIAR_search_entries
```
-- 搜索EMPIAR档案
- 输入：
```
query
```
  （搜索词），可选参数
```
limit
```
- 输出：包含EMPIAR ID、标题、数据类型、大小的条目
```
EMPIAR_get_entry
```
-- 获取条目的详细信息
- 输入：
```
empiar_id
```
  （例如："EMPIAR-10028"）
- 输出：数据描述、文件格式、关联的EMDB条目、下载链接

工作流程:

在EMPIAR中搜索与目标相关的条目
与Phase 1中找到的EMDB条目进行交叉引用（许多EMDB条目链接到EMPIAR）
记录数据类型：显微图像、颗粒堆叠、倾斜序列、增益参考
记录数据集大小（可能从数百GB到数TB不等）

EMPIAR中的数据类型:

显微图像：原始探测器帧或经运动校正的图像
颗粒堆叠：提取的颗粒图像
倾斜序列：不同倾斜角度的连续图像（用于断层扫描）
重构体积：从断层扫描重构得到的3D体积数据

Phase 4: Tomography (CryoET Data Portal)

Phase 4: 断层扫描（CryoET Data Portal）

Objective: Find cryo-electron tomography datasets for cellular and in-situ structural biology.

Tools:

```
CryoET_list_datasets
```
-- search CryoET Data Portal
- Input:
```
query
```
  (search term), optional
```
organism
```
  ,
```
limit
```
- Output: datasets with ID, title, organism, sample type
```
CryoET_get_dataset
```
-- get dataset details
- Input:
```
dataset_id
```
- Output: sample details, tilt series parameters, tomogram info
```
CryoET_list_runs
```
-- search individual tomography runs
- Input:
```
dataset_id
```
  or
```
query
```
  , optional
```
limit
```
- Output: run details, tilt parameters, voxel spacing

Workflow:

Search CryoET Data Portal for the target organism/structure
Get dataset details including sample preparation and imaging parameters
Explore individual runs for tilt series specifications
Note voxel spacing and tomogram dimensions

Tomography vs single particle: Tomography preserves cellular context (in situ) but typically achieves lower resolution. Single particle gives higher resolution but requires purified samples.

目标：查找用于细胞和原位结构生物学研究的冷冻电子断层扫描数据集。

工具:

```
CryoET_list_datasets
```
-- 搜索CryoET Data Portal
- 输入：
```
query
```
  （搜索词），可选参数
```
organism
```
  、
```
limit
```
- 输出：包含ID、标题、生物体、样本类型的数据集
```
CryoET_get_dataset
```
-- 获取数据集详情
- 输入：
```
dataset_id
```
- 输出：样本详情、倾斜序列参数、断层扫描图像信息
```
CryoET_list_runs
```
-- 搜索单个断层扫描运行数据
- 输入：
```
dataset_id
```
  或
```
query
```
  ，可选参数
```
limit
```
- 输出：运行详情、倾斜参数、体素间距

工作流程:

在CryoET Data Portal中搜索目标生物体/结构
获取包括样本制备和成像参数的数据集详情
探索单个运行数据的倾斜序列规格
记录体素间距和断层扫描图像尺寸

断层扫描vs单颗粒分析：断层扫描保留细胞背景（原位）但通常分辨率较低；单颗粒分析分辨率更高但需要纯化样本。

Phase 5: Cross-Reference & Context

Phase 5: 交叉引用与背景关联

Objective: Connect EM data to broader structural biology context.

