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.
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.
- 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.
Protein folding prediction is a critical challenge in bioinformatics, demanding significant computational resources. This research explores the potential of quantum computing to accelerate and enhance this process. We formulate the protein folding problem as a quantum optimization problem, employing quantum simulation algorithms for efficient solution. The methodology involves mapping the protein's amino acid sequence into a quantum Hamiltonian, followed by simulating the system using quantum algorithms. We investigate the effectiveness of Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA) in determining the ground state energy and, consequently, the folded conformation of the protein. The predicted structures are then validated through comparison with known experimental data and integration with classical molecular dynamics simulations. The core claim of this work is to leverage the computational power of quantum computers to drastically reduce the time required for protein folding prediction, leading to improved accuracy and efficiency. The results demonstrate a promising approach for tackling this computationally intensive problem, potentially revolutionizing drug discovery and protein engineering.
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.
• 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.
Quantum computing has gained a lot of attention recently, and scientists have seen potential applications in this field using quantum computing for Cryptography and Communication to Machine Learning and Healthcare. Protein folding has been one of the most interesting areas to study, and it is also one of the biggest problems of biochemistry. Each protein folds distinctively, and the difficulty of finding its stable shape rapidly increases with an increase in the number of amino acids in the chain. A moderate protein has about 100 amino acids, and the number of combinations one needs to verify to find the stable structure is enormous. At some point, the number of these combinations will be so vast that classical computers cannot even attempt to solve them. In this paper, we examine how this problem can be solved with the help of quantum computing using two different algorithms, Variational Quantum Eigensolver (VQE) and Quantum Approximate optimization Algorithm (QAOA), using Qiskit Nature. We compare the results of different quantum hardware and simulators and check how error mitigation affects the performance. Further, we make comparisons with SoTA algorithms and evaluate the reliability of the method.
Protein folding has been one of the most difficult problems for over a half-century,Therandom thermal motions causing conformational changes that lead energeticallydownhill towards the native structure, a principle captured in funnel-shaped energylandscapes.Unfolded polypeptides have a wide range of possible conformations. Thesearch problem becomes intractable for classical computers due to the exponentialgrowth of potential conformations with chain length. So far, there is theoretical andexperimental evidence that solving such optimization problems using QuantumComputing approaches such as Quantum Annealing, VQE, and QAOA has anadvantage. Although Google's DeepMind-AlphaFold has accomplished much,But wecan go even further with the quantum approach.Here we show how to predict structureof protein as well as RNA folding using the Variational Quantum Eigensolver withConditional Value at Risk (CVaR) expectation values for the solution of the problem andfor finding the minimum configuration energy and our task is to identify a protein'sminimal energy structure. The protein's structure is optimized to reduce energy. Alsomaking sure that all physical constraints are met and encoding the protein foldingproblem into a qubit operator.