Agent skill

chai

Structure prediction using Chai-1, a foundation model for molecular structure. Use this skill when: (1) Predicting protein-protein complex structures, (2) Validating designed binders, (3) Predicting protein-ligand complexes, (4) Using the Chai API for high-throughput prediction, (5) Need an alternative to AlphaFold2. For QC thresholds, use protein-qc. For AlphaFold2 prediction, use alphafold. For ESM-based analysis, use esm.

View SKILL.md on GitHub Repository
Stars 125
Forks 14

Install this agent skill to your Project

npx add-skill https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/chai

SKILL.md

Chai-1 Structure Prediction

Prerequisites

Requirement Minimum Recommended
Python 3.10+ 3.11
CUDA 12.0+ 12.1+
GPU VRAM 24GB 40GB (A100)
RAM 32GB 64GB

How to run

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

Option 1: Modal

bash
cd biomodals
modal run modal_chai1.py \
  --input-faa complex.fasta \
  --out-dir predictions/

GPU: A100 (40GB) | Timeout: 30min default

Option 2: Chai API (recommended)

bash
pip install chai_lab

python -c "
import chai_lab
from chai_lab.chai1 import run_inference

# Run prediction
run_inference(
    fasta_file='complex.fasta',
    output_dir='predictions/',
    num_trunk_recycles=3
)
"

Option 3: Local installation

bash
git clone https://github.com/chaidiscovery/chai-lab.git
cd chai-lab
pip install -e .

chai-lab predict \
  --fasta complex.fasta \
  --output predictions/

FASTA Format

Protein complex

>binder
MKTAYIAKQRQISFVKSHFSRQLE...
>target
MVLSPADKTNVKAAWGKVGAHAGE...

Protein + ligand

>protein
MKTAYIAKQRQISFVKSHFSRQLE...
>ligand|smiles
CCO

Protein + DNA/RNA

>protein
MKTAYIAKQRQISFVKSHFSRQLE...
>dna
ATCGATCGATCG

Key parameters

Parameter Default Range Description
num_trunk_recycles 3 1-10 Recycles (more = better)
num_diffn_timesteps 200 50-500 Diffusion steps
seed 0 int Random seed

Output format

predictions/
├── pred.model_idx_0.cif    # Best model (CIF format)
├── pred.model_idx_1.cif    # Second model
├── scores.json             # Confidence scores
├── pae.npy                 # PAE matrix
└── plddt.npy               # pLDDT values

Note: Chai-1 outputs CIF format. Convert to PDB if needed:

python
from Bio.PDB import MMCIFParser, PDBIO
parser = MMCIFParser()
structure = parser.get_structure("pred", "pred.model_idx_0.cif")
io = PDBIO()
io.set_structure(structure)
io.save("pred.model_idx_0.pdb")

Extracting metrics

python
import numpy as np
import json

# Load scores
with open('predictions/scores.json') as f:
    scores = json.load(f)

plddt = np.load('predictions/plddt.npy')
pae = np.load('predictions/pae.npy')

print(f"pLDDT: {plddt.mean():.3f}")
print(f"pTM: {scores['ptm']:.3f}")
print(f"ipTM: {scores.get('iptm', 'N/A')}")

Use cases

Binder validation

python
# Predict complex with Chai
chai-lab predict --fasta binder_target.fasta --output val/

# Check ipTM > 0.5
scores = json.load(open('val/scores.json'))
if scores['iptm'] > 0.5:
    print("Design passes validation")

Protein-ligand complex

python
# FASTA with SMILES
fasta = """
>protein
MKTA...
>ligand|smiles
CCO
"""

# Chai handles both protein and small molecules

Batch prediction

bash
# Multiple sequences
for fasta in sequences/*.fasta; do
    chai-lab predict \
        --fasta "$fasta" \
        --output "predictions/$(basename $fasta .fasta)"
done

Comparison with AF2

Aspect Chai-1 AlphaFold2
MSA required No Yes
Small molecules Yes No
DNA/RNA Yes Limited
Speed Faster Slower
Accuracy Comparable Reference

Sample output

Successful run

$ chai-lab predict --fasta complex.fasta --output predictions/
[INFO] Loading Chai-1 model...
[INFO] Running inference...
[INFO] Saved 5 models to predictions/

predictions/scores.json:
{
  "ptm": 0.82,
  "iptm": 0.71,
  "ranking_score": 0.76
}

