College
College of Engineering
Mentor Information
Lawrence Stern
Description
Interleukin-23 (IL-23) regulates inflammation, yet its overproduction, driven by genetics or overactive immune triggers, can lead to chronic inflammatory diseases like psoriasis. IL-23 consists of two subunits; an exclusive subunit, p19, and a second subunit, p40, that is shared with Interleukin-12. Driving selectivity of p19-p40 interactions promotes a shift in cytokine formation between IL-23 and IL-12. By integrating computational biology, protein engineering, and structural data from the RCSB Protein Data Bank (entry 4RGW), the research addressed structural challenges like unresolved regions in experimental structures and the difficulty of predicting beneficial mutations. Structural modeling using ColabFold, a prediction tool based on AlphaFold2, successfully resolved missing residues. Engineering tools FoldX and PyRosetta refined the protein’s physical shape to ensure accurate stability analysis, establishing a complete digital template for testing individual mutations. Systematic mutational scanning was conducted across all 20 amino acids at identified interface residues using FoldX and PyRosetta independently. The study’s accuracy was confirmed by measuring consistency between software models, ensuring both programs ranked mutations similarly with statistical significance. The analysis identified ten mutationally amenable locations at the p19-p40 interface: TA23, IA52, LA63, FA153, AA155, AA158, VA160, HA163, EB181, and DB290. Both predictive models demonstrated high agreement. To verify these results, yeast surface display will be used to test thousands of mutations simultaneously, physically confirming how the refined mutated group performs. Ultimately, this comparative approach proves that computer models can effectively identify residues on proteins for mutation, reducing time and cost required for protein engineering.
Accelerating Protein Engineering: Using Computer Modeling to Identify Beneficial Interleukin-23 (IL-23) Mutations
Interleukin-23 (IL-23) regulates inflammation, yet its overproduction, driven by genetics or overactive immune triggers, can lead to chronic inflammatory diseases like psoriasis. IL-23 consists of two subunits; an exclusive subunit, p19, and a second subunit, p40, that is shared with Interleukin-12. Driving selectivity of p19-p40 interactions promotes a shift in cytokine formation between IL-23 and IL-12. By integrating computational biology, protein engineering, and structural data from the RCSB Protein Data Bank (entry 4RGW), the research addressed structural challenges like unresolved regions in experimental structures and the difficulty of predicting beneficial mutations. Structural modeling using ColabFold, a prediction tool based on AlphaFold2, successfully resolved missing residues. Engineering tools FoldX and PyRosetta refined the protein’s physical shape to ensure accurate stability analysis, establishing a complete digital template for testing individual mutations. Systematic mutational scanning was conducted across all 20 amino acids at identified interface residues using FoldX and PyRosetta independently. The study’s accuracy was confirmed by measuring consistency between software models, ensuring both programs ranked mutations similarly with statistical significance. The analysis identified ten mutationally amenable locations at the p19-p40 interface: TA23, IA52, LA63, FA153, AA155, AA158, VA160, HA163, EB181, and DB290. Both predictive models demonstrated high agreement. To verify these results, yeast surface display will be used to test thousands of mutations simultaneously, physically confirming how the refined mutated group performs. Ultimately, this comparative approach proves that computer models can effectively identify residues on proteins for mutation, reducing time and cost required for protein engineering.
