Alphafold

Validate protein designs using AlphaFold2 structure prediction.

Install

npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill alphafold -g -a claude-code -y

Press to copy:C Claude CodeX CodexU CursorA Ask your agent

Repository: FreedomIntelligence/OpenClaw-Medical-Skills3.1k
Category: Design
Updated: 4mo ago
License: MIT
Context cost: Lightabout 1,016 tokens
Runs scripts: No, instructions only

What it does

Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (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 esm. For QC thresholds, use protein-qc.

AlphaFold2 Structure Validation

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/alphafold.git
cd alphafold

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

Parameter	Default	Options	Description
`--model_preset`	monomer	monomer/multimer	Model type
`--num_recycle`	3	1-20	Recycling iterations
`--max_template_date`	-	YYYY-MM-DD	Template cutoff
`--use_templates`	True	True/False	Use template search

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

Read the whole SKILL.md on GitHub