alphafold-database

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AlphaFold Database

AlphaFold数据库

Overview

概述

AlphaFold DB is a public repository of AI-predicted 3D protein structures for over 200 million proteins, maintained by DeepMind and EMBL-EBI. Access structure predictions with confidence metrics, download coordinate files, retrieve bulk datasets, and integrate predictions into computational workflows.

AlphaFold DB是由DeepMind和EMBL-EBI维护的公共存储库，包含超过2亿个AI预测的3D蛋白质结构。您可以访问带置信度指标的结构预测结果、下载坐标文件、获取批量数据集，并将预测结果整合到计算工作流中。

When to Use This Skill

何时使用该技能

This skill should be used when working with AI-predicted protein structures in scenarios such as:

Retrieving protein structure predictions by UniProt ID or protein name
Downloading PDB/mmCIF coordinate files for structural analysis
Analyzing prediction confidence metrics (pLDDT, PAE) to assess reliability
Accessing bulk proteome datasets via Google Cloud Platform
Comparing predicted structures with experimental data
Performing structure-based drug discovery or protein engineering
Building structural models for proteins lacking experimental structures
Integrating AlphaFold predictions into computational pipelines

当您在以下场景中处理AI预测的蛋白质结构时，应使用此技能：

通过UniProt ID或蛋白质名称检索蛋白质结构预测结果
下载PDB/mmCIF坐标文件以进行结构分析
分析预测置信度指标（pLDDT、PAE）以评估可靠性
通过Google Cloud Platform访问批量蛋白质组数据集
比较预测结构与实验数据
进行基于结构的药物研发或蛋白质工程
为缺乏实验结构的蛋白质构建结构模型
将AlphaFold预测结果整合到计算流水线中

Core Capabilities

核心功能

1. Searching and Retrieving Predictions

1. 搜索与检索预测结果

Using Biopython (Recommended):

The Biopython library provides the simplest interface for retrieving AlphaFold structures:

python

from Bio.PDB import alphafold_db

使用Biopython（推荐）：

Biopython库为检索AlphaFold结构提供了最简单的接口：

python

from Bio.PDB import alphafold_db

Get all predictions for a UniProt accession

获取某个UniProt登录号的所有预测结果

predictions = list(alphafold_db.get_predictions("P00520"))

Download structure file (mmCIF format)

下载结构文件（mmCIF格式）

for prediction in predictions: cif_file = alphafold_db.download_cif_for(prediction, directory="./structures") print(f"Downloaded: {cif_file}")

for prediction in predictions: cif_file = alphafold_db.download_cif_for(prediction, directory="./structures") print(f"已下载: {cif_file}")

Get Structure objects directly

直接获取Structure对象

from Bio.PDB import MMCIFParser structures = list(alphafold_db.get_structural_models_for("P00520"))


**Direct API Access:**

Query predictions using REST endpoints:

```python
import requests

from Bio.PDB import MMCIFParser structures = list(alphafold_db.get_structural_models_for("P00520"))


**直接API访问：**

使用REST端点查询预测结果：

```python
import requests

Get prediction metadata for a UniProt accession

获取某个UniProt登录号的预测元数据

uniprot_id = "P00520" api_url = f"https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}" response = requests.get(api_url) prediction_data = response.json()

Extract AlphaFold ID

提取AlphaFold ID

alphafold_id = prediction_data[0]['entryId'] print(f"AlphaFold ID: {alphafold_id}")


**Using UniProt to Find Accessions:**

Search UniProt to find protein accessions first:

```python
import urllib.parse, urllib.request

def get_uniprot_ids(query, query_type='PDB_ID'):
    """Query UniProt to get accession IDs"""
    url = 'https://www.uniprot.org/uploadlists/'
    params = {
        'from': query_type,
        'to': 'ACC',
        'format': 'txt',
        'query': query
    }
    data = urllib.parse.urlencode(params).encode('ascii')
    with urllib.request.urlopen(urllib.request.Request(url, data)) as response:
        return response.read().decode('utf-8').splitlines()

alphafold_id = prediction_data[0]['entryId'] print(f"AlphaFold ID: {alphafold_id}")


**通过UniProt查找登录号：**

先搜索UniProt以找到蛋白质登录号：

