Characterization of protein surface hydrophobicity using molecular dynamics simulations and deep learning

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Sinha, Imee
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Electronic thesis
Chemical engineering
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In the past decade, there has been a significant growth in the market size for protein based therapeutics such as monoclonal antibodies, enzymes, bispecifics and Fc-fusion proteins. Protein based therapeutics provide many advantages over small molecule drugs, and are used to treat a wide range of diseases such as cancers, autoimmune disorders and infectious diseases. The design, manufacturing and purification of protein therapeutics require careful consideration of its physicochemical properties, such as immunogenicity, toxicity, and aggregation propensity. Many of these properties are closely connected with the hydrophobicity of these molecules. Hydrophobic domains on proteins and antibodies act as hotspots that seed aggregation and interactions. Hydrophobicity also governs complex phenomena such as protein folding and its stability. The understanding and evaluation of all of these processes requires a diligent method for the quantification of protein surface hydrophobicity. The hydrophobic nature of protein surface patches has been linked to their residue hydropathy values, however, research has shown that hydrophobicity of complex heterogeneous surfaces, is a function of their chemistry and topographical features and the response of water to these. Specifically, hydrophobicity of a heterogeneous surface is closely related to the density fluctuations of its interfacial waters and their response to perturbations. In this work, we have used a fast and computationally efficient enhanced sampling method called sparse sampling INDUS to identify hydrophobic domains for a set of proteins. True to our hypothesis, density fluctuations for hydrophobic patches identified by sparse sampling INDUS, bear large fat tails that represent their proximity to dewetting. These fat tails are indicative of patches that are placed near a dewetting transition due to the unique combination of the chemistry of constituent groups and their topography. Further, we compare and contrast our results to those obtained from traditional methods of hydrophobicity calculation and present an analysis for the similarities and differences observed. Our investigations shed light on the complex nature of hydrophobicity, its underpinnings to density fluctuations and the role of chemical and topographical context in modulating it.
December 2022
School of Engineering
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Rensselaer Polytechnic Institute, Troy, NY
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