> adaptyv
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein
curl "https://skillshub.wtf/K-Dense-AI/claude-scientific-skills/adaptyv?format=md"Adaptyv
Adaptyv is a cloud laboratory platform that provides automated protein testing and validation services. Submit protein sequences via API or web interface and receive experimental results in approximately 21 days.
Quick Start
Authentication Setup
Adaptyv requires API authentication. Set up your credentials:
- Contact support@adaptyvbio.com to request API access (platform is in alpha/beta)
- Receive your API access token
- Set environment variable:
export ADAPTYV_API_KEY="your_api_key_here"
Or create a .env file:
ADAPTYV_API_KEY=your_api_key_here
Installation
Install the required package using uv:
uv pip install requests python-dotenv
Basic Usage
Submit protein sequences for testing:
import os
import requests
from dotenv import load_dotenv
load_dotenv()
api_key = os.getenv("ADAPTYV_API_KEY")
base_url = "https://kq5jp7qj7wdqklhsxmovkzn4l40obksv.lambda-url.eu-central-1.on.aws"
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
# Submit experiment
response = requests.post(
f"{base_url}/experiments",
headers=headers,
json={
"sequences": ">protein1\nMKVLWALLGLLGAA...",
"experiment_type": "binding",
"webhook_url": "https://your-webhook.com/callback"
}
)
experiment_id = response.json()["experiment_id"]
Available Experiment Types
Adaptyv supports multiple assay types:
- Binding assays - Test protein-target interactions using biolayer interferometry
- Expression testing - Measure protein expression levels
- Thermostability - Characterize protein thermal stability
- Enzyme activity - Assess enzymatic function
See reference/experiments.md for detailed information on each experiment type and workflows.
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
- SolubleMPNN - Sequence redesign for improved solubility
- ESM - Sequence likelihood scoring
- ipTM - Interface stability assessment
- pSAE - Hydrophobic exposure quantification
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
- Platform is currently in alpha/beta phase with features subject to change
- Not all platform features are available via API yet
- Results typically delivered in ~21 days
- Contact support@adaptyvbio.com for access requests or questions
- Suitable for high-throughput AI-driven protein design workflows
> related_skills --same-repo
> writing
Use this skill to create high-quality academic papers, literature reviews, grant proposals, clinical reports, and other research and scientific documents backed by comprehensive research and real, verifiable citations. Use this skill whenever the user asks for written output such as a report, paper...etc.
> xlsx
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my
> scikit-learn
Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
> pytorch-lightning
Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.