```python
import urllib.parse, urllib.request

def get_uniprot_ids(query, query_type='PDB_ID'):
    """查询UniProt以获取登录号ID"""
    url = 'https://www.uniprot.org/uploadlists/'
    params = {
        'from': query_type,
        'to': 'ACC',
        'format': 'txt',
        'query': query
    }
    data = urllib.parse.urlencode(params).encode('ascii')
    with urllib.request.urlopen(urllib.request.Request(url, data)) as response:
        return response.read().decode('utf-8').splitlines()

Example: Find UniProt IDs for a protein name

示例：根据蛋白质名称查找UniProt ID

protein_ids = get_uniprot_ids("hemoglobin", query_type="GENE_NAME")

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protein_ids = get_uniprot_ids("hemoglobin", query_type="GENE_NAME")

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2. Downloading Structure Files

2. 下载结构文件

AlphaFold provides multiple file formats for each prediction:

File Types Available:

Model coordinates (
```
model_v4.cif
```
): Atomic coordinates in mmCIF/PDBx format
Confidence scores (
```
confidence_v4.json
```
): Per-residue pLDDT scores (0-100)
Predicted Aligned Error (
```
predicted_aligned_error_v4.json
```
): PAE matrix for residue pair confidence

Download URLs:

python

import requests

alphafold_id = "AF-P00520-F1"
version = "v4"

AlphaFold为每个预测结果提供多种文件格式：

可用文件类型：

模型坐标 (
```
model_v4.cif
```
)：mmCIF/PDBx格式的原子坐标
置信度分数 (
```
confidence_v4.json
```
)：每个残基的pLDDT分数（0-100）
预测对齐误差 (
```
predicted_aligned_error_v4.json
```
)：残基对置信度的PAE矩阵

下载链接：

python

import requests

alphafold_id = "AF-P00520-F1"
version = "v4"

Model coordinates (mmCIF)

模型坐标（mmCIF格式）

model_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-model_{version}.cif" response = requests.get(model_url) with open(f"{alphafold_id}.cif", "w") as f: f.write(response.text)

Confidence scores (JSON)

置信度分数（JSON格式）

confidence_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-confidence_{version}.json" response = requests.get(confidence_url) confidence_data = response.json()

Predicted Aligned Error (JSON)

预测对齐误差（JSON格式）

pae_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-predicted_aligned_error_{version}.json" response = requests.get(pae_url) pae_data = response.json()


**PDB Format (Alternative):**

```python

pae_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-predicted_aligned_error_{version}.json" response = requests.get(pae_url) pae_data = response.json()


**PDB格式（替代方案）：**

```python

Download as PDB format instead of mmCIF

下载PDB格式而非mmCIF格式

pdb_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-model_{version}.pdb" response = requests.get(pdb_url) with open(f"{alphafold_id}.pdb", "wb") as f: f.write(response.content)

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pdb_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-model_{version}.pdb" response = requests.get(pdb_url) with open(f"{alphafold_id}.pdb", "wb") as f: f.write(response.content)

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3. Working with Confidence Metrics

3. 处理置信度指标

AlphaFold predictions include confidence estimates critical for interpretation:

pLDDT (per-residue confidence):

python

import json
import requests

AlphaFold的预测结果包含对解读至关重要的置信度估计：

pLDDT（每个残基的置信度）：

python

import json
import requests

Load confidence scores

加载置信度分数

alphafold_id = "AF-P00520-F1" confidence_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-confidence_v4.json" confidence = requests.get(confidence_url).json()

Extract pLDDT scores

提取pLDDT分数

plddt_scores = confidence['confidenceScore']

