AlphaFold 3 Web Server
Unified Diffusion-Based Prediction of Proteins, DNA, RNA, Ligands & Ions
Google DeepMind and Isomorphic Labs' transformative foundation model for universal biomolecular complex prediction.
技术架构与背景概述
AlphaFold 3 represents a fundamental paradigm shift from AlphaFold 2. By replacing traditional structural geometry modules with a raw atomic diffusion architecture (Pairformer + Diffusion Module), AlphaFold 3 predicts atomic coordinates across all biomolecular classes—proteins, nucleic acids (DNA/RNA), post-translational modifications (PTMs), ions, and drug-like small-molecule ligands—simultaneously within a single unified network.
攻克的核心生物学挑战
Biological processes are governed by intricate multi-component macromolecular assemblies (e.g. CRISPR-Cas9 bound to guide RNA and target DNA, or transcription factors docked to enhancer motifs). Previous computational methods were siloed, requiring separate pipelines for protein folding, RNA folding, and small-molecule docking, often failing at multi-molecular interfaces.
核心能力与技术亮点
Universal Biomolecular Interaction
Simultaneously folds and docks multi-chain proteins, DNA duplexes, RNA stem-loops, chemical ligands, and metal ions.
Diffusion-Based Direct Coordinate Generation
Operates directly on 3D atomic coordinates via denoising diffusion, accurately resolving disordered loops, stereochemistry, and bond lengths.
Interface Prediction Metrics (ipTM & pTM)
Provides rigorous per-interface predicted TM-scores (ipTM), allowing researchers to differentiate true biological binders from non-specific artifacts.
Extensive Chemical & PTM Support
Supports standard post-translational modifications including glycosylation, phosphorylation, methylation, and modified bases.
实战调用命令与代码示例
Define multi-chain protein-DNA-ligand complexes for submission to the AlphaFold 3 Web Server:
# AlphaFold 3 Complex Job Specification JSON
import json
job_config = {
"name": "Transcription_Factor_DNA_Complex",
"modelSeeds": [1],
"sequences": [
{
"proteinChain": {
"sequence": "MVRKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIA",
"count": 2 # Homodimer
}
},
{
"dnaSequence": {
"sequence": "ATGCGTACCGGATCC",
"count": 1
}
}
]
}
print(json.dumps(job_config, indent=2))输入参数与模型输出数据看板
Entity 1: Protein Chain A (p53 DNA-binding domain, 190 residues) Entity 2: Double-stranded DNA Target (GGCAAGTTAGGGCAAGTTAG) Entity 3: Zinc Ion (ZN) cofactor
Users specify amino acid sequences, nucleotide sequences, and chemical identifiers for multi-component folding.
{
"ranking_score": 0.892,
"ptm": 0.915,
"iptm": 0.867,
"fraction_disordered": 0.08,
"chain_pair_iptm": {
"[A, B]": 0.92,
"[A, DNA]": 0.87
}
}数值经归一化处理，直接对应下游生物表型预测。
预测结果生物学解读指南
科研人员如何理解预测数值、判别致病阈值与分子调控机制：
The predicted multi-molecular binding interface is highly reliable, suitable for rational drug discovery or mutagenesis experiments.
Overall topology is plausible; active site residues should be cross-referenced with experimental conservation or mutagenesis data.
The model does not find strong co-evolutionary or physicochemical evidence for a stable interaction under physiological conditions.
AlphaFold 3 Web Server vs. 传统分析工具横向对比
| 对比维度 | AlphaFold 3 Web Server (This Tool) | 传统方法 |
|---|---|---|
| Multi-Entity Complex | End-to-end (Protein + RNA + DNA + Ligand) | Separate rigid-body docking (AutoDock / HADDOCK) |
| Protein-Nucleic Acid Accuracy | State-of-the-art atomic resolution | Often requires experimental cryo-EM constraints |
| Architecture | Diffusion network on raw coordinates | Evoformer + invariant coordinate frames |
常见问题与专家解答 (FAQ)
How do I access AlphaFold 3?
You can use the free AlphaFold Server at alphafoldserver.com with a standard Google account. It requires zero GPU setup and delivers full 3D models in minutes.
Can AlphaFold 3 predict DNA-protein binding specificity?
Yes, AlphaFold 3 exhibits remarkable accuracy in placing transcription factors into DNA major and minor grooves with correct base-specific hydrogen bonding.