Is an API key required to use the Adaptyv skill?

Yes, you must contact support@adaptyvbio.com to request an API token, as the platform is currently in alpha/beta phase.

How long does it take to receive experimental results?

Experimental results are typically processed and delivered back to the user in approximately 21 days.

What experiment types does the Adaptyv skill support?

It supports binding assays (using biolayer interferometry), expression testing, thermostability characterization, and enzyme activity assays.

How do I install the dependencies for this skill?

You can install the required Python packages using 'uv pip install requests python-dotenv' and setting your ADAPTYV_API_KEY environment variable.

Can this skill help optimize protein solubility?

Yes, the skill provides access to tools like NetSolP, SoluProt, and SolubleMPNN to redesign sequences for better expression and solubility before submission.

Adaptyv Protein Design & Validation

Name: Adaptyv Protein Design & Validation
Author: Sologa

bySologa

Data Science & ML

Automates protein testing and validation through a cloud-based laboratory platform for accelerated biotechnological research and sequence optimization.

About

Adaptyv integrates Claude with a remote robotic laboratory to streamline the protein engineering lifecycle. This skill allows researchers to design proteins and submit sequences for experimental validation—including binding assays, expression testing, and thermostability measurements—directly through an API. It bridges the gap between computational design and wet-lab results by offering optimization tools like SolubleMPNN and ESM to ensure sequences are viable before synthesis, ultimately delivering experimental data in roughly 21 days.

Key Features

Automated wet-lab validation for binding, expression, and thermostability
Automated tracking of experiment status and result retrieval
Protein sequence optimization using NetSolP, SoluProt, and SolubleMPNN
Seamless API integration for high-throughput experiment submission
Computational sequence likelihood scoring with ESM and ipTM
0 GitHub stars

Use Cases

Optimizing protein sequences for improved solubility and expression before lab synthesis
Conducting high-throughput binding assays for antibody engineering and drug discovery
Managing a closed-loop AI-to-lab workflow for synthetic biology research

About

Key Features

Automated wet-lab validation for binding, expression, and thermostability
Automated tracking of experiment status and result retrieval
Protein sequence optimization using NetSolP, SoluProt, and SolubleMPNN
Seamless API integration for high-throughput experiment submission
Computational sequence likelihood scoring with ESM and ipTM
0 GitHub stars

Use Cases

Optimizing protein sequences for improved solubility and expression before lab synthesis
Conducting high-throughput binding assays for antibody engineering and drug discovery
Managing a closed-loop AI-to-lab workflow for synthetic biology research