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Folding Unpredictable Disordered Proteins with Quantum Computers... Hybrid Folding Package 'QuPepFold' Released

inquantio

Published Jul 13, 2026• 0 citations
Publisher

Abstract

• Published in PLOS ONE, developed QuPepFold, a Python package specialized for simulating intrinsically disordered protein regions (IDR) and peptide ensembles. • Introduced a Conditional Value at Risk (CVaR)-optimized Variational Quantum Eigensolver (VQE), reducing convergence time by approximately 30% compared to conventional methods, and demonstrated over 90% accuracy on real quantum hardware (IonQ Aria-1). • Drastically lowered the barrier to entry for structural biologists to easily utilize quantum computing in drug discovery by automating complex quantum circuit construction and error mitigation processes. [Quantum Biology Society] A significant portion of proteins in the human body includes intrinsically disordered proteins (IDPs) or intrinsically disordered regions (IDRs) that transition through various conformations without a fixed three-dimensional structure. Although they are cited as key causes of numerous diseases such as cancer and neurodegenerative disorders, they lack distinct binding pockets and have long been considered "undruggable" in traditional structure-based drug design. Recently, a popular software package capable of simulating the ensembles of these disordered proteins with quantum computers has been developed, drawing significant attention. A paper titled "QuPepFold: A python package for hybrid quantum-classical protein folding simulations with CVaR-optimized VQE," published in the international journal PLOS ONE, presented a practical tool capable of immediately integrating quantum computing technology into the field of drug discovery. A joint research team including Akshay Uttarkar and Professor Vidya Niranjan from RV College of Engineering, and Amit Saxena from the Centre for Development of Advanced Computing (C-DAC) in India developed this groundbreaking package. ■ Solving the Disordered Regions that Even AlphaFold Hit a Wall With, Using Quantum Algorithms State-of-the-art artificial intelligence (AI) structure prediction models like AlphaFold have revolutionized the prediction of well-defined protein structures, but they have faced great difficulty in predicting the dynamic ensembles of IDPs or IDRs that lack a fixed structure. To overcome this, the research team developed a modular Python package called QuPepFold. This system converts the three-dimensional conformation into a tetrahedral lattice model, maps it to qubit registers, and then explores the optimal energy state where the peptide folds through quantum algorithms. This is advantageous for efficiently exploring the vast conformational space of disordered proteins, which have numerous degrees of freedom and shallow energy barriers. ■ 30% Faster Convergence and Overcoming Noise with the Introduction of CVaR-VQE The researchers adopted a Conditional Value at Risk (CVaR)-optimized Variational Quantum Eigensolver (VQE) algorithm to calculate the ground state energy of the protein. While conventional VQE focuses on minimizing the expected (average) energy of all measurements, CVaR-VQE focuses only on the measurement outcomes in the lower tail (the lowest energy states). Test results showed that in short peptide structure exploration, the CVaR-optimized VQE reached the ground state approximately 30% faster than the conventional VQE approach. Furthermore, it operated efficiently even in the current quantum hardware environment where errors and noise exist, successfully reproducing ground state energies with high fidelity on a real IonQ Aria-1 quantum computer accessed via Amazon Braket. ■ Declaration of Quantum Computing Democratization for Drug Discovery Researchers The greatest significance of QuPepFold lies in its strong user-friendliness. Previously, if biologists wanted to utilize quantum computing in their research, complex quantum programming knowledge—such as quantum circuit design, Hamiltonian construction, and error mitigation—was essential. However, this package abstracted these technical barriers by hiding them behind automated modules. Users can perform simulations seamlessly across Qiskit simulators, tensor networks, and actual quantum hardware (such as IonQ) simply by inputting a peptide sequence. This study is expected to be a groundbreaking turning point where quantum computing technology moves beyond the exclusive domain of complex programming experts and establishes itself as a practical and popular tool that will change the landscape of drug discovery targeting disordered proteins, which have long been difficult to drug.

