adaptyv

分类: 开发与编程 | 上传者: K-Dense-AI | 下载: 0 | 版本: v1.0（最新）

如何使用 Adaptyv Bio Foundry API 和 Python SDK 进行蛋白质实验设计、提交和结果检索。每当用户提及 Adaptyv、Foundry API、蛋白质结合测定、蛋白质筛选实验、BLI/SPR 测定、热稳定性测定或想要提交蛋白质序列进行实验表征时，请使用此技能。当代码导入“adaptyv”、“adaptyv_sdk”或“FoundryClient”或引用“foundry-api-public.adaptyvbio.com”时也会触发。

更新日志: Source: GitHub https://github.com/K-Dense-AI/claude-scientific-skills

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📄 SKILL.md 7.5 KB

SKILL.md

---
name: adaptyv
author: "K-Dense, Inc."
description: "How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`."
---

# Adaptyv Bio Foundry API

Adaptyv Bio is a cloud lab that turns protein sequences into experimental data. Users submit amino acid sequences via API or UI; Adaptyv's automated lab runs assays (binding, thermostability, expression, fluorescence) and delivers results in ~21 days.

## Quick Start

**Base URL:** `https://foundry-api-public.adaptyvbio.com/api/v1`

**Authentication:** Bearer token in the `Authorization` header. Tokens are obtained from [foundry.adaptyvbio.com](https://foundry.adaptyvbio.com/) sidebar.

When writing code, always read the API key from the environment variable `ADAPTYV_API_KEY` or from a `.env` file — never hardcode tokens. Check for a `.env` file in the project root first; if one exists, use a library like `python-dotenv` to load it.

```bash
export FOUNDRY_API_TOKEN="abs0_..."
curl https://foundry-api-public.adaptyvbio.com/api/v1/targets?limit=3 \
  -H "Authorization: Bearer $FOUNDRY_API_TOKEN"
```

Every request except `GET /openapi.json` requires authentication. Store tokens in environment variables or `.env` files — never commit them to source control.

## Python SDK

Install: `uv add adaptyv-sdk` (falls back to `uv pip install adaptyv-sdk` if no `pyproject.toml` exists)

**Environment variables** (set in shell or `.env` file):
```bash
ADAPTYV_API_KEY=your_api_key
ADAPTYV_API_URL=https://foundry-api-public.adaptyvbio.com/api/v1
```

### Decorator Pattern

```python
from adaptyv import lab

@lab.experiment(target="PD-L1", experiment_type="screening", method="bli")
def design_binders():
    return {"design_a": "MVKVGVNG...", "design_b": "MKVLVAG..."}

result = design_binders()
print(f"Experiment: {result.experiment_url}")
```

### Client Pattern

```python
from adaptyv import FoundryClient

client = FoundryClient(api_key="...", base_url="https://foundry-api-public.adaptyvbio.com/api/v1")

# Browse targets
targets = client.targets.list(search="EGFR", selfservice_only=True)

# Estimate cost
estimate = client.experiments.cost_estimate({
    "experiment_spec": {
        "experiment_type": "screening",
        "method": "bli",
        "target_id": "target-uuid",
        "sequences": {"seq1": "EVQLVESGGGLVQ..."},
        "n_replicates": 3
    }
})

# Create and submit
exp = client.experiments.create({...})
client.experiments.submit(exp.experiment_id)

# Later: retrieve results
results = client.experiments.get_results(exp.experiment_id)
```

## Experiment Types

| Type | Method | Measures | Requires Target |
|---|---|---|---|
| `affinity` | `bli` or `spr` | KD, kon, koff kinetics | Yes |
| `screening` | `bli` or `spr` | Yes/no binding | Yes |
| `thermostability` | — | Melting temperature (Tm) | No |
| `expression` | — | Expression yield | No |
| `fluorescence` | — | Fluorescence intensity | No |

## Experiment Lifecycle

```
Draft → WaitingForConfirmation → QuoteSent → WaitingForMaterials → InQueue → InProduction → DataAnalysis → InReview → Done
```

| Status | Who Acts | Description |
|---|---|---|
| `Draft` | You | Editable, no cost commitment |
| `WaitingForConfirmation` | Adaptyv | Under review, quote being prepared |
| `QuoteSent` | You | Review and confirm the quote |
| `WaitingForMaterials` | Adaptyv | Gene fragments and target ordered |
| `InQueue` | Adaptyv | Materials arrived, queued for lab |
| `InProduction` | Adaptyv | Assay running |
| `DataAnalysis` | Adaptyv | Raw data processing and QC |
| `InReview` | Adaptyv | Final validation |
| `Done` | You | Results available |
| `Canceled` | Either | Experiment canceled |

The `results_status` field on an experiment tracks: `none`, `partial`, or `all`.

