1. Protein Design with Backbone Optimization Brian Kuhlman University of North Carolina at Chapel Hill

2. Rationale for Flexible Backbone Design Amino acid mutations often result in backbone rearrangement. Backbone rearrangement can allow for more favorable interactions with target ligands or substrates. Novel protein structures or complexes are generally not designable without backbone optimization.

3. Flexible Backbone Design Protocols in Rosetta Design and backbone optimization of a selected region of a protein (loop or terminus) Design and backbone optimization of a protein-protein interface Design and backbone optimization over a whole monomeric protein

4. Protein Design with Backbone Optimization Starting structure – should resemble final target structure Design optimal sequence for the protein Optimize the backbone coordinates Design final sequence for the protein

5. start 1)random perturbation to phi,psi angles 2)very rapid rotamer optimization 3)gradient minimization in phi,psi space 4)accept moves based on the Metropolis criterion For each cycle of backbone optimization, ~2000 Monte Carlo steps were performed Backbone Optimization – Monte Carlo Minimization (1) (2) (3) Only phi and psi were varied in the backbone, all bond distances and angles were idealized.

6. Design optimal sequence for the protein Allow the protein to relax in phi,psi space ~10 cycles During this procedure the – 1) the backbone moves ~ 2 Å RMSD 2) > 50% of the residues typically change identity 3) Lennard-Jones energies became comparable to those in naturally occurring proteins Typical Flexible Backbone Optimization Protocol

7. Flexible Backbone Design Protocols in Rosetta Design and backbone optimization of a selected region of a protein (loop or terminus) Design and backbone optimization of a protein-protein interface Design and backbone optimization over a whole monomeric protein

8. Test case: redesign a loop in the context of a well-folded protein Tenascin Protocol for loop design Remove the WT loop Build a new backbone for the loop from PDB fragments Iterate between designing a sequence for the loop and optimizing its conformation Jenny Hu

9. Building the Starting Structures for Loop Design Select loops from the PDB that best overlay with the takeoff residues Close the loops and remove clashes with neighboring residues using 3- residue fragment insertions, small random perturbations to phi and psi angles, and gradient-based minimization ( low resolution scoring function ) 3 of the starting structures selected for high resolution design

10. Iterating Between Sequence Design and Backbone Refinement Sequence design: allow all amino acids for residues in the loop, neighboring amino acids are free to adopt alternative rotamers Backbone refinement: small random changes to phi and psi angles followed by gradient based minimization (same energy function used for sequence design and backbone refinement) Starting seq:LPTQLPVEG Ending seq:QKTQLPVDG

11. Iterating Between Sequence Design and Backbone Refinement Blue: Starting structure / sequence Green: Minimized structure / sequence

12. 3 Loops Picked for Experimental Validation ( from 7200 flexible backbone design trajectories) Designed Sequences WT FKPLAEIDGI L1 SMQLSQLEGI L3 MPPSQPVDGF L6 ALPSRPLDGF

13. WT Loop1 Loop3 Loop6 P24 M23 L28 I31 I28 I31 V28 L28 F31 P24 F31 P23 P24 L23

14. The Loop Designs are Folded Fraction Unfolded

15. Crystal Structure of Loop3 Green: crystal structure Purple: design model Resolution: 1.45 Å

16. pH = 3 Crystal Structure of Loop6

17. Flexible Backbone Design Protocols in Rosetta Design and backbone optimization of a selected region of a protein (loop or terminus) Design and backbone optimization of a protein-protein interface Design and backbone optimization over a whole monomeric protein

18. Protocol for Designing Binding Proteins target Design scaffold 1) Rigid body docking of design template on to the target 2) Fixed backbone sequence design of interface residues 3) High resolution refinement of rigid body orientation and scaffold loops 4) Identify design models that are most likely to bind the target Andrew Leaver-Fay, Ramesh Jha, Glenn Butterfoss

19. Targeting the p21-Activated Kinase (PAK1) PAK1 kinase domain PAK1 autoinhibitory domain

20. Example of Designed Interface Target – PAK1 Designed Protein Andrew Leaver-Fay

21. Flexible Backbone Design Protocols in Rosetta Design and backbone optimization of a selected region of a protein (loop or terminus) Design and backbone optimization of a protein-protein interface Design and backbone optimization over a whole monomeric protein

22. Successful Design of a Novel Protein Structure (TOP7) Red: Design model Blue: crystal structure T m > 100 C°  G° unf > 10 kcal / mol

23. N Template for a  -Sandwich Protein

24. Starting structures for  -sheet Design

25. Current Status of  -sheet De Novo Design Project 4 sequences selected for experimental study from ~50,000 flexible backbone simulations All of them appear to adopt  -structure as evidenced by circular dichroism NMR lines are broad Gel filtration indicates that they are not monomeric

26. What is missing from the  -sheet design process? Do we need to do more conformational sampling to find a backbone that is designable (positive design)? Do we need to explicitly destabilize alternative backbone structures (negative design)?

27. Can we design a well-folded  -sandwich if we start with a naturally occurring protein backbone? Target Structure: Tenascin 1)Strip away naturally occuring side chains. 2)Design a new sequence allowing all amino acids at each sequence position. Resulting sequence 39% identical to WT 60% identical in the core

28. Redesigned Tenascin is Well-Folded 1D-NMR of Redesigned Tenascin

29. Redesigned Tenascin is more stable than Wild-Type Tenascin

30. Acknowledgements Loop Design Jenny Hu Hengming Ke Interface Design Andrew Leaver-Fay Glenn Butterfoss Ramesh Jha  -sheet Design Jenny Hu