Individual skill
protein-binder-design
Design and validate de novo protein binders with the current NVIDIA BioNeMo Agent Toolkit workflow, while adapting honestly when NVIDIA-hosted credentials are unavailable.
backend/cli/skills/biology/protein-binder-design/SKILL.md
Source commit a08e0ad0ad3d6448123039ee4659163eb2e35d99
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- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
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- No
- Skill scan complete
- No
- Observed behavior
- Network or broad shell
- Last checked
- Nov 2, 2026
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Repository context
- Repository stars (not skill ratings)
- 4,354
- Repository last updated
- Nov 7, 2026
- License
- Apache-2.0
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- agent Repository topic
- ai Repository topic
- bun Repository topic
- cli Repository topic
- llm Repository topic
- ml Repository topic
- open-source Repository topic
- research Repository topic
- science Repository topic
- ai-agent Repository topic
- protein-design Declared skill tag
- bionemo Declared skill tag
- modal Declared skill tag
- design Derived category
- operations Derived category
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# Protein binder design
Use this workflow for an end-to-end binder campaign: target preparation, backbone generation, sequence design, independent complex prediction, interface scoring, diversity analysis, ranking, structures, and a reproducible report.
The reviewed upstream source is NVIDIA BioNeMo Agent Toolkit commit `0e67a612e4045f007e38fa77adc8f3ebfc5616b6`. Its canonical workflows are `workflows/generative-protein-binder-design/protein-binder-design` and `workflows/generative-protein-binder-design/complexa-binder-design` (`skills/bionemo-agent-toolkit/` is the generated aggregate). Record that commit and every model/version actually used.
This adapted skill is self-contained; it has no local helper files or references to inspect. Resolve upstream details through the pinned public repository only when the active route needs them.
For a single hosted prediction (Boltz-2, OpenFold, DiffDock, ProteinMPNN, RFdiffusion) with the user's own NVIDIA API key, load `bionemo-nims` instead: it goes through the `scientific_capability` tool and needs no Modal target. This skill is the self-hosted campaign route.
## Start with capabilities
1. Call `compute_job` with `action: "targets"` once. Respect the returned Modal network and credential capabilities.
2. Use the configured Modal target for detached GPU work. Never call the Modal CLI/SDK directly and never place credentials in commands.