Interpret confidence levels

解读置信度等级

pLDDT > 90: Very high confidence

pLDDT > 90：极高置信度

pLDDT 70-90: High confidence

pLDDT 70-90：高置信度

pLDDT 50-70: Low confidence

pLDDT 50-70：低置信度

pLDDT < 50: Very low confidence

pLDDT < 50：极低置信度

high_confidence_residues = [i for i, score in enumerate(plddt_scores) if score > 90] print(f"High confidence residues: {len(high_confidence_residues)}/{len(plddt_scores)}")


**PAE (Predicted Aligned Error):**

PAE indicates confidence in relative domain positions:

```python
import numpy as np
import matplotlib.pyplot as plt

high_confidence_residues = [i for i, score in enumerate(plddt_scores) if score > 90] print(f"高置信度残基数量: {len(high_confidence_residues)}/{len(plddt_scores)}")


**PAE（预测对齐误差）：**

PAE表示对相对结构域位置的置信度：

```python
import numpy as np
import matplotlib.pyplot as plt

Load PAE matrix

加载PAE矩阵

pae_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-predicted_aligned_error_v4.json" pae = requests.get(pae_url).json()

Visualize PAE matrix

可视化PAE矩阵

pae_matrix = np.array(pae['distance']) plt.figure(figsize=(10, 8)) plt.imshow(pae_matrix, cmap='viridis_r', vmin=0, vmax=30) plt.colorbar(label='PAE (Å)') plt.title(f'Predicted Aligned Error: {alphafold_id}') plt.xlabel('Residue') plt.ylabel('Residue') plt.savefig(f'{alphafold_id}_pae.png', dpi=300, bbox_inches='tight')

pae_matrix = np.array(pae['distance']) plt.figure(figsize=(10, 8)) plt.imshow(pae_matrix, cmap='viridis_r', vmin=0, vmax=30) plt.colorbar(label='PAE (Å)') plt.title(f'预测对齐误差: {alphafold_id}') plt.xlabel('残基') plt.ylabel('残基') plt.savefig(f'{alphafold_id}_pae.png', dpi=300, bbox_inches='tight')

Low PAE values (<5 Å) indicate confident relative positioning

低PAE值(<5 Å)表示相对位置置信度高

High PAE values (>15 Å) suggest uncertain domain arrangements

高PAE值(>15 Å)表示结构域排列不确定

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4. Bulk Data Access via Google Cloud

4. 通过Google Cloud访问批量数据

For large-scale analyses, use Google Cloud datasets:

Google Cloud Storage:

bash

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对于大规模分析，使用Google Cloud数据集：

Google Cloud Storage：

bash

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Install gsutil

安装gsutil

uv pip install gsutil

List available data

列出可用数据

gsutil ls gs://public-datasets-deepmind-alphafold-v4/

Download entire proteomes (by taxonomy ID)

下载完整蛋白质组（按分类学ID）

gsutil -m cp gs://public-datasets-deepmind-alphafold-v4/proteomes/proteome-tax_id-9606-*.tar .

Download specific files

下载特定文件

gsutil cp gs://public-datasets-deepmind-alphafold-v4/accession_ids.csv .


**BigQuery Metadata Access:**

```python
from google.cloud import bigquery

gsutil cp gs://public-datasets-deepmind-alphafold-v4/accession_ids.csv .


**BigQuery元数据访问：**

```python
from google.cloud import bigquery

Initialize client

初始化客户端

client = bigquery.Client()

Query metadata

查询元数据

query = """ SELECT entryId, uniprotAccession, organismScientificName, globalMetricValue, fractionPlddtVeryHigh FROM

bigquery-public-data.deepmind_alphafold.metadata

WHERE organismScientificName = 'Homo sapiens' AND fractionPlddtVeryHigh > 0.8 LIMIT 100 """

results = client.query(query).to_dataframe() print(f"Found {len(results)} high-confidence human proteins")