## Common Workflows

### 1. Submit a Binding Screen (Step by Step)

```python
# 1. Find a target
targets = client.targets.list(search="EGFR", selfservice_only=True)
target_id = targets.items[0].id

# 2. Preview cost
estimate = client.experiments.cost_estimate({
    "experiment_spec": {
        "experiment_type": "screening",
        "method": "bli",
        "target_id": target_id,
        "sequences": {"seq1": "EVQLVESGGGLVQ...", "seq2": "MKVLVAG..."},
        "n_replicates": 3
    }
})

# 3. Create experiment (starts as Draft)
exp = client.experiments.create({
    "name": "EGFR binder screen batch 1",
    "experiment_spec": {
        "experiment_type": "screening",
        "method": "bli",
        "target_id": target_id,
        "sequences": {"seq1": "EVQLVESGGGLVQ...", "seq2": "MKVLVAG..."},
        "n_replicates": 3
    }
})

# 4. Submit for review
client.experiments.submit(exp.experiment_id)

# 5. Poll or use webhooks until Done
# 6. Retrieve results
results = client.experiments.get_results(exp.experiment_id)
```

### 2. Automated Pipeline (Skip Draft + Auto-Accept Quote)

```python
exp = client.experiments.create({
    "name": "Auto pipeline run",
    "experiment_spec": {...},
    "skip_draft": True,
    "auto_accept_quote": True,
    "webhook_url": "https://my-server.com/webhook"
})
# Webhook fires on each status transition; poll or wait for Done
```

### 3. Using Webhooks

Pass `webhook_url` when creating an experiment. Adaptyv POSTs to that URL on every status transition with the experiment ID, previous status, and new status.

## Sequences

- Simple format: `{"seq1": "EVQLVESGGGLVQPGGSLRLSCAAS"}`
- Rich format: `{"seq1": {"aa_string": "EVQLVESGGGLVQ...", "control": false, "metadata": {"type": "scfv"}}}`
- Multi-chain: use colon separator — `"MVLS:EVQL"`
- Valid amino acids: A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W, Y (case-insensitive, stored uppercase)
- Sequences can only be added to experiments in `Draft` status

## Filtering, Sorting, and Pagination

All list endpoints support pagination (`limit` 1-100, default 50; `offset`), search (free-text on name fields), and sorting.

**Filtering** uses s-expression syntax via the `filter` query parameter:
- Comparison: `eq(field,value)`, `neq`, `gt`, `gte`, `lt`, `lte`, `contains(field,substring)`
- Range/set: `between(field,lo,hi)`, `in(field,v1,v2,...)`
- Logic: `and(expr1,expr2,...)`, `or(...)`, `not(expr)`
- Null: `is_null(field)`, `is_not_null(field)`
- JSONB: `at(field,key)` — e.g., `eq(at(metadata,score),42)`
- Cast: `float()`, `int()`, `text()`, `timestamp()`, `date()`

**Sorting** uses `asc(field)` or `desc(field)`, comma-separated (max 8):
```
sort=desc(created_at),asc(name)
```

**Example:** `filter=and(gte(created_at,2026-01-01),eq(status,done))`

## Error Handling

All errors return:
```json
{
  "error": "Human-readable description",
  "request_id": "req_019462a4-b1c2-7def-8901-23456789abcd"
}
```
The `request_id` is also in the `x-request-id` response header — include it when contacting support.

## Token Management

Tokens use Biscuit-based cryptographic attenuation. You can create restricted tokens scoped by organization, resource type, actions (read/create/update), and expiry via `POST /tokens/attenuate`. Revoking a token (`POST /tokens/revoke`) revokes it and all its descendants.

## Detailed API Reference

For the full list of all 32 endpoints with request/response schemas, read `references/api-endpoints.md`.

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