Design VHH nanobody binders against target proteins using structure-conditioned generation.
npx skills add https://github.com/junior1p/ProteinClaw --skill mber
Please help me install this Agent Skill. Skill: mber Source: https://github.com/junior1p/ProteinClaw/tree/main/skills/mber Command: npx skills add https://github.com/junior1p/ProteinClaw --skill mber
mBER enables the design and refinement of VHH nanobody binders against protein targets using structure-conditioned generation and AlphaFold-Multimer guidance.
Provide a target protein and run the mBER design workflow to generate a VHH binder against it.
High-intent search queries and answers about installing and using this skill.
You can design VHH nanobody binders using structure-conditioned generation, which leverages AlphaFold-Multimer guidance to generate and refine binders against your specific target protein through a multi-stage design workflow.
Yes, you can redesign CDR1, CDR2, and CDR3 loops on existing scaffolds. This structure-conditioned generation allows you to specifically target and optimize hotspots on the target antigens during the redesign process.
Nanobody binder design handles multi-chain target antigens by using chain-offset support. This feature allows the design workflow to process complex targets with multiple chains effectively during structure-conditioned generation.
Yes, AlphaFold-Multimer conditioning is explicitly supported for VHH binder optimization. The design workflow satisfies AF-Multimer conditioning, sequence masking, and multi-stage design requirements to generate robust binders.
Structure-conditioned nanobody design requires target protein structural inputs and relies on AlphaFold-Multimer conditioning. It is specifically tailored for VHH binders and CDR loop redesign rather than general antibody fragment engineering.
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