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，该算法实现了一种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）。摘要：从氨基酸序列预测蛋白质的构象是当今医学中最大的问题之一。虽然在AlphaFold中实现的经典计算算法（Jumper et al., 2021）在蛋白质结构预测方面取得了巨大进步，但由于任务的天文数字复杂性，经典计算机很可能永远无法真正“解决”蛋白质折叠问题，即使是小蛋白质。理论上，这个问题很容易通过量子计算（QC）来解决。量子计算机利用量子系统的特定属性，如叠加和纠缠，来解决在经典计算机上难以解决的问题。虽然QC硬件要达到计算机容错和足够大以可靠地执行如此大规模计算的程度还需要许多年的时间，但算法可以在此期间与QC硬件的发展一起开发。最近，Robert等人（Robert et al., 2021）提出了一种策略，结合简化的点阵模型，结合专门适应经典成本函数的变分量子算法和进化策略，研究血管紧张素肽（10个氨基酸）在22个量子比特上的折叠，以及7个氨基酸神经肽在9个量子比特上的折叠。环肽是制药行业特别感兴趣的。据估计，80%与疾病有关的蛋白质无法使用传统的小分子药物进行药物治疗（Scudellari, 2019）。环肽可能提供另一种策略，确实已经在这一领域取得了成功。从学术和工业的角度来看，目前对环肽特性的开发和理解引起了极大的兴趣（Yudin, 2019）。该项目包括Robert等人所做工作的延伸，通过设计和修改算法来引入新的因素（如溶剂化）来发展模型的复杂性，实现对氨基酸残基的更详细处理，并使模型专门用于研究环肽的构象。

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.