adaptyv-bio

Files (1)

• SKILL.md

SKILL.md (first lines)

# Adaptyv Bio

## Overview

Adaptyv Bio is a protein expression and characterization platform accessed via a REST API and Python SDK. Users submit protein sequences (antibodies, nanobodies, enzymes, binding proteins) and receive expressed protein along with binding affinity measurements (KD via biolayer interferometry) within days. The platform is designed for high-throughput directed evolution loops: generate candidate sequences (computationally or by library design) → order expression + assay via API → receive affinity data → retrain model or select top candidates → repeat. The SDK handles experiment submission, status polling, and result retrieval in Python.

## When to Use

- Screening computationally designed protein variants for experimental binding affinity validation
- Running ML-guided directed evolution loops where in silico candidate generation alternates with wet-lab characterization
- Ordering cell-free expression of nanobodies, antibodies, or binding domains without maintaining wet-lab infrastructure
- Automating high-throughput protein characterization pipelines using the REST API
- Integrating experimental affinity data (KD values) with computational models for Bayesian optimization of protein sequences
- Validating ESM, AlphaFold, or docking predictions with experimental binding data
- Use `benchling-integration` for LIMS-style sequence and plasmid management; use Adaptyv Bio instead when you need automated cell-free expression and affinity characterization without wet-lab setup

## Prerequisites

- **Python packages**: `adaptyvbio`, `requests`, `pandas`
- **Account**: Adaptyv Bio account required; obtain API key from dashboard
- **Data requirements**: protein sequence(s) in FASTA or plain string format; target protein specification

```bash
pip install adaptyvbio requests pandas
# Set API key as environment variable
export ADAPTYV_API_KEY="your_api_key_here"
```

## Quick Start

```python
import adaptyvbio as ab
import os

# Initialize client
client = ab.Client(api_key=os.environ["ADAPTYV_API_KEY"])

# List available experiment types
experiment_types = client.get_experiment_types()
for et in experiment_types:
    print(f"  {et['name']}: {et['description']}")
```

## Core API

### Module 1: Sequence Submission

Submit protein sequences for cell-free expression and characterization.

```python
import adaptyvbio as ab
import os

client = ab.Client(api_key=os.environ["ADAPTYV_API_KEY"])

# Submit a single protein sequence for expression
sequence = "MAQRITLPSGMKELRLSYNMGEIVYKIEPVGSIVHIEYYDPENKDTLVNKPSDIVELTMPGKLVVENAKTFAEK"

submission = client.submit_experiment(
    experiment_type="expression",   # "expression" or "binding"
    sequences=[sequence],
    metadata={
        "project": "nanobody_optimization_round1",
        "designer": "ESM2_1000_candidates",
    }
)

experiment_id = submission["experiment_id"]
print(f"Submitted experiment: {experiment_id}")
print(f"Status: {submission['status']}")
print(f"Estimated completion: {submission.get('estimated_completion', 'N/A')}")
```

```python
# Submit batch of sequences (up to 96 per experiment)
import pandas as pd

# Load candidate sequences from CSV
candidates = pd.read_csv("esm_candidates.csv")  # columns: name, sequence, score
top_candidates = candidates.nlargest(48, "score")

sequences = top_candidates["sequence"].tolist()
names = top_candidates["name"].tolist()

batch_submission = client.submit_experiment(
    experiment_type="binding",
    sequences=sequences,
    sequence_names=names,
    target="target_protein_name",  # registered target in your Adaptyv account
    metadata={"round": 2, "parent_experiment": experiment_id}
)
print(f"Batch experiment: {batch_submission['experiment_id']}")
print(f"Sequences submitted: {len(sequences)}")
```

### Module 2: Experiment Status Tracking

Poll experiment status and retrieve results when complete.

```python
import adaptyvbio as ab
import os
import time

client = ab.Client(api_key=os.environ["ADAPTYV_API_KEY"])
experiment_id = "exp_abc123"  # from submission step

# Check current status
status = client.get_experiment_status(experiment_id)
print(f"Status: {status['status']}")  # "pending", "running", "complete", "failed"
print(f"Progress: {status.get('progress', 0):.0%}")

# Poll until complete (with timeout)
max_wait_hours = 72
poll_interval_minutes = 30
timeout = max_wait_hours * 3600

start = time.time()
while time.time() - start < timeout:

From FridrichMethod/awesome-skills, MIT.

More from FridrichMethod/awesome-skills