Tools:

```
alphafold_get_prediction
```
-- get AlphaFold predicted structure
- Input:
```
qualifier
```
  (UniProt accession)
- Output: predicted structure coordinates, confidence scores (pLDDT)
```
PubMed_search_articles
```
-- find publications describing the EM work
- Input:
```
query
```
  (search term), optional
```
limit
```
- Output: articles with title, abstract, PMID

Workflow:

For proteins with EM structures, get AlphaFold predictions for comparison
Note regions where AlphaFold confidence is low (pLDDT < 70) -- these may be flexible and harder to resolve by EM
Search PubMed for methodological papers and biological insights from the EM studies
Cross-reference EMDB/PDB/EMPIAR accessions in publications

目标：将电镜数据与更广泛的结构生物学背景关联起来。

工具:

```
alphafold_get_prediction
```
-- 获取AlphaFold预测结构
- 输入：
```
qualifier
```
  （UniProt编号）
- 输出：预测结构坐标、置信度分数（pLDDT）
```
PubMed_search_articles
```
-- 查找描述电镜研究的出版物
- 输入：
```
query
```
  （搜索词），可选参数
```
limit
```
- 输出：包含标题、摘要、PMID的文章

工作流程:

对有电镜结构的蛋白质，获取AlphaFold预测结果进行对比
记录AlphaFold置信度低的区域（pLDDT < 70）——这些区域可能更灵活，难以用电镜解析
在PubMed中搜索电镜研究的方法学论文和生物学见解
在出版物中交叉引用EMDB/PDB/EMPIAR编号

Phase 6: Interpretation & Recommendations

Phase 6: 解读与建议

Don't just list maps — help the user choose the RIGHT map for their purpose.

Decision matrix: Which map should I use?

Purpose	Best Resolution	Method	Priority Criteria
Atomic model building	< 3.5A	Single particle	Highest resolution with fitted PDB model
Drug binding site analysis	< 3.0A	Single particle	Must resolve side chains in binding pocket
Domain architecture	4-8A	Single particle or subtomogram avg	Large complexes where domains need fitting
Conformational states	< 4.5A	Single particle (multiple classes)	Look for entries with multiple maps from same dataset
Cellular context	15-40A	Cryo-ET	Tomographic datasets showing in-situ arrangement
Reprocessing	Any	Any	Must have EMPIAR raw data; prefer recent datasets (better detectors)

Quality assessment checklist:

Resolution reported is the "gold standard" FSC 0.143 cutoff? (some older entries use 0.5 cutoff — inflates resolution)
Map sharpened appropriately? (over-sharpened maps can look better but contain artifacts)
Fitting statistics available? (cross-correlation > 0.7 is acceptable)
Multiple maps from same sample? (suggests conformational heterogeneity — important for drug design)

Resolution trend analysis: If multiple maps exist over time, note the resolution trajectory. Improvement from 6A (2015) to 2.8A (2023) suggests the sample is amenable to high-resolution single particle analysis with modern hardware.

不要仅罗列图谱——帮助用户选择符合其需求的正确图谱。

决策矩阵：我应该使用哪张图谱？

用途	最佳分辨率	方法	优先标准
原子模型构建	< 3.5Å	单颗粒分析	带有拟合PDB模型的最高分辨率图谱
药物结合位点分析	< 3.0Å	单颗粒分析	必须能解析结合口袋中的侧链
结构域架构分析	4-8Å	单颗粒分析或亚断层扫描平均	需要拟合结构域的大型复合物
构象状态分析	< 4.5Å	单颗粒分析（多类别）	寻找同一数据集的多张图谱条目
细胞背景分析	15-40Å	冷冻电子断层扫描	显示原位排布的断层扫描数据集
重新处理	任意	任意	必须有EMPIAR原始数据；优先选择近期数据集（探测器更先进）

质量评估清单:

报告的分辨率是否采用"金标准"FSC 0.143 cutoff？（部分旧条目使用0.5 cutoff——会高估分辨率）
图谱是否经过适当锐化？（过度锐化的图谱看起来更好但可能包含伪影）
是否有拟合统计数据？（交叉相关性>0.7为可接受水平）
同一样本是否有多个图谱？（表明构象异质性——对药物设计很重要）

