(334bd) Leveraging Molecular Simulations and Data Science to Improve the Design of Biomaterials with Fine-Tuned Hydrophobicity
Hydrophobicity is an important interfacial property that determines the association of nonpolar materials in water and is highly correlated with biologically relevant properties, including immune response, protein-protein interactions, and cell uptake. Accurately predicting hydrophobicity is vital to the design of new materials with favorable properties for applications in drug delivery, biosensing, and antifouling. My research interests lie in developing and applying molecular simulations techniques and data science methods to understand how surface properties of biomaterials influence interfacial phenomena and intermolecular interactions. Then applying these newfound understandings to developing predictive models based on physical insights to rapidly screen drug and gene agents to identify candidates with desirable physicochemical properties. These models would be valuable to improving the efficiency and efficacy of new drug therapeutic design and discovery.
My research focuses on modeling the interface between water and self-assembled monolayer (SAM) protected gold nanoparticles using molecular dynamics (MD) simulations to understand how physical and chemical properties influence the hydrophobicity of the SAM surfaces. My research project can be divided into three fundamental questions: (1) How do physical surface properties influence hydrophobicity? (2) How do chemical surface properties influence hydrophobicity? and (3) How do physical and chemical surface properties cooperatively influence hydrophobicity? The goal of answering these questions is to better understand how surface properties alter hydrophobicity; thus, providing a design framework to engineer novel biomolecules with fine-tuned hydrophobicity. To achieve this goal, I have developed a simulation methodology that applies umbrella sampling (an enhanced sampling MD technique) to measure the hydrophobic force exerted between two SAM surfaces. I collaborated closely with experimentalists to ensure the model accurately predicts hydrophobic forces. To screen a larger range of SAM chemistries and compositions, I applied a more efficient enhanced sampling technique (Indirect Umbrella Sampling) to predict the hydrophobicity. This method required approximately ten times less simulation time than classical umbrella sampling. Next, I applied traditional analysis techniques, such as time-averaged measurements, as well as more data-centric methods, including principal component analysis and multivariate linear regression, to unbiased MD simulation data to interrogate how interfacial water structure, physical and chemical surface properties, and hydrophobicity are related. Finally, in collaboration with data-science experts, we leveraged the predictive power of machine learning algorithms to create a model that predicts hydrophobicity 400 times faster than the most efficient enhanced sampling MD simulation methods.
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