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

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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.