Development of Quantum Computing Algorithms to Explore Cyclic Peptides

简短总结：该项目涉及量子算法的开发，实现粗粒度的方法来确定小环肽的最小能量构象（即蛋白质折叠）。概要：从其氨基酸序列预测蛋白质的构象是当今医学科学中最大的问题之一。虽然诸如在AlphaFold中实现的那些经典计算算法（Jumper等人，尽管量子计算机（2021年）在蛋白质结构预测方面取得了巨大的进步，但由于任务的天文复杂性，即使是小蛋白质，经典计算机也可能永远无法真正“解决”蛋白质折叠问题。从理论上讲，这个问题非常容易通过量子计算（QC）来解决。量子计算机利用量子系统的特定特性，如叠加和纠缠，来解决经典计算机难以解决的问题。虽然在QC硬件达到计算机具有容错性并且足够大以可靠地执行这种大规模计算的程度之前还需要许多年，但是可以在QC硬件的开发的同时开发算法。2021）提出了一种策略，该策略将简化的格上模型与专门适用于经典成本函数的变分量子算法和进化策略相结合，以研究血管紧张素肽（10个氨基酸）在22个量子比特上的折叠和使用9个量子比特的7个氨基酸的神经肽。据估计，80%与疾病相关的蛋白质无法使用传统小分子药物进行药物治疗（Scudellari，2019）。环肽可以提供一种替代策略，实际上在这一领域已经取得了成功。从学术和工业的角度来看，环肽的开发和性质的理解目前都引起了极大的兴趣（Yudin，2019）。该项目包括Robert等人提出的工作的延伸，通过设计和修改算法来引入新的因素，如溶剂化，以实现对氨基酸残基的更详细的处理，并使模型专门用于研究环肽的构象，从而提高模型的复杂性。

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.