Predictions of the Docking of an Engineered Antibody to Anthrax Toxin

1. Predictions of the Docking of an Engineered Antibody to Anthrax Toxin Arvind Sivasubramanian & Jeffrey J. Gray ACS 2006 San Francisco PROGRAM IN Molecular Biophysics

2. Antibody therapeutics • At least 20 therapeutic antibodies have been approved by regulatory authorities, with another 150 molecules in the clinic Key Growth driver! mAbs entering Clinical study Reichert, Nat Biotech, 2005 Das et al, Intl. Sci. Comm, 2006

3. Blind Prediction of Docking (CAPRI) • Laminin + Nidogen, model #2: • 53% contacts, rmsd 4.6 Å, interface rmsd 0.66 Å • One of two best predictionsout of 20+ groups ininternational challenge Red—Native laminin Green—Predicted laminin Blue—Nidogen D800, N802, V804 constrained near interface Methods in Gray et al. 2003 JMB Xtal by T. Springer, Harvard

4. Typical requirements and assumptions of docking predictions • Monomer structures known • Strong binding (Kd < µm) • Small proteins (1-2 domains per partner) • Little significant backbone movement • Some experimental data are helpful

5. Mechanism of toxin action Therapy: Prevent PA binding to cells using mAb’s

6. Structure of pre-pore • Santelli et al, Nature 2004.

7. mAb 14B7 functions by blocking PA receptor binding site • Small loop (679-693) residues are critical for receptor binding. • 14B7 family antibodies function by blocking receptor binding site (Rosovitz et al, JBC 2003) PA Domain II PA Domain IV Small loop CMG Receptor

8. Affinity maturation in 14B7 family

9. Mutations in mAbs14B7, 1H and M18 Heavy Light Mutations L2 mutations

10. Errors in mAb 14B7 homology model H1 H3 • White and Gray : Crystal structure • Green and Yellow: WAM model • CDR H3 rmsd is 2.7Å

11. Objectives • Predict the structure of the 14B7-PA complex • Propose structural explanation for affinity maturation in 14B7 family? • Investigate effect of homology model inaccuracies on docking.

12. Model 14B7 using WAM PA-14B7 (Xtal or WAM) docking using RosettaDock 1x105 modelsbest scorescluster~200 structures Filter structures using known PA and 14B7 hotspot residues ~ 6 preliminary models Calculate G of mutations using RosettaInterface Consistent with experiments? No Yes Reject Validate with new computational and experimental mutagenesis

13. Docking Algorithm Overview Random Start Position Low-Resolution Monte Carlo Search High-Resolution Refinement 105 Clustering Predictions

14. Low-Resolution Decoy random perturbation repack START minimization FINISH High-Resolution Refinement Small Rigid-Body Move • Simultaneous rigid-body and side-chain optimization Repack Side Chains Rigid-Body Minimization Monte Carlo Accept? Filter Reject 50x Clustering

15. Full-Atom scoring • Terms: (combined linearly) • Attractive Van der Waals tight packing • Repulsive Van der Waals avoid clashes • Solvation (pair-wise Gaussian exclusion)bury non-polars (Lazaridis/Karplus) • Hydrogen bonding(Kortemme) • Side-chain conformational energyuse common rotamers • Residue pair scores statistical catch-all • Solvent-Accessible Surface Area (ASPs) • Electrostatics

16. Experimental information is used to guide predictions post-docking Antigen Antibody Rosovitz et. al. JBC 2003. Maynard et. al. unpublished

17. Contact filter based on known hotspots

18. Consensus between models • Homology model docking generates low resolution representations of crystal structure docking models

19. Computational mutagenesis using known hotspots

20. Final models Model 1 Antigen hotspots contact chain L Model 3 Antigen hotspots contact chain H

21. Final models Models 1 and 3 share 67 and 87% of receptor and ligand interface residue identities BUT 0% common 14B7-PA interface contacts

22. New antigen epitope regions identified 712-720 681-688 (Known) 648-660

23. I656 D648 N682 E650 D658 D683 S717 R53 N92 R50 Model 3 hydrogen bonding R99

24. Model 3 hydrophobic interactions L685 Y688 Y52 E654 L652 L97 W33 Y100 Y50

25. Model 1 hydrogen bonding interactions K653 D648 N691 N719 E654 D683 Y52 R53 W33 Y49 R99 N92

26. Model 1 hydrophobic interactions I656 E654 L97 Y688 L652 L98 Y50 Y32 W33 Y100

27. Site-directed antigen mutations for model validation ++ Neutral - Reduced binding

28. Implications for affinity maturation pathway • Mutated residues do not contact the antigen significantly Model 1 Model 3

29. Affinity maturation hypotheses • Loop entropy contribution? • VL-VH interface stabilization?

30. Affinity maturation • Previously proposed explanations • Increased hydrophobic surface burial (HyHEL-HEL, Li et. al, 2003) • Enhanced electrostatic complementarity (TEM-BLIP, Joughin et. al, 2005) • Cumulative effect of minor structural alterations (4M5.3-Fluorescein, Midelfort et. al., 2004) • Potential explanations for the mAb 14B7-PA interaction • Rigidification of CDR L2 (Q55L and S56P mutations) or CDR L3 (L94P mutation) • Rigidification of CDR H3 contributes ~ 2kcal/mol (  G = -1.5RT ln(n), Wang et. al. 2005) • Stabilization of VL-VH interface (L46F, Q55L and L94P mutations) VL-VH interface stabilization? Electrostatics (K64E)?

31. Homology model docking produces low-res representations of crystal structure docking Model 1 Model 3

32. CDR H3 conformation modeling critical for high-res docking WAM WAM H3 chimera H3 chimera 14B7 14B7

33. L: N92 L652 H: W33 S690 Y688 L: Y50 H: R50 Figure : Illustration of minor backbone differences between the 14B7 and the WAM structures that produce docking errors. (a) light chain and (b) heavy chain. 14B7 and PA toxin from best 14B7 model (grey and green); WAM model of 14B7 (blue) after structural superposition of the antibody framework with the 14B7 structure. WAM model errors frustrate high-resolution docking prediction

34. Conclusions • Two candidate structures for 14B7-PA complex • Computational mutagenesis suggests good agreement with experimental data on interface. • Affinity maturation not mediated directly by contacting residues • Alternate explanations are needed • Homology docking agrees with crystal structure docking at low-resolution • Improvements in CDR H3 modeling • Docking with backbone flexibility

35. Acknowledgments • Georgiou & Iverson Labs – University of Texas • Jennifer Maynard (UMn) • Andrew Hayhurst • Johns Hopkins • Carlos Castaneda • Sony Somarouthu • Mike Daily • Aroop Sircar • David Baker Lab – University of Washington • Tanja Kortemme (UCSF) • Brian Kuhlman (UNC) • Carol Rohl (UCSC) • Rees & Whitelegg • NIH/NHGRI K01-HG02316