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.