DOI
10.5281/zenodo.21329155

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inquantio

- A study published in the international journal npj Quantum Information (Nature Portfolio) presents a resource-efficient quantum algorithm framework based on the Variational Quantum Eigensolver (VQE) for peptide and protein folding. - By translating protein folding into a tetrahedral lattice model and combining Conditional Value-at-Risk (CVaR) VQE with a classical evolutionary algorithm, the number of required qubits was drastically reduced. - It successfully folded a 7-amino acid neuropeptide using only 9 qubits on actual IBM 20-qubit quantum hardware, demonstrating its practical potential for next-generation drug discovery in the NISQ (Noisy Intermediate-Scale Quantum) era. [Quantum Biology Society] The protein folding problem, which involves predicting the three-dimensional structure of proteins and peptides from their primary amino acid sequence, plays a central role in various applications such as chemistry, biology, and medicine. However, as the sequence lengthens, the number of possible structures to explore explodes exponentially, making it difficult for classical computers to handle its intrinsic NP-hard complexity. Recently, a new quantum algorithm that can efficiently solve this long-standing challenge even on noisy intermediate-scale quantum (NISQ) computers has been developed, drawing the attention of the academic community. The paper titled "Resource-efficient quantum algorithm for protein folding," published in the international journal npj Quantum Information, presents an innovative computing framework for the discovery of next-generation peptide drugs. The research team of Anton Robert and Ivano Tavernelli from IBM Quantum, IBM Research - Zurich in Switzerland successfully led this groundbreaking study. ■ The Meeting of Lattice Models and Qubit Optimization Until now, attempts to introduce quantum computing to protein folding have required an unattainably high number of qubits that current, near-term quantum hardware cannot handle. To solve this, the research team converted protein folding into a tetrahedral lattice model. This model can efficiently depict the growth of a polymer chain while maintaining chemically plausible bond angles. In particular, they adopted a method of adding penalty terms to the Hamiltonian to prevent unphysical amino acid overlaps (e.g., beads occupying the same lattice site). Through this, they succeeded in drastically reducing the number of qubits required for calculation while suppressing the complexity of the algorithm to the level of O(N⁴). ■ Demonstration of Folding on Actual Quantum Hardware This algorithm maximized efficiency by combining CVaR-VQE, a variation of the Variational Quantum Eigensolver (VQE), with a classical Differential Evolution optimizer. Applying this technique, the researchers simulated the folding of the 10-amino acid Angiotensin peptide using only 22 qubits, successfully finding the most stable energy state (native fold). Furthermore, they successfully demonstrated the folding of a 7-amino acid neuropeptide (APRLRFY) using only 9 qubits on IBM's 20-qubit quantum computer hardware. This study is a groundbreaking achievement proving that the protein folding problem, which previously required massive computing resources, can be successfully solved even in a noisy real quantum processor environment. It is expected to bring tremendous innovation to next-generation /de novo/ drug design and disease treatment research utilizing quantum computing in the future.

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•Jul 13, 2026•0 citations
• Published in PLOS ONE, developed QuPepFold, a Python package specialized for simulating intrinsically disordered protein regions (IDR) and peptide ensembles. • Introduced a Conditional Value at Risk (CVaR)-optimized Variational Quantum Eigensolver (VQE), reducing convergence time by approximately 30% compared to conventional methods, and demonstrated over 90% accuracy on real quantum hardware (IonQ Aria-1). • Drastically lowered the barrier to entry for structural biologists to easily utilize quantum computing in drug discovery by automating complex quantum circuit construction and error mitigation processes. [Quantum Biology Society] A significant portion of proteins in the human body includes intrinsically disordered proteins (IDPs) or intrinsically disordered regions (IDRs) that transition through various conformations without a fixed three-dimensional structure. Although they are cited as key causes of numerous diseases such as cancer and neurodegenerative disorders, they lack distinct binding pockets and have long been considered "undruggable" in traditional structure-based drug design. Recently, a popular software package capable of simulating the ensembles of these disordered proteins with quantum computers has been developed, drawing significant attention. A paper titled "QuPepFold: A python package for hybrid quantum-classical protein folding simulations with CVaR-optimized VQE," published in the international journal PLOS ONE, presented a practical tool capable of immediately integrating quantum computing technology into the field of drug discovery. A joint research team including Akshay Uttarkar and Professor Vidya Niranjan from RV College of Engineering, and Amit Saxena from the Centre for Development of Advanced Computing (C-DAC) in India developed this groundbreaking package. ■ Solving the Disordered Regions that Even AlphaFold Hit a Wall With, Using Quantum Algorithms State-of-the-art artificial intelligence (AI) structure prediction models like AlphaFold have revolutionized the prediction of well-defined protein structures, but they have faced great difficulty in predicting the dynamic ensembles of IDPs or IDRs