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Computational Studies of Pantetheine-Containing Ligands (PCLs) and Advancements of the Polarizable Gaussian Multipole (pGM) Model

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Pantetheine-containing ligands (PCLs) play key roles in the biosynthesis of polyketides, a large family of natural products with various bioactivities. A major hurdle that remains is our poor understanding of the protein-substrate interactions of ketoreductase (KR), a key component of polyketide synthases (PKSs). Since the poly-β-ketone intermediates are highly reactive and cannot be isolated, the first project of this dissertation employed molecular dynamics (MD) simulations to interpret the transient KR-substrate interactions. Several key factors guiding KR-substrate interactions were identified, which will help further engineering of PKSs and directing biosynthesis of novel polyketides.

The reliability of MD simulations depends on the quality of the employed force field, which comprises a mathematical formula and a set of parameters to represent the potential energy of molecular systems. The parameter sets for simulating amino acids, nucleic acids, sugars, and lipids are already available. However, lack of scalable parameter sets for PCLs has hampered the computational studies of various biomolecules containing PCLs. Therefore, in the second project of this dissertation, the first Pantetheine Force Field (PFF) library containing parameter sets for various PCLs compatible with additive force fields was developed and validated.

The extensively used additive force fields use fixed atom-centered partial charges to model atomic electrostatic interactions. However, additive force fields cannot accurately model atomic polarization effects, leading to unrealistic simulations in polarization-sensitive processes. The polarizable Gaussian Multipole (pGM) model with all atomic multipoles represented by Gaussian densities has been recently developed. In the third project of this dissertation, an electrostatic parameterization scheme for the pGM model was developed, and the accuracy and transferability of the pGM model were assessed. Encouragingly, the pGM model was shown to be accurate and transferable, which has the potential to serve as the template for developing the next-generation polarizable force field for modeling various biological systems.

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