{"id":{"repo_id":"gatech","oai_identifier":"oai:repository.gatech.edu:1853/78694"},"canonical_url":"https://search.dev.ndltd.org/etd/gatech/oai:repository.gatech.edu:1853/78694","repository":{"repo_id":"gatech","name":"Georgia Tech","base_url":"https://repository.gatech.edu/server/oai/request"},"display":{"title":"Synthetic Transcription Factor Allostery Mapping and Analysis","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Berry, Andre D."],"institution":"Georgia Institute of Technology","degree_name":null,"degree_level":"Masters","degree_discipline":null,"degree_department":"Chemical and Biomolecular Engineering","school":null,"contributors":[],"advisors":["Wilson, Corey J."],"committee_chairs":[],"committee_members":["Lieberman, Raquel","Realff, Matthew"],"year":2025,"date_issued":"2025-07-22","date_published":"2025-07-22","updated_at":"2026-07-27T19:49:34Z","subjects":["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"],"languages":["en_US"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1853/78694","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Wilson, Corey J."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Lieberman, Raquel","Realff, Matthew"]},{"key":"dc:contributor.department","label":"Department","values":["Chemical and Biomolecular Engineering"]},{"key":"dc:creator","label":"Author","values":["Berry, Andre D."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-08-15T12:42:55Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-08-15T12:42:55Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-07-22"]},{"key":"dc:publisher","label":"Institution","values":["Georgia Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Text"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["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"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1853/78694"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.S."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Synthetic Transcription Factor Allostery Mapping and Analysis"]}]}],"canonical_facts":{"dc:contributor.advisor":["Wilson, Corey J."],"dc:contributor.committeemember":["Lieberman, Raquel","Realff, Matthew"],"dc:contributor.department":["Chemical and Biomolecular Engineering"],"dc:creator":["Berry, Andre D."],"dc:date.accessioned":["2025-08-15T12:42:55Z"],"dc:date.available":["2025-08-15T12:42:55Z"],"dc:date.issued":["2025-07-22"],"dc:description.abstract":["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."],"dc:description.degree":["M.S."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/1853/78694"],"dc:language.iso":["en_US"],"dc:publisher":["Georgia Institute of Technology"],"dc:subject":["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"],"dc:title":["Synthetic Transcription Factor Allostery Mapping and Analysis"],"dc:type":["Text"],"thesis:degree_level":["Masters"]},"updated_at":"2026-07-27T19:49:34Z"}