that lack a fixed structure. To overcome this, the research team developed a modular Python package called QuPepFold. This system converts the three-dimensional conformation into a tetrahedral lattice model, maps it to qubit registers, and then explores the optimal energy state where the peptide folds through quantum algorithms. This is advantageous for efficiently exploring the vast conformational space of disordered proteins, which have numerous degrees of freedom and shallow energy barriers. ■ 30% Faster Convergence and Overcoming Noise with the Introduction of CVaR-VQE The researchers adopted a Conditional Value at Risk (CVaR)-optimized Variational Quantum Eigensolver (VQE) algorithm to calculate the ground state energy of the protein. While conventional VQE focuses on minimizing the expected (average) energy of all measurements, CVaR-VQE focuses only on the measurement outcomes in the lower tail (the lowest energy states). Test results showed that in short peptide structure exploration, the CVaR-optimized VQE reached the ground state approximately 30% faster than the conventional VQE approach. Furthermore, it operated efficiently even in the current quantum hardware environment where errors and noise exist, successfully reproducing ground state energies with high fidelity on a real IonQ Aria-1 quantum computer accessed via Amazon Braket. ■ Declaration of Quantum Computing Democratization for Drug Discovery Researchers The greatest significance of QuPepFold lies in its strong user-friendliness. Previously, if biologists wanted to utilize quantum computing in their research, complex quantum programming knowledge—such as quantum circuit design, Hamiltonian construction, and error mitigation—was essential. However, this package abstracted these technical barriers by hiding them behind automated modules. Users can perform simulations seamlessly across Qiskit simulators, tensor networks, and actual quantum hardware (such as IonQ) simply by inputting a peptide sequence. This study is expected to be a groundbreaking turning point where quantum computing technology moves beyond the exclusive domain of complex programming experts and establishes itself as a practical and popular tool that will change the landscape of drug discovery targeting disordered proteins, which have long been difficult to drug.
- A study published in the international journal npj Quantum Information (Nature Portfolio) presents a resource-efficient quantum algorithm framework based on the Variational Quantum Eigensolver (VQE) for peptide and protein folding. - By translating protein folding into a tetrahedral lattice model and combining Conditional Value-at-Risk (CVaR) VQE with a classical evolutionary algorithm, the number of required qubits was drastically reduced. - It successfully folded a 7-amino acid neuropeptide using only 9 qubits on actual IBM 20-qubit quantum hardware, demonstrating its practical potential for next-generation drug discovery in the NISQ (Noisy Intermediate-Scale Quantum) era. [Quantum Biology Society] The protein folding problem, which involves predicting the three-dimensional structure of proteins and peptides from their primary amino acid sequence, plays a central role in various applications such as chemistry, biology, and medicine. However, as the sequence lengthens, the number of possible structures to explore explodes exponentially, making it difficult for classical computers to handle its intrinsic NP-hard complexity. Recently, a new quantum algorithm that can efficiently solve this long-standing challenge even on noisy intermediate-scale quantum (NISQ) computers has been developed, drawing the attention of the academic community. The paper titled "Resource-efficient quantum algorithm for protein folding," published in the international journal npj Quantum Information, presents an innovative computing framework for the discovery of next-generation peptide drugs. The research team of Anton Robert and Ivano Tavernelli from IBM Quantum, IBM Research - Zurich in Switzerland successfully led this groundbreaking study. ■ The Meeting of Lattice Models and Qubit Optimization Until now, attempts to introduce quantum computing to protein folding have required an unattainably high number of qubits that current, near-term quantum hardware cannot handle. To solve this, the research team converted protein folding into a tetrahedral lattice model. This model can efficiently depict the growth of a polymer chain while maintaining chemically plausible bond angles. In particular, they adopted a method of adding penalty terms to the Hamiltonian to prevent unphysical amino acid overlaps (e.g., beads occupying the same lattice site). Through this, they succeeded in drastically reducing the number of qubits required for calculation while suppressing the complexity of the algorithm to the level of O(N⁴). ■ Demonstration of Folding on Actual Quantum Hardware This algorithm maximized efficiency by combining CVaR-VQE, a variation of the Variational Quantum Eigensolver (VQE), with a classical Differential Evolution optimizer. Applying this technique, the researchers simulated the folding of the 10-amino acid Angiotensin peptide using only 22 qubits, successfully finding the most stable energy state (native fold). Furthermore, they successfully demonstrated the folding of a 7-amino acid neuropeptide (APRLRFY) using only 9 qubits on IBM's 20-qubit quantum computer hardware. This study is a groundbreaking achievement proving that the protein folding problem, which previously required massive computing resources, can be successfully solved even in a noisy real quantum processor environment. It is expected to bring tremendous innovation to next-generation /de novo/ drug design and disease treatment research utilizing quantum computing in the future.
