adaptyv-bio

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

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

Quick Start

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.

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')}")

# 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.

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:
    status = client.get_experiment_status(experiment_id)
    print(f"[{time.strftime('%H:%M')}] Status: {status['status']}")
    if status["status"] in ("complete", "failed"):
        break
    time.sleep(poll_interval_minutes * 60)

print(f"Final status: {status['status']}")

Module 3: Results Retrieval

Download and parse experiment results.

import adaptyvbio as ab
import pandas as pd
import os

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

# Get results (only available when status is "complete")
results = client.get_experiment_results(experiment_id)

# Convert to DataFrame
records = []
for result in results["results"]:
    records.append({
        "name": result.get("sequence_name", "unnamed"),
        "sequence": result["sequence"],
        "kd_nM": result.get("kd_nM"),          # binding dissociation constant
        "yield_ug": result.get("yield_ug"),      # expression yield
        "expression_pass": result.get("expression_pass"),
        "binding_pass": result.get("binding_pass"),
    })

df = pd.DataFrame(records)
df = df.sort_values("kd_nM", ascending=True)  # rank by affinity (lower KD = tighter binding)

print(f"Results: {len(df)} sequences")
print(f"Successfully expressed: {df['expression_pass'].sum()}")
print(f"KD range: {df['kd_nM'].min():.2f} – {df['kd_nM'].max():.2f} nM")
print(df[["name", "kd_nM", "yield_ug", "expression_pass"]].head(10).to_string())

df.to_csv(f"{experiment_id}_results.csv", index=False)

Module 4: Experiment History and Project Management

import adaptyvbio as ab
import pandas as pd
import os

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

# List all experiments
experiments = client.list_experiments(project="nanobody_optimization")
print(f"Total experiments: {len(experiments)}")

for exp in experiments:
    print(f"  {exp['experiment_id']}: {exp['status']} | "
          f"{exp['n_sequences']} seqs | {exp['created_at'][:10]}")

# Retrieve all results across experiments for a project
all_results = []
for exp in experiments:
    if exp["status"] == "complete":
        results = client.get_experiment_results(exp["experiment_id"])
        for r in results["results"]:
            r["experiment_id"] = exp["experiment_id"]
            r["round"] = exp.get("metadata", {}).get("round", "unknown")
            all_results.append(r)

project_df = pd.DataFrame(all_results)
print(f"\nAll results: {len(project_df)} sequences across {len(experiments)} experiments")
print(f"Best KD: {project_df['kd_nM'].min():.3f} nM")

Module 5: Integration with Sequence Design

Integrate Adaptyv Bio results with computational protein design tools.

import adaptyvbio as ab
import pandas as pd
import numpy as np
import os

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

def run_design_iteration(previous_results_df, n_new_candidates=48):
    """
    Closed-loop protein engineering iteration.
    Input: DataFrame with sequence, kd_nM from previous round
    Output: submitted experiment_id for new round
    """
    # Select top performers as parents for next round
    parents = previous_results_df.nsmallest(5, "kd_nM")["sequence"].tolist()
    print(f"Top parent KDs: {previous_results_df.nsmallest(5, 'kd_nM')['kd_nM'].values}")

    # --- Placeholder for computational design step ---
    # In practice: call ESM, ProteinMPNN, or mutation scanning here
    # new_sequences = design_model.generate(parents, n=n_new_candidates)
    # For demonstration, create random variants:
    new_sequ