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Activatable “Antibodies”

Activatable “Antibodies”. Patrick Conway Wendy Thomas Lab. Activatable recognition proteins?. Allosteric regulation of binding Targeted binding Good for triggering binding Purification Assembly/Disassembly Imaging Requirements Separated regulation and binding Easy to alter specificity.

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Activatable “Antibodies”

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  1. Activatable “Antibodies” Patrick Conway Wendy Thomas Lab

  2. Activatable recognition proteins? • Allosteric regulation of binding • Targeted binding • Good for triggering binding • Purification • Assembly/Disassembly • Imaging • Requirements • Separated regulation and binding • Easy to alter specificity

  3. It all starts with FimH • E. coli fimbria mannose binding domain • Affinity regulated through bond tension • Similar to a Chinese finger trap toy

  4. FimH Structure Mannose Twisted beta sheet Regulatory region

  5. movie

  6. Mannose binding site • Consists of 3 loops • Comparable to Complementarity Determining Regions (CDRs) of antibodies Mannose Loop 2 Loop 3 Loop 1

  7. Engineering specificity • Build diversity library at the binding site • General enough to cover most ligands • Screen for ligand binders • Flow cytometry • Adhesion assay • Perform directed evolution to improve affinity

  8. Diversity Library • Randomizing binding site sequences • Construct multiple random sequences • Insert with primer and PCR • Sequence space vs actual library • 20^26 possible sequences • 10^8, 10^9 library size

  9. Optimizing Diversity Library Coverage • Reduce stop codons • Limit deleterious mutations • Identify via Rosetta simulation • Modify codon diversity • wild-type, some combinations, or all combinations NUCLEOTIDE POSITION SEQUENCE G 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 ACG CTG ACTG ACTG CT CT CG ATG ATG CG CG CG ACTG ACTG TG ACTG ACTG ACTG ACTG ACTG Possible Nucleotide Combinations ACTG ACTG ACTG ACTG A A

  10. My role • Optimize library • Predict deleterious mutations with Rosetta • Quantify percent of well-formed FimH mutants with flow cytometry

  11. Screening deleterious mutations with Rosetta • Compute ddG of every possible substitution • For all binding site positions (520 mutations total) • Both high and low affinity conformations • Minimize whole structure with constraints • Select best of 50 minimizations per mutation • 3 days to try all mutations on one residue • Use high performance computing cluster to parallelize • Compare wild-type energy to mutation energy for ddG value

  12. Rosetta Results • Some positions can mutate with all possibilities • Others positions should remain wild-type

  13. Rosetta Results • Conformational preferences for 14G, 15G • Hinge region • Affects mobility between conformations Favors high affinity Favors low affinity

  14. Rosetta Results • Proline sensitivity in many residues • Difficult to exclude proline • CCN codon (N = ACTG)

  15. Physical diversity library analysis • Outcome of codon selection • Amount of well-formed FimH mutants vs library size • Low affinity vs high affinity bias • We expect low affinity conformation by default!

  16. FACS assay (controls) MaB-21 PaB-49 PaB-280 (High-affinity FimH) (FimA) (FimH) KB91 (low-affinity FimH expression) FocH (high-affinity FimH expression) KB2 (deleted FimH)

  17. Many thanks! Wendy Thomas David Baker Todor Avramov Victoria Rodriguez Olga Yakovenko An-yue Tu Colin Sullender Gianluca Interlandi Thomas Lab

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