Automate protein testing and validation through the Adaptyv API.
npx skills add https://github.com/yf8578/clawomics --skill adaptyv-yf8578
Please help me install this Agent Skill. Skill: adaptyv Source: https://github.com/yf8578/clawomics/tree/main/skills/adaptyv Command: npx skills add https://github.com/yf8578/clawomics --skill adaptyv-yf8578
This Skill automates protein testing and validation, streamlining the process of designing proteins and obtaining experimental results for binding assays, expression testing, and more.
Set up your ADAPTYV_API_KEY environment variable and then run uv pip install requests python-dotenv before executing the provided Python examples.
High-intent search queries and answers about installing and using this skill.
Automating protein testing involves submitting protein sequences to a cloud laboratory platform via API to run binding assays, expression testing, thermostability measurements, and enzyme activity assays. This Skill programmatically controls submission, tracks status, and retrieves experimental results.
Yes, optimizing protein sequences for expression and stability before experimental validation is possible using computational tools. This Skill integrates NetSolP, SoluProt, SolubleMPNN, and ESM to computationally optimize sequences for better expression and thermostability prior to cloud laboratory submission.
Submitting protein variants to a cloud laboratory requires programmatic API integration. After setting up your ADAPTYV_API_KEY environment variable, you can submit sequences, track their testing status, and retrieve experimental validation results directly through Python scripts.
Yes, you need an ADAPTYV_API_KEY environment variable to run computational biology scripts for protein engineering. You must also install the requests and python-dotenv dependencies to enable programmatic submission and results retrieval from the cloud laboratory.
Validating computationally designed therapeutic antibodies is best achieved by combining computational optimization with experimental testing. You can submit multiple antibody variants to be optimized for expression, then experimentally validated for binding affinity to a target antigen through a cloud laboratory.
Limitations of computational protein sequence optimization include the inability to guarantee experimental success without physical testing. Tools like ESM and SolubleMPNN predict stability and expression, but actual binding affinity and enzyme activity still require downstream cloud laboratory validation.
ryan-ressmeyerRetrieve open-access PDF URLs for academic papers via Unpaywall, PubMed Central, arXiv, and Semantic Scholar.
felixboehmSubmit protein sequences for automated experimental assays and computational optimization.
biomaps-infraSubmit protein sequences for experimental validation via a cloud laboratory API.
yf8578Retrieve AlphaFold protein structures by UniProt ID and download PDB/mmCIF files.
