Committee Members: 

Dr. Raquel Lieberman, Georgia Institute of Technology, School of Chemistry & Biochemistry

Dr. Matthew Realff , Georgia Institute of Technology, School of Chemical and Biomolecular Engineering

Synthetic Transcription Factor Allostery Mapping and Analysis

This work aims to advance the synthetic design of allosterically regulated systems by extracting sequence-function correlations from engineered LacI-based anti-repressors. Through deep mutational scanning, we have generated comprehensive datasets correlating single-mutant genotypes with functional phenotypes. Building upon the experimental dataset and alongside model development efforts that utilize it, I introduce a novel approach to complement these analyses. By projecting deep mutational scanning data onto a representative protein structure model, I enable visual inspection and procedural analysis of position-based relationships. This projection, combined with quantitative data analysis across multiple datasets, generates a comprehensive, site-specific value list that can be algorithmically manipulated. This integrated approach, blending structural visualization with quantitative analysis, provides a deeper understanding towards position linked factors integral in LacI allostery. Ultimately, this research seeks to establish a foundation for improved engineering strategies for synthetic transcription factors and to enhance the development of features relevant for phenotype prediction efforts by further elucidating the mechanistic underpinnings of anti-repressor function and contributing to the broader understanding of protein allostery.