Which protein optimization tools are supported?

The skill supports tools such as NetSolP, SoluProt, SolubleMPNN for solubility redesign, ESM for likelihood scoring, and pSAE for hydrophobic exposure.

How long does it take to receive experimental results?

Experimental results from the Adaptyv cloud lab are typically delivered in approximately 21 days.

Do I need an API key to use Adaptyv?

Yes, you must contact support@adaptyvbio.com to request an API access token, which is then configured as an environment variable.

What is the Adaptyv Claude skill used for?

It is used to automate protein testing and validation by connecting Claude to Adaptyv's cloud laboratory platform for assays like binding, expression, and thermostability.

Is Adaptyv suitable for high-throughput workflows?

Yes, it is specifically designed for high-throughput, AI-driven protein design cycles that require empirical wet-lab validation.

Adaptyv Protein Engineering

Name: Adaptyv Protein Engineering
Author: BbgnsurfTech

byBbgnsurfTech

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데이터 과학 및 ML

Streamlines automated protein testing and validation through cloud-based laboratory workflows and sequence optimization tools.

소개

The Adaptyv skill for Claude bridges the gap between computational protein design and physical wet-lab validation. It enables researchers and developers to submit protein sequences for automated testing—including binding assays, expression testing, and thermostability measurements—directly via API. Beyond lab submissions, the skill integrates with advanced computational tools like SolubleMPNN and ESM to optimize sequences for solubility and expression, providing a comprehensive end-to-end workflow for high-throughput protein engineering within the Claude environment.

주요 기능

Support for binding assays, expression testing, and thermostability measurements
Hydrophobic exposure quantification and interface stability assessment
Automated wet-lab experiment submission and status tracking via API
Protein sequence optimization using NetSolP, SoluProt, and SolubleMPNN
3 GitHub stars
Integration with ESM likelihood scoring for sequence validation

사용 사례

Managing high-throughput protein engineering cycles with automated feedback loops
Optimizing protein solubility and expression levels before synthesis
Validating AI-generated protein designs through physical laboratory assays

소개

주요 기능

Support for binding assays, expression testing, and thermostability measurements
Hydrophobic exposure quantification and interface stability assessment
Automated wet-lab experiment submission and status tracking via API
Protein sequence optimization using NetSolP, SoluProt, and SolubleMPNN
3 GitHub stars
Integration with ESM likelihood scoring for sequence validation

사용 사례

Managing high-throughput protein engineering cycles with automated feedback loops
Optimizing protein solubility and expression levels before synthesis
Validating AI-generated protein designs through physical laboratory assays