What good output looks like:

  • pTM: > 0.7 (confident global structure)
  • ipTM: > 0.5 (confident interface, > 0.7 for high confidence)
  • CIF files with reasonable atom positions

Decision tree

Should I use Chai?
│
├─ What are you predicting?
│  ├─ Protein-protein complex → Chai ✓ or ColabFold
│  ├─ Protein + small molecule → Chai ✓
│  ├─ Protein + DNA/RNA → Chai ✓
│  └─ Single protein only → Use ESMFold (faster)
│
├─ Need MSA?
│  ├─ No / want speed → Chai ✓
│  └─ Yes / want accuracy → ColabFold
│
└─ Priority?
   ├─ Highest accuracy → ColabFold with MSA
   ├─ Speed / no MSA → Chai ✓
   └─ Ligand binding → Chai ✓

Typical performance

Campaign Size Time (A100) Cost (Modal) Notes
100 complexes 30-60 min ~$10 Standard validation
500 complexes 2-4h ~$45 Large campaign
1000 complexes 5-8h ~$90 Comprehensive

Per-complex: ~20-40s for typical binder-target complex.

Verify

bash
find predictions -name "*.cif" | wc -l  # Should match input count

Troubleshooting

Low pLDDT: Increase num_trunk_recycles Low ipTM: Check chain order, interface region OOM errors: Use A100-80GB or reduce batch Slow prediction: Reduce num_diffn_timesteps

Error interpretation

Error Cause Fix
RuntimeError: CUDA out of memory Complex too large Use A100-80GB or split prediction
KeyError: 'iptm' Single chain predicted Ensure FASTA has multiple chains
ValueError: invalid SMILES Malformed ligand Validate SMILES with RDKit
torch.cuda.OutOfMemoryError GPU exhausted Reduce num_diffn_timesteps to 100

Next: protein-qc for filtering and ranking.

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

adaptyvbio/protein-design-skills

proteinmpnn

Design protein sequences using ProteinMPNN inverse folding. Use this skill when: (1) Designing sequences for RFdiffusion backbones, (2) Redesigning existing protein sequences, (3) Fixing specific residues while designing others, (4) Optimizing sequences for expression or stability, (5) Multi-state or negative design. For backbone generation, use rfdiffusion or bindcraft. For ligand-aware design, use ligandmpnn. For solubility optimization, use solublempnn.

125 14
Explore
adaptyvbio/protein-design-skills

campaign-manager

Goal-oriented binder design campaign planning and health assessment. Use this skill when: (1) Planning a complete binder design campaign, (2) Converting high-level goals into runnable pipelines, (3) Assessing campaign health and pass rates, (4) Diagnosing why designs are failing QC, (5) Estimating time, cost, and expected yields, (6) Selecting between design tools for a specific target. This skill orchestrates the other protein design tools. For individual tool parameters, use the specific tool skills.

125 14
Explore
adaptyvbio/protein-design-skills

esm

ESM2 protein language model for embeddings and sequence scoring. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) scores, (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect prediction, (5) Analyzing sequence-function relationships. For structure prediction, use chai or boltz. For QC thresholds, use protein-qc.

125 14
Explore
adaptyvbio/protein-design-skills

binding-characterization

Guidance for SPR and BLI binding characterization experiments. Use when: (1) Planning binding kinetics experiments, (2) Troubleshooting poor/no binding signal, (3) Interpreting kinetic data artifacts, (4) Choosing between SPR vs BLI platforms.

125 14
Explore
adaptyvbio/protein-design-skills

cell-free-expression

Guidance for cell-free protein synthesis (CFPS) optimization. Use when: (1) Planning CFPS experiments, (2) Troubleshooting low yield or aggregation, (3) Optimizing DNA template design for CFPS, (4) Expressing difficult proteins (disulfide-rich, toxic, membrane).

125 14
Explore
adaptyvbio/protein-design-skills

ligandmpnn

Ligand-aware protein sequence design using LigandMPNN. Use this skill when: (1) Designing sequences around small molecules, (2) Enzyme active site design, (3) Ligand binding pocket optimization, (4) Metal coordination site design, (5) Cofactor binding proteins. For standard protein design, use proteinmpnn. For solubility optimization, use solublempnn.

125 14
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results