476080 Fast Modeling Protein Corona on Nanoparticle‐Based Biosensors in Complex Solvent Environments/ Cell Membrane By a Coarse‐Grained Simulation System
Understand protein behavior in a complex environment is the key in rationalizing the design of many novel techniques such as protein biosensors based on novel nanoparticle (NP) materials. Protein folding and aggregation properties can be largely affected by the environments such as solid abiotic interfaces and complex co-solvents, which would directly affect the expected functionality or performance of the biosensors. Difficulties remain in applying current experimental methods to obtain a detailed picture of protein structures on a solid surface, so our ability is limited by these factors in the design of biosensor surface chemistry a priori for desired protein behavior. To address this point, in this work we present a coarse-grained simulation system that is able to model protein folding, misfolding, and aggregation on various NP surfaces and in the presence of co-solvents based on the Karanicolas and Brooks (KB) Go-like protein model. A flat-surface potential was first included and carefully parameterized based on a large experimental data set. This surface model has been shown in many studies to be able to reproduce and predict protein behavior at different types of surfaces as verified by experimental tests. Further effort in developing the surface potential was applied in describing surface curvature effects on protein behavior as a means of understanding nano-sized biosensor materials. Tanford's transfer free energy model was recently added to this simulation system as a descriptor of co-solvent effects or cell membrane environments. The energy of each part of the underlying folding/surface/solvation potential has been well balanced so that each element works collaboratively as a single complex environment for the protein. Together, this coarse-grained modeling system is expected to accurately predict the protein adsorption, folding/unfolding, aggregation, and protein-protein interactions in a complex environment as it would experience in differing cellular or solvent environments as well as in the context of a NP-based biosensor.
I would like to practice my teaching under the guidance of the student-centered learning philosophy, which emphasizes personal reading, active peer discussing, and collaboration. The eventual goal of my teaching is to help them find their way to acquire knowledge and tools independently but not simply from the class. As a potential new faculty member, I am very interested in keeping optimizing my teaching techniques through my career as an educator.
Specifically, I would like to teach engineering classes that are currently given by the department on undergraduate or graduate levels, such as: Engineering Mathematics, Thermodynamics, Kinetics, and Transport Phenomena. Also, I want to bring classes such as molecular simulation and statistical mechanics for biological engineers, which are directly related to my research experiences and interests. A variety of simulation methods including quantum chemistry calculation, multi-levels molecular dynamics/ Monte Carlo simulation, and quantitative structure-activity relationship (QSAR) modeling will be taught in the classes.
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