The development of quantum computing-based biomolecular recognition methods for use by researchers at all stages of the translational pathway

开发基于量子计算的生物分子识别方法，供研究人员在翻译途径的各个阶段使用

• 批准号: 2728403
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2728403
• 关键词: development; quantum; computing; based; biomolecular

The work plan will involve iteration between experimental (UCL) and computational (Evotec, UCL) elementsof the project throughout each of the years of study. Four different FMO applications will be pursued duringthe proposed project:Year 1: FMO-ESPC (electrostatic potential complementarity) - The role of electrostatic interactions isvital for biomolecular recognition. Integration between FMO and ESPC (FMO-ESPC) will enable theaccurate calculation of the electrostatic potential complementarity (ESPC) between a ligand and the proteinto identify key interaction points critical for SBDD.Year 2: FMO-PPI (Protein-protein interactions) - Protein-protein interactions (PPIs) are essential forprotein functions. More than 645,000 disease-relevant PPIs have been reported in the human interactomehowever only 2% of these drug targets have been addressed with PPI modulators. We will integrate FMOand PPI (FMO-PPI) to identify residues that are critical for protein-protein binding (hotspots) thus facilitatingstructure-based drug design of PPI modulators (SBDD-PPI).Year 3: FMO-Residence Time - We have developed an accurate means of calculating drug residence timeusing steered molecular dynamics. We will integrate Fragment Molecular Orbital (SMD/FMO) computationalmodelling for G protein-coupled receptors in a way that can be automated and scaled for use on high-endcomputing systems.Year 4: Development of an online FMO Workflow for use by biomedical researchers

工作计划将涉及在研究期间的每一年中，项目的实验(UCL)和计算(Evotec，UCL)元素之间的迭代。在拟议的项目期间，将进行四种不同的FMO应用：第1年：FMO-ESPC(静电势互补)--静电相互作用的作用对生物分子识别至关重要。FMO和ESPC之间的整合(FMO-ESPC)将能够准确计算配体和蛋白质之间的静电势互补(ESPC)，从而识别对SBDD至关重要的关键相互作用点。据报道，在人类相互作用中，已有超过645,000种与疾病相关的PPI，然而，这些药物靶点中只有2%使用PPI调节剂。我们将整合FMO和PPI(FMO-PPI)来识别对蛋白质-蛋白质结合(热点)至关重要的残基，从而促进基于结构的PPI调节剂(SBDD-PPI)的药物设计。第3年：FMO-滞留时间-我们开发了一种使用引导分子动力学计算药物滞留时间的准确方法。我们将对G蛋白偶联受体的片断分子轨道(SMD/FMO)计算模型进行集成，以一种可自动化和可扩展的方式用于高端计算系统。第4年：开发供生物医学研究人员使用的在线FMO工作流程