1. 9. Protein interface Alanine Scanning and Design, continued 1

2. Interface manipulation and design 1.Computational Alanine scanning 2.Computational binding prediction 3.Interface design – Specificity switch – Negative design – Multistate design – De novo design – Design of multiprotein assemblies: cages and layers 2

3. Negative design Problem: optimization for a given fold / interaction does not guarantee that other alternative folds / interactions are not more favorable for a sequence Solubility: prevent aggregation Compactness: prevent molten globule states Specificity: Negative design prevents alternative conformations / interactions 3

4. Negative design against hetero-dimer Sequence 2 is better than Sequence 1: specific, even though higher in energy Design of Homo-dimeric coiled-coils (Havranek & Harbury NSB 2003) 4

5. Design of protein-interaction specificity gives selective bZIP-binding peptides (Grigoryan et al, Nature 2009) bZip transcription factors: – Leucine zipper: Coiled-coil – Homodimerize, heterodimerize – Human: ~53 bZip, 20 different families. 5

6. bZIP proteins Basic region Zipper region 6

7. Leucine zipper is responsible for dimerization specificity GCN4- GCN4Jun- JunFos- JunFos- Fos Bzip region alone acts as inhibitor 7 Challenge: design of specific leucine zippers inhibitors (minimize binding of inhibitor to bZips from other families)​

8. Hydrophobic packing at a-d, Salt bridge at e-g positions 8

9. Design of protein-interaction specificity gives selective bZIP-binding peptides (Grigoryan et al, Nature 2009) Challenge: design specific inhibitors to 46 human bzips Scheme: + Binding to target -No binding to self -No binding to 19 other classes of human bzip proteins Affinity-specificity tradeoff: minimize affinity loss for increased specificity 9

10. Design of protein-interaction specificity gives selective bZIP-binding peptides CLASSY (cluster expansion and linear programming- based analysis of specificity and stability ) integer linear programming (ILP) – find optimal sequence cluster expansion - convert a structure-based interaction model into sequence-based scoring function (very fast)  simultaneous consideration of many different competing sequences possible (efficient negative design ) Here: include additional constrain: compatibility with bzip PSSM 10

11. CLASSY setup for Bzip 11 Sparse interaction scheme – simple system

12. Design of protein-interaction specificity gives selective bZIP-binding peptides Approach: “Specificity Sweep” - minimize sacrifice in stability when increasing energy gaps from competing complexes 1 1 2 2 3 3 4 4 12 True for 65% of bZIP designs!

13. Design of protein-interaction specificity gives selective bZIP-binding peptides (Grigoryan et al, Nature 2009) 13

14. Design of protein-interaction specificity gives selective bZIP-binding peptides (Grigoryan et al, Nature 2009) Results: Specific design: highest affinity to target (or target sibling) Good inhibitors: target binds better to design than to its original partner 14

15. Design of protein-interaction specificity gives selective bZIP-binding peptides (Grigoryan et al, Nature 2009) Analysis of sequence diversity and specificity designed sequences are less diverse, but contribute many more Interactions Conclusion: interaction space was not fully sampled by evolution: ~ >1900 new possible interactions Excellent for synthetic biology!! natural designs 15

16. Multispecificity design: binding to many partners Humphris & Kortemme (2007) PLoS CB What are the restrictions of evolution on protein binding? How is promiscuity achieved? 16

17. Multispecificity design: binding to many partners 2. redesign interface sequence using a genetic algorithm Protocol: 1. Dataset comprised of 20 proteins with solved complex structures. 17

18. Multi-faceted binding in Hub protein RAN Humphris & Kortemme (2007) PLoS CB (grey –not at interface in that structure) 18

19. Nature gains multispecificity by two strategies Humphris & Kortemme (2007) PLoS CB Group I: Small shared interface - little improvement in sequence recovery by using multiple constraints Group II: Large shared interface - Multiple constraints improve sequence recovery 19

20. Difference in binding contribution Humphris & Kortemme (2007) PLoS CB Group I: single-constraint performs as well as multi-constraint Group II: multi-constraint performs better than single-constraint “tradeoff value”: improvement in energy of single design compared to multi design. Highly shared residues: residues with low tradeoff values 20

21. Difference in binding contribution Humphris & Kortemme (2007) PLoS CB High compromise: Ran Medium compromise: CheY Low compromise: Ovomucoid inhibitor 21

22. What next? De novo design of interaction (Fleishman 2011, Science; Fleishman 2011, JMB) Aim: design a new interaction from stratch System: high-affinity binder to constant region of Influenza Hemagglutinin (1918 pandemic) – could help for general vaccine – eradication of influenza – broadly neutralizing antibody known (CR6261) 22

23. Overview of approach (Fleishman 2011, Science) 23

24. Hotspots from known interface More general: individual residues mapped on surface Create libraries (inverse rotamer approach) 1. Hotspot library design Dock single amino acids onto defined surface patches of the target: – HS1 – HS2 – HS3 24

25. 2. Find shape complementary scaffolds Search set of 865 proteins – Easy to express Use Patchdock to find shape complementarity Refine with RosettaDock with constraints to match as many hotspots as possible Filter >1000A 2 buried surface area < -15 REU > 0.65 shape complementary replace all interface residues in scaffold with Ala (except Gly & Pro) to increase chance of match 25

26. 3. Incorporate hotspot residues Replace matching positions on scaffold with hotspot residues from library: For each position near hotspot in scaffold For each rotamer in library 1.attach scaffold to hotspot 2. optimize RB orientation Applied to: – HS1 -> HS2 (2 residue strategy) – HS3 ->HS1 &HS2 (three residue strategy 26

27. 4. Design scaffold residues around hotspots Several rounds of design/minimization Reduce # mutations: Residues with improvement of <0.5REU are reverted back to wt 27

28. 5. Results 88 designs, derived from 79 different protein scaffolds, average of 11 mutations Importance of structural genomics – provides good scaffolds Experimental assessment: yeast display – Allows for fast validation of many candidates Specificity of binding assessed by competition with Cr6261 neutralizing antibody 28

29. 2/88 bound with medium affinity 29

30. 6. What next? Affinity maturation with yeast surface display Express protein of interest on surface Identify rapidly binding partners ➜ fast in vitro evolution Simultaneous detection of expression and binding biotin strepavidin phycoerythrin 30

31. Affinity maturation Few mutations increase affinity dramatically, ….. and identify weaknesses of computational approach 31

32. 7. Proof: crystal structure 32

33. 8. What can we improve? Steric interactions Salt bridgesSolvation 33

34. Generalization of de novo design (Fleishman 2011, JMB) Protocol tested on a benchmark of interactions: 34

35. Incorporate negative design 35 Observation: Restricted side chain plasticity in interfaces of native complexes Hotspot residue conformations @interfaces are prepositioned – