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 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.
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 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.
• 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.
Protein folding processes are a vital aspect of molecular biology that is hard to simulate with conventional computers. Quantum algorithms have been proven superior for certain problems and may help tackle this complex life science challenge. We analyze the resource requirements for simulating simplified yet computationally challenging protein folding models on a quantum computer, assessing the feasibility of these existing approaches in the current and near-future technological landscape. We calculate the minimum number of qubits, interactions, and two-qubit gates necessary to build a heuristic quantum algorithm with the specific information of a folding problem. Particularly, we focus on the resources needed to build quantum operations based on the Hamiltonian linked to the protein folding models for a given amino acid count. Such operations are a fundamental component of these quantum algorithms, guiding the evolution of the quantum state for efficient computations. Specifically, we study coarse-grained folding models on the lattice and the fixed backbone side-chain conformation model and assess their compatibility with the constraints of existing quantum hardware given different bit encodings. We conclude that the number of qubits required falls within current technological capabilities. However, the limiting factor is the high number of interactions in the Hamiltonian, resulting in a quantum gate count unavailable today.
Protein folding is one of the age-old biological problems that refers to the mechanism of understanding and predicting how a protein's linear sequence of amino acids folds into its specific three dimensional structure.This structure is critical, as a protein's functionality is inherently linked to its final folded form. Misfolding can lead to severe diseases such as Alzheimer's and cystic fibrosis, highlighting the biological and clinical importance of understanding protein folding mechanisms. This work presents a novel turn based encoding optimization algorithm for predicting the folded structures of peptides and small proteins. Our approach builds upon our previous research, where our objective function focused on hydrophobic collapse, a fundamental phenomenon underlying the protein folding process. In this work, we extend that framework by not only incorporating hydrophobic interactions but also including all non bonded interactions modeled using the Miyazawa Jernigan potential. We constructed a Hamiltonian from the defined objective function that encodes the folding process on a three dimensional face centered cubic lattice, offering superior packing efficiency and a realistic representation of protein conformations. This Hamiltonian is then solved using classical and quantum solvers to explore the vast conformational space of proteins. To identify the lowest-energy folded configurations, we utilize the Variational Quantum Eigensolver implemented on IBM 133 qubit hardware. The predicted structures are validated against experimental data using root mean square deviation as a metric and compared against classical simulated annealing and molecular dynamics simulation results. Our findings highlight the promise of hybrid classical and quantum approaches in advancing protein folding predictions, particularly for sequences with low homology.
Protein folding poses a significant challenge in biology due to its combinatorial nature and NP-completeness, making it a prime candidate for quantum computing solutions. Quantum computers offer the potential to expedite protein folding predictions by leveraging superposition and entanglement properties for efficient exploration of protein structure space. This study focuses on enhancing peptide folding predictions through an innovative Ansatz circuit design incorporating T-gates alongside Clifford gates to reduce noise in quantum algorithms. The study benchmarks existing tools like MD simulation and stochastic techniques against the Variational Quantum Eigensolver (VQE) using quantum simulators and QPUs. Detailed qubit configurations and peptide folding processes are outlined, highlighting the use of efficient quantum structures and circuit designs. The experimental setup includes running the circuit for multiple replicas to analyze error rates and energy minimization for different peptide sequences. The results demonstrate a significant difference in gate performance between Clifford gates alone and Clifford + T gates, with T gates leading to reduced noise and improved performance. Statistical analyses reveal a negative mean difference and a high effect size, emphasizing the impact of T gates on noise reduction in quantum algorithms for peptide folding predictions. The study showcases consistent results across multiple replicas, indicating the reliability and efficiency of the proposed Ansatz circuit design. The study highlights the importance of gate sequencing and optimization in quantum computing applications for protein structure prediction. Future directions include expanding the model to larger proteins, refining ansatz circuits, and collaborating with experts to advance quantum algorithms for complex biological systems.