分辨率趋势分析：若存在随时间变化的多张图谱，记录分辨率变化轨迹。从2015年的6Å提升到2023年的2.8Å，表明该样本适合使用现代硬件进行高分辨率单颗粒分析。

Phase 7: Report Synthesis

Phase 7: 报告整合

Assemble findings into an actionable report:

Target Overview -- protein/complex identity, biological significance
EM Map Landscape -- available maps with resolution, method, and year
Best Available Structures -- highest resolution maps with fitted models, with quality assessment
Recommendation -- which specific map/model to use for the user's purpose (with reasoning)
Raw Data Availability -- EMPIAR datasets for reprocessing, with dataset sizes
Tomography Data -- cellular context datasets if available
Structural Context -- comparison with X-ray/NMR/AlphaFold structures
Key Publications -- methods papers, biological discoveries
Data Gaps -- missing conformational states, unresolved regions, need for higher resolution

将研究结果整理为可执行的报告：

目标概述 —— 蛋白质/复合物的身份、生物学意义
电镜图谱全景 —— 可用图谱的分辨率、方法和年份
最佳可用结构 —— 带有拟合模型的最高分辨率图谱及质量评估
建议 —— 针对用户需求推荐具体的图谱/模型（附理由）
原始数据可用性 —— 可用于重新处理的EMPIAR数据集及大小
断层扫描数据 —— 若有可用的细胞背景数据集
结构背景 —— 与X射线/NMR/AlphaFold结构的对比
关键出版物 —— 方法学论文、生物学发现
数据缺口 —— 缺失的构象状态、未解析区域、对更高分辨率的需求

Common Analysis Patterns

常见分析模式

Pattern	Description	Key Phases
Structure Discovery	Find all EM data for a protein	0, 1, 2, 5
Reprocessing Prep	Find raw data for re-analysis	0, 1, 3
Tomography Survey	Explore in-situ structural data	0, 4
Resolution Comparison	Track resolution improvements over time	0, 1, 2
Map-Model Validation	Assess quality of fitted atomic models	0, 1, 2, 5

模式	描述	关键阶段
结构发现	查找某一蛋白质的所有电镜数据	0, 1, 2, 5
重新处理准备	查找用于重新分析的原始数据	0, 1, 3
断层扫描调研	探索原位结构数据	0, 4
分辨率对比	追踪分辨率随时间的提升	0, 1, 2
图谱-模型验证	评估拟合原子模型的质量	0, 1, 2, 5

Edge Cases & Fallbacks

边缘情况与备选方案

No EMDB entries: The complex may only have X-ray or NMR structures. Search PDB via
```
RCSBAdvSearch_search_structures
```
with method filter
EMDB entry without PDB model: Common for lower-resolution maps. Note the gap; suggest AlphaFold for approximate modeling
No EMPIAR data: Raw data deposition is newer and not universal. The processed map in EMDB may be the only available data
Large complexes: Ribosomes, viruses, etc. may have hundreds of EMDB entries. Use resolution filters to narrow results

无EMDB条目：该复合物可能仅有X射线或NMR结构。使用
```
RCSBAdvSearch_search_structures
```
并通过方法筛选搜索PDB
无PDB模型的EMDB条目：低分辨率图谱常见此类情况。记录该缺口；建议使用AlphaFold进行近似建模
无EMPIAR数据：原始数据提交是较新的要求，并非所有数据都有。EMDB中的已处理图谱可能是唯一可用数据
大型复合物：核糖体、病毒等可能有数百条EMDB条目。使用分辨率筛选缩小结果范围

Limitations

局限性

No map visualization: This skill retrieves metadata and statistics, not 3D renderings. Use UCSF ChimeraX or IMOD for visualization
No reprocessing: Finding raw data is supported; actual cryo-EM data processing requires specialized software (RELION, cryoSPARC)
Resolution is not accuracy: A 3A map processed with errors may be less reliable than a well-validated 4A map. Fitting statistics matter
Deposition lag: Structures may be published months before EMDB deposition, or vice versa

无图谱可视化：本技能仅检索元数据和统计数据，不提供3D渲染。使用UCSF ChimeraX或IMOD进行可视化
不支持重新处理：支持查找原始数据；实际冷冻电镜数据处理需要专用软件（RELION、cryoSPARC）
分辨率不等于准确性：存在错误的3Å图谱可能不如经过充分验证的4Å图谱可靠。拟合统计数据至关重要
提交滞后：结构可能在发表数月后才提交至EMDB，反之亦然