Development of Quantum Computing Algorithms to Explore Cyclic Peptides

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Short Summary: This project involves the development of quantum algorithms that implement a coarse-grained approach to determine the minimum energy conformation of small cyclic peptides (i.e. protein folding).Summary:Predicting the conformation of a protein from its amino acid sequence is one of the greatest problems in the medical sciences today. While classical computing algorithms such as those implemented in AlphaFold (Jumper et al., 2021) have made great strides in protein structure prediction, it is likely that classical computers will never truly be able to "solve" the protein folding problem due to the astronomical complexity of the task, even for small proteins.In theory, the problem is highly apt to be approached by quantum computing (QC). Quantum computers leverage specific properties of quantum systems, such as superposition and entanglement, to solve problems that would be intractable to solve on a classical computer. While it will be many years before QC hardware reaches a point where the computers are fault-tolerant and large enough to reliably perform such large-scale calculations, the algorithms can be developed in the meantime, alongside the development of QC hardware.Recently, Robert et al (Robert et al., 2021) presented a strategy combining a simplified on-lattice model in conjunction with variational quantum algorithms specifically adapted to classical cost functions and evolutionary strategies to study the folding of the Angiotensin peptide (10 amino acids) on 22 qubits and a 7-amino acid neuropeptide using 9 qubits.Cyclic peptides are of particular interest to the pharmaceutical sector. It has been estimated that 80% of proteins involved in disease cannot be drugged using conventional small-molecule drugs (Scudellari, 2019). Cyclic peptides may offer an alternative strategy and indeed there has already been success in this area. The development and understanding the properties of cyclic peptides is currently of great interest from both an academic and industrial point of view (Yudin, 2019).This project comprises an extension of the work presented in Robert et al., developing the complexity of the model by designing and modifying algorithms to introduce new factors such as solvation, to implement a more detailed treatment of amino acid residues, and to adapt the model specifically to study the conformation of cyclic peptides.