Development of Quantum Computing Algorithms to Explore Cyclic Peptides

开发量子计算算法来探索环肽

• 批准号: 2607523
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2607523
• 关键词: Development; Quantum; Computing; Algorithms; Explore

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.

简短摘要：该项目涉及量子算法的开发，该算法实施粗粒度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科学中最大的问题之一。虽然诸如 AlphaFold（Jumper 等人，2021）中实现的经典计算算法在蛋白质结构预测方面取得了长足的进步，但由于任务的复杂性（即使是小蛋白质），经典计算机很可能永远无法真正“解决”蛋白质折叠问题，即使对于小蛋白质也是如此。理论上，该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（例如叠加和纠缠）来解决在经典计算机上难以解决的问题。虽然 QC 硬件要达到计算机具有容错性且足够大以可靠地执行此类大规模计算的水平还需要很多年，但算法可以与 QC 硬件的开发一起开发。最近，Robert 等人 (Robert et al., 2021) 提出了一种将简化的格上模型与变分量子算法相结合的策略 适应经典成本函数和进化策略，研究血管紧张素肽（10 个氨基酸）在 22 个量子位上的折叠和使用 9 个量子位的 7 个氨基酸神经肽。制药行业对环肽特别感兴趣。据估计，与疾病有关的 80% 的蛋白质无法使用传统的小分子药物进行治疗（Scudelari，2019）。环肽可能提供一种替代策略，并且事实上该领域已经取得了成功。目前，从学术和工业的角度来看，环肽特性的开发和理解都引起了人们的极大兴趣（Yudin，2019）。该项目包括 Robert 等人提出的工作的扩展，通过设计和修改算法来引入溶剂化等新因素，从而提高模型的复杂性，对氨基酸残基进行更详细的处理，并专门调整模型以研究环肽的构象 环肽。