Abstract Quantum computing in biology is one of the most rapidly evolving field of technology. Protein folding is one of the key challenges which requires accurate and efficient algorithms with a quick computational time. Structural conformations of proteins with disordered regions need colossal amount of computational resource to map its least energy conformation state. In this regard, quantum algorithms like Variational quantum eigensolver (VQE) are applied in the current research work to predict the lowest energy value of 50 peptides of 7 amino acids each. VQE is initially used to calculate the energy values over which Variational Quantum Optimization is applied via Conditional Value at Risk (CVaR) over 100 iterations of 500000 shots each to obtain least ground state energy value. This is compared to the molecular dynamics-based simulations of 50 nanoseconds each to calculate the energy values along with the folding pattern. The results suggest efficient folding outcomes from CvaR-VQE compared to MD based simulations. With the ever-expanding quantum hardware and improving algorithms the problem of protein folding can be resolved to obtain in depth insights on the biological process and drug design.
Protein folding is a fundamental process crucial for the functionality of biological molecules. Despite its significance, predicting protein structures accurately remains a challenging task due to the complex nature of folding pathways and interactions. In this study, we explore the application of quantum computing, specifically error mitigated VQE, in investigating the folding of disordered regions in Ubiquitin C. By integrating advanced simulation techniques and quantum algorithms, we aim to unravel the intricate dynamics of protein folding at a molecular level. We employ a combination of molecular dynamics simulations and quantum VQE algorithms to analyze the folding kinetics and stability of C-terminal region of Ubiquitin C. Utilizing state-of-the-art quantum simulators and computational tools, we track the evolution of protein conformations and assess ground state energy values to elucidate the folding process. Our results demonstrate the effectiveness of error mitigated VQE in providing accurate ground state energy values compared to traditional methods like MD simulations with difference less than -0.91 kcal/mol. The analysis reveals insights into the structural transitions and stability of Ubiquitin C during the folding process, shedding light on key interactions and conformational changes. This study underscores the potential of quantum computing in advancing our understanding of protein folding dynamics.
Protein folding has been one of the most difficult problems for over a half-century,The random thermal motions causing conformational changes that lead energetically downhill towards the native structure, a principle captured in funnel-shaped energy landscapes.Unfolded polypeptides have a wide range of possible conformations. The search problem becomes intractable for classical computers due to the exponential growth of potential conformations with chain length. So far, there is theoretical and experimental evidence that solving such optimization problems using Quantum Computing approaches such as Quantum Annealing, VQE, and QAOA has an advantage. Although Google's DeepMind-AlphaFold has accomplished much,But we can go even further with the quantum approach.Here we show how to predict structure of protein as well as RNA folding using the Variational Quantum Eigensolver with Conditional Value at Risk (CVaR) expectation values for the solution of the problem and for finding the minimum configuration energy and our task is to identify a protein's minimal energy structure. The protein's structure is optimized to reduce energy. Also making sure that all physical constraints are met and encoding the protein folding problem into a qubit operator.
