Modelling Protein Folding and Interactions

Predicting how proteins fold into their 3D shapes and how they interact with other molecules (like drugs or ligands) is challenging and computationally demanding. For example, even for very simple models, finding low-energy 3D structures is an intractable problem.

Quantum algorithms like QAOA, VQE, Grover’s algorithm, and quantum Monte Carlo methods are being used to model protein folding and interactions more efficiently. For example, VQE combined with advanced techniques can estimate binding energies between proteins and drug molecules. Quantum approaches like Gaussian Boson Sampling have also been tested for predicting how ligands dock onto proteins. Lastly, quantum annealing could be used to find the lowest-energy shapes (conformations) of simplified protein models on a grid.

Protein folding models are important for computational biophysics as they provide useful insight into the energy landscape of natural proteins. Ultimately, this could help design new proteins, improve drug discovery, and better predict how proteins behave in complex biological environments.