DescriptionThis thesis describes a computational protein engineering approach, which utilizes protein assemblies and enzyme engineering, for the biodegradation of an endocrine disruptor and common pollutant, atrazine, and describes all the experimental approaches that were used to further characterize the designed enzymes. A computational generalizable approach for designing fusion proteins that can self-assembly into fractal-like morphologies on the 10 nm – 10 µM length scale was developed. This approach will allow for any set of oligomeric proteins (with cyclic, dihedral, and other symmetries) to form multivalent connections along with designed flexible loops enabling the control of size of a fractal shaped assembly. Our current approach utilizes the SH2 binding domain-pY peptide to allow for a stimulus control of assembly formation through the post-translational modification of phosphorylation. This same generalizable approach can be applied to other metabolic pathways with other domain-peptide recognition proteins with various different responsiveness to other chemicals or physical stimuli. The phase to phase transition that the assembly produces under self-assembly has the potential to provide various applications, such as creating protein-based nanobiomaterials or creating nanocages (in our case protein fractals) to sequester antibodies and easily precipitate out the antibody as needed.
In addition to engineering a stimulus responsive protein fractal assembly, the bottle neck enzyme in the biodegradation of atrazine, atzC, was computationally engineered to improve the catalytic efficiency of other known pollutants, N-t-butylammelide and ammelide. This general approach for computationally designing the active site of an enzyme by probing with energetically acceptable substitutions in the various shells of the protein (first and second shell), not including the active site, but instead focusing on mutations nearby the active site resulted in successfully designing variants of atzC with a broadened s-triazine substrate spectrum. To summarize, this dissertation presents a novel and innovative approach for engineering fractal self-assembly of enzymes and explores the design approach for engineering an enzyme with limited abilities for novel substrates.