Adaptyv

SkillDev tools

Submits and tracks protein-testing experiments on the Adaptyv Bio Foundry cloud lab (wet-lab validation), and optimizes protein sequences before submission with computational tools (NetSolP, SoluProt, SolubleMPNN, ESM). Use when designing proteins that need wet-lab validation - binding/affinity screening, expression testing, thermostability, or fluorescence assays - or when submitting experiments to the Foundry API, browsing the target catalog, tracking experiment status, retrieving results, or pre-screening sequences for solubility/expression. Triggers on "Adaptyv", "Foundry API", "cloud lab", "biolayer interferometry / BLI", "wet-lab validation". Part of the AlterLab Academic Skills suite.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Adaptyv skill

What this skill tells your AI

The instructions your AI receives, as published by alterlab-ieu/alterlab-academic-skills in skills/domain-specific/alterlab-adaptyv/SKILL.md and read by ahel’s review.

Adaptyv Bio runs the Foundry cloud lab: submit protein sequences and a target, the lab runs the assay, and you retrieve experimental data (binding/affinity, thermostability, expression, fluorescence). The public Foundry API drives the full lifecycle programmatically. Turnaround is on the order of weeks; confirm the current estimate from the per-experiment quote rather than assuming a fixed number.

The exact request/response shapes evolve. This skill captures the verified API contract and conventions; for the authoritative spec see the OpenAPI doc at https://foundry-api-public.adaptyvbio.com/api/v1/openapi.json and https://docs.adaptyvbio.com.

Prefer the official tooling first

Adaptyv ships its own integrations - reach for them before hand-rolling requests:

  • Official Python SDKgithub.com/adaptyvbio/adaptyv-sdk (MIT). Decorator-based: wrap a design function with @lab.experiment(target=...); reads ADAPTYV_API_KEY / ADAPTYV_API_URL (and optional ADAPTYV_ORGANIZATION_ID) from the environment. Install from source (pip install -e . after cloning — no PyPI release confirmed; verify before pinning).
  • Adaptyv's own Claude Code skillsgithub.com/adaptyvbio/protein-design-skills. Useful prior art for protein-design + Foundry workflows.

Use this skill's raw-requests recipes when the SDK is unavailable or you need fine control over the lifecycle.

Quick Start

Authentication Setup

  1. Create a token in the Foundry portal: https://foundry.adaptyvbio.com/Organization → Settings → Tokens (pick a role: Member = read/write, Viewer = read-only; set an expiry). The token value is shown only once — copy it immediately.
  2. Set it in your environment (never commit it):
export ADAPTYV_API_KEY="your_token_here"

Or put it in a gitignored .env:

ADAPTYV_API_KEY=your_token_here

Installation

If using the raw API directly:

uv pip install requests python-dotenv

Basic Usage

The API uses a draft → submit flow: create an experiment (it starts as a draft), then submit it. sequences is a {label: amino_acid_string} map (multi-chain constructs join chains with a colon, e.g. "heavy:light").

import os
import requests
from dotenv import load_dotenv

load_dotenv()

api_key = os.getenv("ADAPTYV_API_KEY")
base_url = "https://foundry-api-public.adaptyvbio.com/api/v1"
headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json",
}

# 1. Create a draft experiment
resp = requests.post(
    f"{base_url}/experiments",
    headers=headers,
    json={
        "name": "mini-binder round 1",
        "experiment_spec": {
            "experiment_type": "affinity",   # screening|affinity|thermostability|fluorescence|expression
            "method": "bli",                 # bli|spr (for binding-type assays)
            "target_id": "<target uuid from GET /targets>",
            "sequences": {
                "design_a": "MKVLWALLGLLGAA...",
                "design_b": "MATGVLWALLG...",
            },
        },
    },
)
resp.raise_for_status()
experiment_id = resp.json()["experiment_id"]

# 2. Submit it to the lab (after reviewing the quote — see reference/api_reference.md)
requests.post(f"{base_url}/experiments/{experiment_id}/submit", headers=headers).raise_for_status()

Available Experiment Types

Foundry supports these experiment_type values:

  • screening - Binding detection via biolayer interferometry (BLI) or SPR. Requires a target.
  • affinity - Kinetic constants (KD, kon, koff) by BLI/SPR. Requires a target.
  • thermostability - Melting temperature (Tm) via DSF. No target required.
  • fluorescence - Fluorescence intensity. No target required.
  • expression - Protein yield quantification. No target required.

See reference/experiments.md for detailed information on each assay and its outputs.

Protein Sequence Optimization

Before submitting sequences, optimize them for better expression and stability:

Common issues to address:

  • Unpaired cysteines that create unwanted disulfides
  • Excessive hydrophobic regions causing aggregation
  • Poor solubility predictions

Recommended tools:

  • NetSolP / SoluProt - Initial solubility filtering (both are web services, not pip packages)
  • SolubleMPNN - Solubility-biased sequence redesign (a weight set within the ProteinMPNN / LigandMPNN family)
  • ESM (fair-esm) - Sequence likelihood / naturalness scoring
  • ipTM (AlphaFold-Multimer / ColabFold) - Interface stability for binder designs
  • pSAE - Solvent-accessible hydrophobic exposure, from a predicted/known structure

See reference/protein_optimization.md for detailed optimization workflows and tool usage.

API Reference

For complete API documentation including all endpoints, request/response formats, and authentication details, see reference/api_reference.md.

Examples

For concrete code examples covering common use cases (experiment submission, status tracking, result retrieval, batch processing), see reference/examples.md.

Important Notes

  • The Foundry API is public but still evolving — treat the OpenAPI doc (/api/v1/openapi.json) as the source of truth and verify field names before relying on them.
  • Submission is two-step: create a draft, review the cost quote, then POST .../submit. Nothing is charged until you confirm the quote.
  • affinity/screening require a target_id from the catalog (GET /targets); thermostability, fluorescence, and expression do not.
  • Turnaround is multiple weeks — read the estimate from the experiment/quote rather than assuming a fixed number.
  • Support and docs: support@adaptyvbio.com / https://docs.adaptyvbio.com.
  • Suitable for high-throughput AI-driven protein design workflows (closed-loop design → test → learn).

Signals

GitHub stars
66
Forks
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Last commit
Sep 2026
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Catalog kind
skill
Gateway key
alterlab-adaptyv
Source
github.com/alterlab-ieu/alterlab-academic-skills