**Download by Species:**

```python
import subprocess

def download_proteome(taxonomy_id, output_dir="./proteomes"):
    """Download all AlphaFold predictions for a species"""
    pattern = f"gs://public-datasets-deepmind-alphafold-v4/proteomes/proteome-tax_id-{taxonomy_id}-*_v4.tar"
    cmd = f"gsutil -m cp {pattern} {output_dir}/"
    subprocess.run(cmd, shell=True, check=True)

query = """ SELECT entryId, uniprotAccession, organismScientificName, globalMetricValue, fractionPlddtVeryHigh FROM

bigquery-public-data.deepmind_alphafold.metadata

WHERE organismScientificName = 'Homo sapiens' AND fractionPlddtVeryHigh > 0.8 LIMIT 100 """

results = client.query(query).to_dataframe() print(f"找到{len(results)}个高置信度人类蛋白质")


**按物种下载：**

```python
import subprocess

def download_proteome(taxonomy_id, output_dir="./proteomes"):
    """下载某个物种的所有AlphaFold预测结果"""
    pattern = f"gs://public-datasets-deepmind-alphafold-v4/proteomes/proteome-tax_id-{taxonomy_id}-*_v4.tar"
    cmd = f"gsutil -m cp {pattern} {output_dir}/"
    subprocess.run(cmd, shell=True, check=True)

Download E. coli proteome (tax ID: 83333)

下载大肠杆菌蛋白质组（分类ID：83333）

download_proteome(83333)

Download human proteome (tax ID: 9606)

下载人类蛋白质组（分类ID：9606）

download_proteome(9606)

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download_proteome(9606)

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5. Parsing and Analyzing Structures

5. 解析与分析结构

Work with downloaded AlphaFold structures using BioPython:

python

from Bio.PDB import MMCIFParser, PDBIO
import numpy as np

使用BioPython处理下载的AlphaFold结构：

python

from Bio.PDB import MMCIFParser, PDBIO
import numpy as np

Parse mmCIF file

解析mmCIF文件

parser = MMCIFParser(QUIET=True) structure = parser.get_structure("protein", "AF-P00520-F1-model_v4.cif")

Extract coordinates

提取坐标

coords = [] for model in structure: for chain in model: for residue in chain: if 'CA' in residue: # Alpha carbons only coords.append(residue['CA'].get_coord())

coords = np.array(coords) print(f"Structure has {len(coords)} residues")

coords = [] for model in structure: for chain in model: for residue in chain: if 'CA' in residue: # 仅提取α碳 coords.append(residue['CA'].get_coord())

coords = np.array(coords) print(f"结构包含{len(coords)}个残基")

Calculate distances

计算距离

from scipy.spatial.distance import pdist, squareform distance_matrix = squareform(pdist(coords))

Identify contacts (< 8 Å)

识别接触点（<8 Å）

contacts = np.where((distance_matrix > 0) & (distance_matrix < 8)) print(f"Number of contacts: {len(contacts[0]) // 2}")


**Extract B-factors (pLDDT values):**

AlphaFold stores pLDDT scores in the B-factor column:

```python
from Bio.PDB import MMCIFParser

parser = MMCIFParser(QUIET=True)
structure = parser.get_structure("protein", "AF-P00520-F1-model_v4.cif")

contacts = np.where((distance_matrix > 0) & (distance_matrix < 8)) print(f"接触点数量: {len(contacts[0]) // 2}")


**提取B因子（pLDDT值）：**

AlphaFold将pLDDT分数存储在B因子列中：

```python
from Bio.PDB import MMCIFParser

parser = MMCIFParser(QUIET=True)
structure = parser.get_structure("protein", "AF-P00520-F1-model_v4.cif")

Extract pLDDT from B-factors

从B因子中提取pLDDT

plddt_scores = [] for model in structure: for chain in model: for residue in chain: if 'CA' in residue: plddt_scores.append(residue['CA'].get_bfactor())

Identify high-confidence regions

识别高置信度区域

high_conf_regions = [(i, score) for i, score in enumerate(plddt_scores, 1) if score > 90] print(f"High confidence residues: {len(high_conf_regions)}")

