Scientists Revolutionize Protein Folding Prediction with Quantum Computing
Scientists have made significant strides in predicting protein folding, a longstanding challenge in biology. A team led by Anders Irbäck, Lucas Knuthson, and Sandipan Mohanty has developed a novel method that could revolutionize our understanding of protein structures and their role in diseases.
The team transformed the complex protein folding problem into a mathematical form suitable for both classical and quantum computation. They achieved this by simplifying protein models and optimization procedures, allowing them to predict and design protein structures for very long chains, up to 48 units.
Their approach involves reformulating the problem as a quadratic unconstrained binary optimization (QUBO) problem, which can be solved rapidly and consistently for such long protein chains. This breakthrough enables the exploration of protein folding energy landscapes and the design of new proteins on a potentially scalable level.
The method was tested using both classical simulated annealing and hybrid quantum-classical annealing on a D-Wave system. The latter took approximately 10 seconds to determine the lowest energy state of protein chains up to 48 units in length. This significant speedup highlights the potential of quantum computing for solving complex scientific problems.
The research, extending to modelling protein phase separation and aggregation relevant to diseases like amyloidosis, Alzheimer's, and Parkinson's, offers a promising avenue for accurate protein structure prediction. This could greatly benefit drug discovery and the design of new biomaterials, marking a substantial step forward in the field of computational biology.
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