QuPepFold

QupepFold is a small, research-oriented toolkit that turns short amino-acid sequences into quantum bitstring encodings, optimizes them with a CVaR-VQE routine, and exports 3D PDB files (with CONECT records) for high-probability folds. It’s built to be easy to run, easy to inspect, and easy to tweak.

qFoldIT / Foldnite architecture role: QuPepFold is a scientific-computation/reference capability that can be routed into governed Scientific Missions where quantum peptide/protein folding is an appropriate capability.

Principal Global Headquarters: Elkridge, Maryland, USA.

The toolkit remains scientifically independent: it is a computational method and reference implementation, not an authoritative claim that a generated candidate is biologically active. Any downstream mission acceptance must cross the configured validation and evidence boundary.

Features

✨ Easy installation.
✨ Simple CLI usage.
✨ Get detailed outputs: Qubit mapping, best CVaR energy, probable bitstrings, and more!
✨ Visualize your results with optimal_circuit.png, cvar_scatter.png, and bitstring_histogram.png.
✨ Export 3D PDB files and summaries.

Installation and easy way to use in CLI

pip3 install qupepfold
pip3 install pylatexenc

qupepfold --seq APRLFHG --tries 30 --shots 1000 --alpha 0.025 --write-csv --out /path/to/output/directory

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What to expect in output in terminal

1. Qubit mapping (config/interaction/ancilla counts)
2. Best CVaR energy
3. Most probable bitstring (with probability)
4. Lowest-energy bitstring (with energy)

Results in the output folder

1. output_summary.txt — quick result summary
2. optimal_circuit.png — ansatz diagram (no measurements)
3. cvar_scatter.png — CVaR value per multi-start iteration
4. bitstring_histogram.png — bar chart for states ≥ threshol
5. bitstring_summary.csv — [bitstring, cfg_bits, probability, energy, exported_PDB3D]
6. bitstring_summary_cvar.csv — same distribution (kept for continuity)
7. most_negative_energy_breakdown.png + .csv — component energies (backbone/MJ/distance/locality) for the lowest energy state
8. pdb3d/*.pdb — one PDB per exported bitstring, with CONECT bonds
9. pdb3d_bitstrings_ge_2pct.zip — bundle of those PDBs

Uninstall

pip3 uninstall qupepfold

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Our previously published Quantum research works:

1. Akshay Uttarkar, Vidya Niranjan (2024). Quantum synergy in peptide folding: A comparative study of CVaR-variational quantum eigensolver and molecular dynamics simulation. International Journal of Biological Macromolecules. Volume 273, Part 1, 2024, 133033, ISSN 0141-8130, https://doi.org/10.1016/j.ijbiomac.2024.133033
2. Uttarkar, A., Niranjan, V. A comparative insight into peptide folding with quantum CVaR-VQE algorithm, MD simulations and structural alphabet analysis. Quantum Inf Process 23, 48 (2024). https://doi.org/10.1007/s11128-024-04261-9
3. A. Uttarkar and V. Niranjan, "Quantum Enabled Protein Folding of Disordered Regions in Ubiquitin C Via Error Mitigated VQE Benchmarked on Tensor Network Simulator and Aria 1," in IEEE Transactions on Molecular, Biological, and Multi-Scale Communications, doi: 10.1109/TMBMC.2025.3600516 https://ieeexplore.ieee.org/document/11130538
4. A. Uttarkar, A. S. Setlur and V. Niranjan, "T-Gate Enabled Fault-Tolerant Ansatz Circuit Design for Variational Quantum Algorithms in Peptide Folding on Aria-1," 2024 International Conference on Artificial Intelligence and Emerging Technology (Global AI Summit), Greater Noida, India, 2024, pp. 1271-1276, doi: 10.1109/GlobalAISummit62156.2024.10947993 https://ieeexplore.ieee.org/document/10947993
5. Rutwik S, A. Uttarkar, A. S. Setlur, A. B. H and V. Niranjan, "Exploring VQE for Ground State Energy Calculations of Small Molecules With Higher Bond Orders," 2024 International Conference on Artificial Intelligence and Emerging Technology (Global AI Summit), Greater Noida, India, 2024, pp. 1182-1187, doi: 10.1109/GlobalAISummit62156.2024.10947806. https://ieeexplore.ieee.org/document/10947806

🚀 Future Version Update: Scalability for larger peptides

qFoldIT / Foldnite mission integration

QuPepFold can serve as a computational capability behind a governed mission contract for quantum-assisted peptide/protein exploration. In that role:

Scientific Objective
      ↓
Mission Contract
      ↓
Quantum / peptide capability
      ↓
QuPepFold computation
      ↓
Candidate structure / energy output
      ↓
Independent validation
      ↓
Evidence / provenance
      ↓
Enterprise or research deliverable

The qFoldIT control plane may use this capability as one candidate-generating method; it does not elevate a QuPepFold output into scientific truth without the applicable validation pipeline.