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 ongoing machine learning 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 associated machine learning models by further elucidating the mechanistic underpinnings of anti-repressor function and contributing to the broader understanding of protein allostery.

Level thesis:degree_level

Masters

Department dc:contributor.department

Chemical and Biomolecular Engineering

Grantor dc:publisher

Georgia Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Berry, Andre D.

Advisor dc:contributor.advisor

• Wilson, Corey J.

Committee members dc:contributor.committeemember

• Lieberman, Raquel
• Realff, Matthew

• Allostery
• Protein Engineering
• Synthetic Biology
• Lactose Repressor (LacI)
• Anti-repressor
• Deep Mutational Scanning (DMS)
• Data Visualization
• Residue Interaction Networks (RINs)
• Genotype-Phenotype Mapping
• Structure-Function Relationship
• Blender
• Molecular Nodes
• AlphaFold
• Gene Regulation

Language dc:language.iso

en_US

Handle dc:identifier.uri

https://hdl.handle.net/1853/78694

OAI identifier oai:identifier

oai:repository.gatech.edu:1853/78694