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high_conf_regions = [(i, score) for i, score in enumerate(plddt_scores, 1) if score > 90] print(f"高置信度残基数量: {len(high_conf_regions)}")

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6. Batch Processing Multiple Proteins

6. 批量处理多个蛋白质

Process multiple predictions efficiently:

python

from Bio.PDB import alphafold_db
import pandas as pd

uniprot_ids = ["P00520", "P12931", "P04637"]  # Multiple proteins
results = []

for uniprot_id in uniprot_ids:
    try:
        # Get prediction
        predictions = list(alphafold_db.get_predictions(uniprot_id))

        if predictions:
            pred = predictions[0]

            # Download structure
            cif_file = alphafold_db.download_cif_for(pred, directory="./batch_structures")

            # Get confidence data
            alphafold_id = pred['entryId']
            conf_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-confidence_v4.json"
            conf_data = requests.get(conf_url).json()

            # Calculate statistics
            plddt_scores = conf_data['confidenceScore']
            avg_plddt = np.mean(plddt_scores)
            high_conf_fraction = sum(1 for s in plddt_scores if s > 90) / len(plddt_scores)

            results.append({
                'uniprot_id': uniprot_id,
                'alphafold_id': alphafold_id,
                'avg_plddt': avg_plddt,
                'high_conf_fraction': high_conf_fraction,
                'length': len(plddt_scores)
            })
    except Exception as e:
        print(f"Error processing {uniprot_id}: {e}")

高效处理多个预测结果：

python

from Bio.PDB import alphafold_db
import pandas as pd

uniprot_ids = ["P00520", "P12931", "P04637"]  # 多个蛋白质
results = []

for uniprot_id in uniprot_ids:
    try:
        # 获取预测结果
        predictions = list(alphafold_db.get_predictions(uniprot_id))

        if predictions:
            pred = predictions[0]

            # 下载结构
            cif_file = alphafold_db.download_cif_for(pred, directory="./batch_structures")

            # 获取置信度数据
            alphafold_id = pred['entryId']
            conf_url = f"https://alphafold.ebi.ac.uk/files/{alphafold_id}-confidence_v4.json"
            conf_data = requests.get(conf_url).json()

            # 计算统计数据
            plddt_scores = conf_data['confidenceScore']
            avg_plddt = np.mean(plddt_scores)
            high_conf_fraction = sum(1 for s in plddt_scores if s > 90) / len(plddt_scores)

            results.append({
                'uniprot_id': uniprot_id,
                'alphafold_id': alphafold_id,
                'avg_plddt': avg_plddt,
                'high_conf_fraction': high_conf_fraction,
                'length': len(plddt_scores)
            })
    except Exception as e:
        print(f"处理{uniprot_id}时出错: {e}")

Create summary DataFrame

创建汇总DataFrame

df = pd.DataFrame(results) print(df)

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df = pd.DataFrame(results) print(df)

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Installation and Setup

安装与设置

Python Libraries

Python库

bash

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bash

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Install Biopython for structure access

安装Biopython以访问结构

uv pip install biopython

Install requests for API access

安装requests以访问API

uv pip install requests

For visualization and analysis

用于可视化与分析

uv pip install numpy matplotlib pandas scipy

For Google Cloud access (optional)

用于Google Cloud访问（可选）

uv pip install google-cloud-bigquery gsutil

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uv pip install google-cloud-bigquery gsutil

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3D-Beacons API Alternative

3D-Beacons API替代方案

AlphaFold can also be accessed via the 3D-Beacons federated API:

python

import requests

也可通过3D-Beacons联邦API访问AlphaFold：

python

import requests

Query via 3D-Beacons

通过3D-Beacons查询

uniprot_id = "P00520" url = f"https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/api/uniprot/summary/{uniprot_id}.json" response = requests.get(url) data = response.json()

Filter for AlphaFold structures

筛选AlphaFold结构

af_structures = [s for s in data['structures'] if s['provider'] == 'AlphaFold DB']

