Alphafold2 Multimer Agent Skill

> AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring. (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm2-sequence-scoring. For QC thresholds, use protein-design-qc.

3k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

132

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/BioTender-max/awesome-bio-agent-skills --skill alphafold2-multimer

What comes with it

4 414 bytes besides the instruction

README.md

references/multimer.md

The instruction itself

19 sections, as written by the author

AlphaFold2 / AlphaFold-Multimer Validation

Plain-language role: Use AlphaFold when you want a reference-grade structure prediction check for a designed sequence or complex.

Prerequisites

| Requirement | Minimum | Recommended |

|-------------|---------|-------------|

| Python | 3.8+ | 3.10 |

| CUDA | 11.0+ | 12.0+ |

| GPU VRAM | 32GB | 40GB (A100) |

| RAM | 32GB | 64GB |

| Disk | 100GB | 500GB (for databases) |

How to run

> First time? See Installation Guide to set up Modal and biomodals.

Option 1: ColabFold (recommended for multimer)

cd biomodals
modal run modal_colabfold.py \
  --input-faa sequences.fasta \
  --out-dir output/

GPU: A100 (40GB) | Timeout: 3600s default

Option 2: Local installation

git clone https://github.com/deepmind/alphafold2-multimer.git
cd alphafold2-multimer

python run_alphafold.py \
  --fasta_paths=query.fasta \
  --output_dir=output/ \
  --model_preset=monomer \
  --max_template_date=2026-01-01

Option 3: ESMFold (fast single-chain)

modal run modal_esmfold.py \
  --sequence "MKTAYIAKQRQISFVK..."

Key parameters

|-----------|---------|---------|-------------|

| --num_recycle | 3 | 1-20 | Recycling iterations |

Output format

output/
├── ranked_0.pdb           # Best model
├── ranked_1.pdb           # Second best
├── ranking_debug.json     # Confidence scores
├── result_model_1.pkl     # Full results
├── msas/                  # MSA files
└── features.pkl           # Input features

Extracting metrics

import pickle

with open('result_model_1.pkl', 'rb') as f:
    result = pickle.load(f)

plddt = result['plddt']
ptm = result['ptm']
iptm = result.get('iptm', None)  # Multimer only
pae = result['predicted_aligned_error']

Sample output

Successful run

$ python run_alphafold.py --fasta_paths complex.fasta --model_preset multimer
[INFO] Running MSA search...
[INFO] Running model 1/5...
[INFO] Running model 5/5...
[INFO] Relaxing structures...

Results:
  ranked_0.pdb:
    pLDDT: 87.3 (mean)
    pTM: 0.78
    ipTM: 0.62
    PAE (interface): 8.5

Saved to output/

What good output looks like:

pLDDT: > 85 (mean, on 0-100 scale) or > 0.85 (normalized)
pTM: > 0.70
ipTM: > 0.50 for complexes
PAE_interface: < 10

Decision tree

Should I use AlphaFold?
│
├─ What are you predicting?
│  ├─ Single protein → ESMFold (faster)
│  ├─ Protein-protein complex → AlphaFold/ColabFold ✓
│  ├─ Protein + ligand → Chai or Boltz
│  └─ Batch of sequences → ColabFold ✓
│
├─ What do you need?
│  ├─ Highest accuracy → AlphaFold/ColabFold ✓
│  ├─ Fast screening → ESMFold
│  └─ MSA-free prediction → Chai or ESMFold
│
└─ Which AF2 option?
   ├─ Local installation → Full control, slow setup
   ├─ ColabFold → Easier, MSA server
   └─ Modal → Recommended for batch

Typical performance

|---------------|-------------|--------------|-------|

| 100 complexes | 1-2h | ~$8 | With MSA server |

| 500 complexes | 5-10h | ~$40 | Standard campaign |

| 1000 complexes | 10-20h | ~$80 | Large campaign |

Per-complex: ~30-60s with MSA server.

Verify

find output -name "ranked_0.pdb" | wc -l  # Should match input count

Troubleshooting

Low pLDDT regions: May indicate disorder or poor design

Low ipTM: Interface not confident, check hotspots

High PAE off-diagonal: Chains may not interact

OOM errors: Use ColabFold with MSA server instead

Error interpretation

| Error | Cause | Fix |

|-------|-------|-----|

| RuntimeError: CUDA out of memory | Sequence too long | Use A100 or split prediction |

| KeyError: 'iptm' | Running monomer on complex | Use multimer preset |

| FileNotFoundError: database | Missing MSA databases | Use ColabFold MSA server |

| TimeoutError | MSA search slow | Reduce num_recycles |

Next: protein-design-qc for filtering and ranking.

Inputs

One or more protein sequences in FASTA format, optionally grouped as a complex.
Optional template structures, MSA settings, and recycle count overrides.
A prediction workspace with enough disk for intermediate features and outputs.

Outputs

Predicted structure files such as PDB/mmCIF plus per-model confidence JSON or PKL files.
Model-level confidence metrics including pLDDT, pTM, ipTM, and PAE matrices.
A ranked prediction set ready for protein-design-qc filtering or ipsae ranking.

Next Step

Run protein-design-qc to filter low-confidence models, then use ipsae when ranking binders for experiments.

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Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.

7k tokens

How to use it

Copy the folder

Take biotender-max/alphafold2-multimer from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

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