Researchers from Cleveland Clinic and IBM have published findings that could pave the way for using quantum computing methods in protein structure prediction. The study, part of the Cleveland Clinic-IBM Discovery Accelerator partnership, was led by Cleveland Clinic postdoctoral fellow Bryan Raubenolt, Ph.D., and IBM researcher Hakan Doga, Ph.D. The team used a mix of quantum and classical computing methods to predict the folding of a small fragment of a Zika virus protein on a quantum computer. The results outperformed both a classical physics-based method and AlphaFold2, a machine learning technique. The team plans to continue developing quantum algorithms for predicting larger and more complex protein structures.
Quantum Computing and Protein Structure Prediction
Researchers from Cleveland Clinic and IBM have recently published a paper in the Journal of Chemical Theory and Computation, which explores the potential of quantum computing methods in protein structure prediction. This publication is the first peer-reviewed quantum computing paper from the Cleveland Clinic-IBM Discovery Accelerator partnership. The study could pave the way for a new approach to understanding protein structures, which are crucial to many aspects of human health and disease.
Protein structures are complex and can determine how diseases spread and how effective therapies can be developed. For decades, scientists have used computational approaches to predict these structures. However, these methods have limitations, particularly when dealing with proteins that are mutated or very different from those in the training data. This is where quantum computing could offer a significant advantage.
Quantum-Classical Hybrid Framework in Protein Folding Simulation
The team, led by Cleveland Clinic postdoctoral fellow Bryan Raubenolt, Ph.D., and IBM researcher Hakan Doga, Ph.D., applied a mix of quantum and classical computing methods to overcome the limitations of current computational approaches. The researchers used a quantum algorithm to model the lowest energy conformation for the protein’s backbone, which is typically the most computationally demanding step of the calculation. Classical approaches were then used to convert the results obtained from the quantum computer, reconstruct the protein with its sidechains, and perform final refinement of the structure with classical molecular mechanics force fields.
The quantum-classical hybrid framework’s initial results outperformed both a classical physics-based method and AlphaFold2, a machine learning program designed for protein structure prediction. This demonstrates the framework’s ability to create accurate models without relying heavily on substantial training data.
Multidisciplinary Collaboration and Future Directions
The success of this project was largely due to the multidisciplinary collaboration involved. The team’s expertise ranged from computational biology and chemistry, structural biology, software and automation engineering, experimental atomic and nuclear physics, mathematics, quantum computing, and algorithm design. This diverse knowledge base was crucial in creating a computational framework that can mimic one of the most important processes for human life.
Combining classical and quantum computing methods is a significant step forward in advancing our understanding of protein structures and their impact on our ability to treat and prevent disease. The team plans to continue developing and optimizing quantum algorithms that can predict the structure of larger and more sophisticated proteins.
The Role of Quantum Computing in Protein Structure Prediction
Quantum computing could potentially revolutionize the field of protein structure prediction. The ability to simulate the physics of protein folding, particularly for larger proteins, is a challenge that classical computers struggle with. Quantum computing, however, could address these challenges, including protein size, intrinsic disorder, mutations, and the physics involved in proteins folding.
The researchers’ goal is to design quantum algorithms that can predict protein structures as realistically as possible. This work is an important step forward in exploring where quantum computing capabilities could show strengths in protein structure prediction.
Cleveland Clinic is a nonprofit multispecialty academic medical center that integrates clinical and hospital care with research and education. Founded in 1921, it has pioneered many medical breakthroughs, including coronary artery bypass surgery and the first face transplant in the United States. Cleveland Clinic is consistently recognized for its expertise and care, with more than 5,743 salaried physicians and researchers, and 20,160 registered nurses and advanced practice providers, representing 140 medical specialties and subspecialties.
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