Protein structure prediction remains central to computational biology and drug discovery. While deep learning models such as AlphaFold have achieved remarkable accuracy for known fold families, they struggle with novel folds, intrinsically disordered regions, and conformational dynamics, and they do not explicitly optimize physics-based energy functions. We present a quantum-assisted approach to protein conformation optimization that uses parameterized quantum circuits within a variational framework to explore the energy landscape of small proteins and fragments from first-principles or coarse-grained physics. Our method encodes backbone torsion angles (ϕ, ψ) and selected side-chain degrees of freedom into qubit registers and employs a variational quantum eigensolver (VQE)-inspired optimization to minimize an approximate Hamiltonian incorporating bonded interactions, sterics, hydrogen bonding, and hydrophobic contacts. We introduce problem-inspired ansätze that respect chain connectivity and secondary-structure motifs (helices, beta-sheets) and propose hierarchical circuit growth strategies for scaling from peptides to larger domains. Through classical simulations of small peptides and secondary-structure motifs, we demonstrate that quantum variational optimization can identify low-energy conformations consistent with experimentally determined structures. We provide explicit resource estimates for NISQ and early fault-tolerant quantum computers and discuss hybrid classical-quantum strategies for refining quantum solutions with classical molecular dynamics and machinelearning priors. While full protein-folding quantum advantage remains distant, we identify concrete subproblems where nearterm quantum devices offer complementary physics-based insight beyond data-driven approaches, opening pathways toward practical quantum-classical protein structure and drug discovery pipelines.
• Published on bioRxiv, successfully applied the variational quantum eigensolver (VQE) algorithm to calculate the electronic structure of a tau peptide fragment. • Overcame the exponential limits of existing classical computers, demonstrating the possibility of precise biomolecular energy calculations even on NISQ-era hardware. • Presents a scalable pathway for quantum chemical analysis of complex large protein systems through a fragment-based workflow. [Quantum Biology Society] Quantum computing technology, which overcomes the limitations of classical computers, has been deployed to elucidate the aggregation mechanism of the tau protein, one of the core causes of Alzheimer's disease. By precisely calculating the electronic structure of complex biological systems like peptides at the subatomic level, revolutionary changes are expected in the design of new drugs for treating neurodegenerative diseases in the future. Recently, an empirical study titled "Quantum Computing Reveals Energetics of Tau Peptide Fragments" was published on the preprint repository bioRxiv. A research team led by Dr. Anuj Guruacharya and Dr. Binita Rajbanshi at Nanil Therapeutics Inc. in the USA announced the results of successfully applying a quantum algorithm to a peptide system through this paper. ■ 'Variational Quantum Eigensolver (VQE)' Breaking Through Classical Limits Accurately predicting electron interactions within biomolecules is the core of new drug development, but there has been a fatal problem where the amount of computation required by classical computers increases exponentially as the molecule size grows. To solve this, the research team introduced the variational quantum eigensolver (VQE) algorithm, which can be run even on current noisy intermediate-scale quantum (NISQ) devices. The research team targeted a peptide fragment, which is a core region of the tau protein aggregates observed in the brains of Alzheimer's disease patients, and successfully derived its electronic structure and ground state energy under a quantum environment. ■ Elucidating the Differences Between Electronic Structure Methods and Classical Force Fields One of the greatest achievements of this study is that it rigorously compared and verified the energy calculations from the quantum algorithm against existing classical methods. As a result of the analysis, the quantum energy values from VQE showed systematic agreement with classical restricted Hartree-Fock (RHF) calculations, but exhibited a weak correlation with classical molecular dynamics (MD) force-field energies widely used in conventional drug design. This suggests that classical force-field models have limitations in fully capturing the electronic properties of biomolecules, proving that quantum mechanical treatment is essential for more precise drug design. ■ Securing Fragment-Based Scalability for Macromolecular Protein Analysis Instead of calculating the entire macromolecular protein at once, the researchers integrated a fragment-based workflow into VQE, dividing the molecule into smaller pieces for computation. This approach presents a practical pathway to scale to large systems using current quantum computing resources efficiently, while preserving the essential quantum characteristics of complex biological peptide systems. This empirical study is evaluated as a significant milestone demonstrating how quantum biology theory can be integrated into actual drug development platforms. Experts predict that as this technology advances, ultra-precise peptide drug design and the prediction of complex protein-protein interactions—which were impossible in the past due to computational cost issues—will become possible in near real-time.