---
id: wiki-2026-0508-bioinformatics-structure-predict
title: Bioinformatics Structure Prediction
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Protein Structure Prediction, AlphaFold, ESM]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [bioinformatics, ml, protein, structure]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: applied
tech_stack:
  language: Python
  framework: AlphaFold3/ESM3/ColabFold
---

# Bioinformatics Structure Prediction

## 매 한 줄
> **"매 sequence 에서 3D 구조까지 — 50년 grand challenge 가 2021 년 풀렸다."**. AlphaFold2 (2021) 가 CASP14 에서 experimental accuracy 달성, AlphaFold3 (2024) 가 protein-ligand-NA complex 까지 확장, ESM3 (2024) 가 generative protein design 시대를 열었다. 2026 의 표준: AF3 + ESMFold + RoseTTAFold All-Atom + ColabFold pipeline.

## 매 핵심

### 매 Method Lineage
- **Homology modeling** (1990s): MODELLER — known template 의존.
- **Threading / fold recognition** (2000s).
- **Ab initio physics** (Rosetta).
- **Coevolution + DL** (2018+): trRosetta, AlphaFold1.
- **Attention-based** (2021+): AlphaFold2 — Evoformer + Structure module.
- **All-atom diffusion** (2024+): AlphaFold3 — protein/DNA/RNA/ligand 통합.
- **Single-sequence (LLM)**: ESMFold, ESM3 — 매 MSA 없이 fast.

### 매 AlphaFold3 Capability (2024)
- 매 protein-protein, protein-NA, protein-ligand complex.
- 매 covalent modifications, ions.
- 매 diffusion-based all-atom output.
- 매 license: research-only via AF Server.

### 매 응용
1. **Drug discovery**: target-ligand docking, hit triage.
2. **Protein engineering**: enzyme design, antibody.
3. **Disease mechanism**: variant effect (missense3D, AlphaMissense).
4. **Structural biology**: cryo-EM model building.
5. **De novo design**: RFdiffusion + ProteinMPNN.

## 💻 패턴

### ColabFold one-liner
```bash
# 매 fast MSA via MMseqs2 + AF2 inference
colabfold_batch input.fasta out_dir/ \
  --num-recycle 3 --model-type alphafold2_multimer_v3
```

### ESMFold (single-sequence, no MSA)
```python
import torch
from transformers import EsmForProteinFolding
model = EsmForProteinFolding.from_pretrained("facebook/esmfold_v1").cuda().eval()
seq = "MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGDGTQDNLSGAEKAVQVK"
with torch.no_grad():
    out = model.infer_pdb(seq)
open("pred.pdb","w").write(out)
```

### AlphaFold3 via API
```python
# 매 AF3 server (research) — JSON job spec
import requests
job = {
  "name": "complex_001",
  "modelSeeds": [42],
  "sequences": [
    {"protein": {"id":"A","sequence":"MKTA..."}},
    {"ligand":  {"id":"L","ccdCodes":["ATP"]}}
  ]
}
r = requests.post("https://alphafoldserver.com/api/job", json=job, headers=auth)
```

### RFdiffusion de novo binder design
```bash
# 매 design 80aa binder against target hotspot
python run_inference.py \
  inference.output_prefix=binders/run \
  contigmap.contigs="['A1-150,0 80-80']" \
  ppi.hotspot_res="['A30','A33','A56']" \
  inference.num_designs=100
```

### Confidence (pLDDT) filtering
```python
import numpy as np
# 매 pLDDT > 90 = very high; 70-90 = confident; 50-70 = low; <50 = disordered
plddt = np.array([atom.bfactor for atom in structure.get_atoms() if atom.name == "CA"])
mean_conf = plddt.mean()
disordered_frac = (plddt < 50).mean()
```

## 매 결정 기준
| 상황 | Tool |
|---|---|
| Single protein, fast | ESMFold |
| Single protein, accurate | AlphaFold2 (ColabFold) |
| Multimer / complex | AlphaFold3 / AF-Multimer |
| Protein + ligand | AlphaFold3 / Boltz-1 |
| De novo design | RFdiffusion + ProteinMPNN |
| Variant effect | AlphaMissense |

**기본값**: 매 ColabFold AF2-multimer → AF3 for ligand/NA.

## 🔗 Graph
- 부모: [[Statistics & Data Analysis]]
- 변형: [[Anomaly-Detection]]
- 응용: [[Practical-Cryptography]]
- Adjacent: [[Inferential-Statistics]]

## 🤖 LLM 활용
**언제**: protein language model embedding, binder search, paper summary, mutation scan ranking.
**언제 X**: 매 final pose prediction — physics/structure model 이 specialized.

## ❌ 안티패턴
- **pLDDT 무시**: 매 low-confidence region 을 그대로 사용 — 매 disordered 일 수 있음.
- **Single seed**: 매 AF3 multi-seed sampling 권장 — diversity.
- **MSA 없이 large complex**: 매 ESMFold 는 single-chain 강점, multimer 약함.
- **License 위반**: 매 AF3 weights non-commercial — server API 만 허용.

## 🧪 검증 / 중복
- Verified: Jumper et al. 2021 Nature (AF2); Abramson et al. 2024 Nature (AF3); Lin et al. 2023 Science (ESM2).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — AF3/ESM3/RFdiffusion 2026 stack |
