AI Protein Design Workflow

Backbone generation, sequence painting, structure prediction and binding-energy calculation.

Prompt

Create an AI protein design workflow diagram.

Layout:
- Left to right pipeline: target definition -> backbone generation -> sequence design / painting -> structure prediction -> binding-energy calculation -> experimental prioritization.
- Add feedback loops from failed binding-energy or structure-quality checks back to sequence design.
- Include small visual icons: protein backbone ribbon, amino-acid sequence strip, predicted structure, docking interface, ranked candidates table.
- Add QC metrics: pLDDT, RMSD, binding energy, interface contacts.

Style:
- Life-science AI workflow figure on white background.
- Use protein-ribbon visuals with restrained colors, navy labels, teal successful flow, coral feedback loops.
- Keep labels concise and scientifically plausible.
- Suitable for computational biology papers, protein engineering decks, and methods figures.

Use in Generator

When to use

For computational structural biology and protein engineering papers.

Variations

With wet-lab validation arm

Append a Stage 5 "Wet-Lab Validation" showing recombinant expression, biolayer interferometry binding measurement, and a comparison plot of computed vs measured K_d.

Tips

Mention each model class by analogy (RFdiffusion-class, AlphaFold-class) — generators reproduce the structure even if the exact name varies.
Show the pLDDT heatmap explicitly — it is the visual signal most readers expect at the structure step.
End with a numeric output. Workflows that end on a vague step lose impact.

FAQ

How do I add filtering between stages?

Add a small "filter" symbol (a funnel icon) on each inter-stage arrow with a one-line criterion (e.g., "pLDDT > 80, Ramachandran clean").