From Sequence to Quantum Fold
The complete BioQuantum workflow — build, validate, run, and iterate.
🧬This guide walks through the full BioQuantum method using a real example sequence: AGGAGA (6 residues). By the end you will have run two quantum algorithms, compared their outputs, and understood when to redesign.
STEP 1: Design Your Sequence
Navigate to: Peptide Designer
Two ways to build your sequence:
OPTION A — Quick Input (recommended for beginners):
- Click the "Quick Input" field at the top
- Type or paste:
AGGAGA - Press Ctrl+Enter or click "Load Sequence"
- The Sequence Builder canvas fills with 6 residue tiles
OPTION B — Drag and Drop:
- Find A (Alanine) in the Amino Acid Palette — orange tile
- Drag it into the Sequence Builder canvas
- Repeat for G, G, A, G, A in order
Thinking in HP Model
BioQuantum's quantum algorithms use the HP (Hydrophobic-Polar) binary model. Before sending to quantum fold, mentally map your sequence:
- A (Alanine) → H (Hydrophobic) — nonpolar, orange in palette
- G (Glycine) → P (Polar/neutral) — special, flexible
AGGAGA maps to: H P P H P H
This gives 3 hydrophobic and 3 polar residues — a good balance for an interesting fold landscape.
STEP 2: Validate in Peptide Designer
Do NOT send to quantum fold yet. First validate:
SUB-STEP 2A — Check 3D Structure:
In the 3D Structure Viewer (bottom of Peptide Designer):
- Switch representation to "Cartoon"
- Switch coloring to "Hydrophobicity"
What you should see for AGGAGA:
- A short extended chain (6 residues is too short for a full helix — you need 8+ for α-helix)
- Mixed red/blue coloring — confirms HP balance ✓
⚠️ Warning: If your sequence shows ALL red (fully hydrophobic) or ALL blue (fully polar), the quantum algorithm will find a trivial solution with no interesting energy landscape. Go back and add alternating H/P residues.
SUB-STEP 2B — Check Properties per Residue:
Click each residue tile. Key things to check:
| What to check | Good sign | Warning sign |
|---|---|---|
| Charge at pH 7 | Mix of 0, +1, -1 | All same charge |
| Hydrophobicity | Range of values | All identical |
| No consecutive P (Proline) | — | PP or PPP breaks structure |
SUB-STEP 2C — Confirm Sequence Length:
Check which algorithm you plan to run first:
| Algorithm | Max residues | AGGAGA (6 residues) |
|---|---|---|
| VQE | 20 | ✅ Supported |
| GroveFold | 10 | ✅ Supported |
| QAOA | 10 | ✅ Supported |
| Quantum Walks | 20 | ✅ Supported |
AGGAGA works with all 4 algorithms — ideal for learning the platform.
STEP 3: Run Quantum Algorithms
Click "Send to Quantum Fold" — the purple button in the center of Peptide Designer.
You will land on Quantum Models with AGGAGA pre-filled. Run TWO algorithms in this order:
RUN 1: VQE (QF-VQE-01)
Purpose: Find the single lowest-energy conformation.
Settings:
- Engine: qiskit-aer-local (default)
- Hardware: Quantum Simulator (default)
- Interaction model: Miyazawa-Jernigan (default)
Click "Run Model →"
Expected output for AGGAGA:
- Ground state energy: approximately -2.3 to -2.8 MJ units
- Optimal conformation: H residues (A) pulled inward, G residues forming flexible outer loop
- pTM-equivalent confidence: displayed as "State Fidelity" score
How to read VQE output:
| Metric | Range | Interpretation |
|---|---|---|
| Ground state energy | More negative = better | -2.5 is good for 6-mer |
| State fidelity | 0 to 1 | > 0.85 = high confidence |
| Circuit depth | Integer | Lower = less noise |
| Iterations to converge | Integer | < 150 = clean landscape |
RUN 2: QAOA (QF-QAOA-03)
Purpose: See ALL possible conformations with probabilities.
Settings:
- Same sequence: AGGAGA (already pre-filled)
- p layers: 2 (default — increase for more accuracy)
- Mixer: X mixer (default)
Click "Run Model →"
Expected output for AGGAGA:
Probability distribution across conformations. You should see a bar chart with 4–8 bars.
How to read QAOA output:
| What you see | What it means |
|---|---|
| Tallest bar (highest probability) | Most likely fold — should match VQE result |
| Second tallest bar | Competing metastable state |
| Many bars roughly equal height | Floppy sequence — consider redesign |
| One bar at >75% | Very stable, well-defined fold |
✅ Success: For AGGAGA, if your tallest QAOA bar matches the VQE ground state conformation, you have confirmed the fold with two independent quantum methods. This is high-confidence structural prediction.
STEP 4: Interpret and Iterate
Three scenarios and what to do next:
- SCENARIO A — VQE and QAOA agree (>70% on same conformation)
✅ High confidence result
→ Download the conformation
→ Use sequence as-is for further drug design work
→ Try Quantum Walks next to get secondary structure classification (α-helix vs β-sheet probability) - SCENARIO B — QAOA shows 2–3 competing conformations
⚠️ Sequence is in a frustrated energy landscape
→ This is actually scientifically interesting — sequence may be intrinsically disordered
→ Go back to Peptide Designer
→ Try adding 1–2 more hydrophobic residues to stabilize (e.g., change a G to L or V)
→ Re-run both algorithms and compare - SCENARIO C — QAOA shows uniform distribution (all bars equal)
❌ No dominant fold found
→ Sequence too short OR too polar
→ Go back to Peptide Designer
→ Extend to 8–10 residues
→ Ensure at least 40% hydrophobic residues (H in HP model)
→ Re-run
The Iteration Loop
new algorithm
Quick Reference — Algorithm Cheat Sheet
VQE
- When to use: "I want THE best fold"
- Output: Single ground state structure
- Sequence limit: 6–20 residues
- Time: 30–120s
QAOA
- When to use: "I want ALL possible folds"
- Output: Probability distribution
- Sequence limit: 5–10 residues
- Time: 20–90s
GroveFold
- When to use: "I want the best SEQUENCE for a target"
- Output: Optimal amino acid sequence
- Sequence limit: 4–10 residues
- Time: 15–60s
Quantum Walks
- When to use: "I want secondary structure prediction"
- Output: α-helix/β-sheet probability
- Sequence limit: 3–20 residues
- Time: 10–40s