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af_structures = [s for s in data['structures'] if s['provider'] == 'AlphaFold DB']

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Common Use Cases

常见用例

Structural Proteomics

结构蛋白质组学

Download complete proteome predictions for analysis
Identify high-confidence structural regions across proteins
Compare predicted structures with experimental data
Build structural models for protein families

下载完整蛋白质组预测结果进行分析
识别跨蛋白质的高置信度结构区域
比较预测结构与实验数据
为蛋白质家族构建结构模型

Drug Discovery

药物研发

Retrieve target protein structures for docking studies
Analyze binding site conformations
Identify druggable pockets in predicted structures
Compare structures across homologs

检索目标蛋白质结构用于对接研究
分析结合位点构象
识别预测结构中的可成药口袋
比较同源蛋白的结构

Protein Engineering

蛋白质工程

Identify stable/unstable regions using pLDDT
Design mutations in high-confidence regions
Analyze domain architectures using PAE
Model protein variants and mutations

使用pLDDT识别稳定/不稳定区域
在高置信度区域设计突变
使用PAE分析结构域架构
建模蛋白质变体与突变

Evolutionary Studies

进化研究

Compare ortholog structures across species
Analyze conservation of structural features
Study domain evolution patterns
Identify functionally important regions

比较跨物种的直系同源蛋白结构
分析结构特征的保守性
研究结构域进化模式
识别功能重要区域

Key Concepts

关键概念

UniProt Accession: Primary identifier for proteins (e.g., "P00520"). Required for querying AlphaFold DB.

AlphaFold ID: Internal identifier format:

AF-[UniProt accession]-F[fragment number]

(e.g., "AF-P00520-F1").

pLDDT (predicted Local Distance Difference Test): Per-residue confidence metric (0-100). Higher values indicate more confident predictions.

PAE (Predicted Aligned Error): Matrix indicating confidence in relative positions between residue pairs. Low values (<5 Å) suggest confident relative positioning.

Database Version: Current version is v4. File URLs include version suffix (e.g.,

model_v4.cif

Fragment Number: Large proteins may be split into fragments. Fragment number appears in AlphaFold ID (e.g., F1, F2).

**UniProt登录号：**蛋白质的主要标识符（例如："P00520"），查询AlphaFold DB时必需。

**AlphaFold ID：**内部标识符格式：

AF-[UniProt登录号]-F[片段编号]

（例如："AF-P00520-F1"）。

**pLDDT（预测局部距离差异测试）：**每个残基的置信度指标（0-100），值越高表示预测置信度越高。

**PAE（预测对齐误差）：**表示残基对相对位置置信度的矩阵，低数值(<5 Å)表示相对位置置信度高。

**数据库版本：**当前版本为v4，文件链接包含版本后缀（例如：

model_v4.cif

）。

**片段编号：**大型蛋白质可能会被拆分为多个片段，片段编号会出现在AlphaFold ID中（例如：F1、F2）。

Confidence Interpretation Guidelines

置信度解读指南

pLDDT Thresholds:

>90: Very high confidence - suitable for detailed analysis
70-90: High confidence - generally reliable backbone structure
50-70: Low confidence - use with caution, flexible regions
<50: Very low confidence - likely disordered or unreliable

PAE Guidelines:

<5 Å: Confident relative positioning of domains
5-10 Å: Moderate confidence in arrangement
>15 Å: Uncertain relative positions, domains may be mobile

pLDDT阈值：

>90：极高置信度 - 适合详细分析
70-90：高置信度 - 主链结构通常可靠
50-70：低置信度 - 谨慎使用，多为柔性区域
<50：极低置信度 - 可能无序或不可靠

PAE指南：

<5 Å：结构域相对位置置信度高
5-10 Å：排列置信度中等
>15 Å：相对位置不确定，结构域可能可移动

Resources

资源

references/api_reference.md

Comprehensive API documentation covering:

Complete REST API endpoint specifications
File format details and data schemas
Google Cloud dataset structure and access patterns
Advanced query examples and batch processing strategies
Rate limiting, caching, and best practices
Troubleshooting common issues

Consult this reference for detailed API information, bulk download strategies, or when working with large-scale datasets.

