Molecular Dynamics Simulations of Graphene-Protein interactions for Biomedical Diagnostic Sensors

对蛋白质动力学、能量景观和构象变化的基本理解是深入了解蛋白质特定生化功能的核心。这可能有助于药物发现，新的蛋白质工程和区分疾病诊断应用的正常和致病性构象变化。在这个项目中，我们的目标是研究一种新的方法，通过直接比较分子动力学模拟（MDS）与普利茅斯大学与剑桥大学合作开发的传感器的实验结果，检测石墨烯表面上的蛋白质动力学和相互作用。对于MDS，我们将采用密度泛函理论方法的Kohn-Sham（KS）形式来进行石墨烯-蛋白质系统的硅内研究。KS方法是研究表现出量子力学行为的材料性质和过程的最可靠的第一原理方法之一。这项研究的开创性将使学生能够使用BigDFT大规模并行电子结构代码来模拟石墨烯-蛋白质传感器，该传感器基于普利茅斯大学的高性能计算集群，并与尖端的实验测量进行比较。MDS和实验结果之间的准确比较有可能导致我们对蛋白质动力学和构象状态的理解取得突破，从而在诊断，药理学和治疗学中开辟大量应用，特别是阿尔茨海默氏症，癌症和心血管疾病。

Fundamental understanding of protein dynamics, energy landscape and conformational changes, is central to deeper insights into a protein's specific biochemical functions. This could aid in drug discovery, novel protein engineering and distinguishing between normal and pathogenic conformational changes for disease diagnostics applications. In this project we aim to investigate a novel approach for the detection of protein dynamics and interactions on the surface of Graphene through direct comparison of Molecular Dynamics Simulations (MDS) with experimental results obtained from the sensors developed at the University of Plymouth in collaboration with the University of Cambridge. For the MDS we will employ the Kohn-Sham (KS) formalism of the density functional theory approach to perform the in-silicio study of the graphene-protein system. The KS approach is one of the most reliable first-principles methods for investigating material properties and processes that exhibit quantum mechanical behaviour. The pioneering nature of this research will enable the student to use BigDFT massively parallel electronic structure code to simulate the graphene-protein sensor, based on the High Performance Computing Cluster at the University of Plymouth, as well as making comparisons with cutting-edge experimental measurements. Accurate comparisons between MDS and experimental results has the potential to lead to a breakthrough in our understanding of protein dynamics and conformational states, thus opening a plethora of applications in diagnostics, prognostics and therapeutics particularly for Alzheimer's, cancer and cardiovascular diseases.