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QuPepFold: A python package for hybrid quantum-classical protein folding simulations with CVaR-optimized VQE.

Akshay Uttarkar1, Vidya Niranjan1,2, Amit Saxena3

  • 1Department of Biotechnology, R V College of Engineering, (Affiliated to Visvesvaraya Technological University, Belagavi), Bangalore, Karnataka, India.

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Summary
This summary is machine-generated.

QuPepFold democratizes quantum-classical simulations for peptide folding, accelerating the exploration of intrinsically disordered regions (IDRs) for drug discovery. This new package enhances computational efficiency and accuracy in structural biology.

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Area of Science:

  • Computational Biology
  • Quantum Computing
  • Drug Discovery

Background:

  • Protein folding, especially for intrinsically disordered regions (IDRs), presents significant computational challenges.
  • Classical methods struggle with the conformational sampling required for IDR analysis.
  • Targeting IDRs is crucial for developing therapeutics against diseases linked to protein misfolding.

Purpose of the Study:

  • Introduce QuPepFold, a modular Python package for hybrid quantum-classical peptide folding simulations.
  • Enable broader access to quantum computing for structural biology research.
  • Facilitate the exploration of IDR ensembles for therapeutic targeting.

Main Methods:

  • Utilize a variational quantum eigensolver (VQE) optimized with a conditional value-at-risk (CVaR) objective for ground-state energy computation.
  • Employ a CVaR approach to prioritize low-energy measurements, improving convergence and noise resilience.
  • Ensure hardware independence with interfaces for various quantum simulators and devices via Amazon Braket.

Main Results:

  • CVaR-optimized VQE achieved ground-state energies approximately 30% faster than standard VQE for short peptides.
  • Achieved over 90% fidelity in reproducing ground-state energies on the IonQ Aria-1 quantum computer.
  • Demonstrated consistent and transferable energy calculations across different simulators and physical hardware.

Conclusions:

  • QuPepFold provides an accessible framework for integrating quantum computing into peptide folding studies.
  • The package simplifies quantum circuit construction and error mitigation, lowering the barrier for structural biologists.
  • Facilitates drug discovery efforts targeting disordered proteins by enabling efficient sampling of IDR ensembles.