全面的API文档涵盖：

完整的REST API端点规范
文件格式细节与数据模式
Google Cloud数据集结构与访问模式
高级查询示例与批量处理策略
请求速率限制、缓存与最佳实践
常见问题排查

如需详细API信息、批量下载策略或处理大规模数据集，请参考此文档。

Important Notes

重要说明

Data Usage and Attribution

数据使用与归因

AlphaFold DB is freely available under CC-BY-4.0 license
Cite: Jumper et al. (2021) Nature and Varadi et al. (2022) Nucleic Acids Research
Predictions are computational models, not experimental structures
Always assess confidence metrics before downstream analysis

AlphaFold DB在CC-BY-4.0许可下免费提供
引用文献：Jumper et al. (2021) Nature 和 Varadi et al. (2022) Nucleic Acids Research
预测结果是计算模型，而非实验结构
在进行下游分析前务必评估置信度指标

Version Management

版本管理

Current database version: v4 (as of 2024-2025)
File URLs include version suffix (e.g.,
```
_v4.cif
```
)
Check for database updates regularly
Older versions may be deprecated over time

当前数据库版本：v4（截至2024-2025年）
文件链接包含版本后缀（例如：
```
_v4.cif
```
）
定期检查数据库更新
旧版本可能会逐渐被弃用

Data Quality Considerations

数据质量考量

High pLDDT doesn't guarantee functional accuracy
Low confidence regions may be disordered in vivo
PAE indicates relative domain confidence, not absolute positioning
Predictions lack ligands, post-translational modifications, and cofactors
Multi-chain complexes are not predicted (single chains only)

高pLDDT值不保证功能准确性
低置信度区域在体内可能是无序的
PAE表示结构域相对置信度，而非绝对位置
预测结果不包含配体、翻译后修饰和辅因子
不预测多链复合物（仅单链）

Performance Tips

性能技巧

Use Biopython for simple single-protein access
Use Google Cloud for bulk downloads (much faster than individual files)
Cache downloaded files locally to avoid repeated downloads
BigQuery free tier: 1 TB processed data per month
Consider network bandwidth for large-scale downloads

简单的单蛋白质访问使用Biopython
批量下载使用Google Cloud（比单个文件下载快得多）
本地缓存已下载文件以避免重复下载
BigQuery免费层级：每月可处理1TB数据
大规模下载时考虑网络带宽

Additional Resources

其他资源

AlphaFold DB Website: https://alphafold.ebi.ac.uk/
API Documentation: https://alphafold.ebi.ac.uk/api-docs
Google Cloud Dataset: https://cloud.google.com/blog/products/ai-machine-learning/alphafold-protein-structure-database
3D-Beacons API: https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/
AlphaFold Papers:
- Nature (2021): https://doi.org/10.1038/s41586-021-03819-2
- Nucleic Acids Research (2024): https://doi.org/10.1093/nar/gkad1011
Biopython Documentation: https://biopython.org/docs/dev/api/Bio.PDB.alphafold_db.html
GitHub Repository: https://github.com/google-deepmind/alphafold

AlphaFold DB官网： https://alphafold.ebi.ac.uk/
API文档： https://alphafold.ebi.ac.uk/api-docs
Google Cloud数据集： https://cloud.google.com/blog/products/ai-machine-learning/alphafold-protein-structure-database
3D-Beacons API： https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/
AlphaFold论文：
- Nature (2021)：https://doi.org/10.1038/s41586-021-03819-2
- Nucleic Acids Research (2024)：https://doi.org/10.1093/nar/gkad1011
Biopython文档： https://biopython.org/docs/dev/api/Bio.PDB.alphafold_db.html
GitHub仓库： https://github.com/google-deepmind/alphafold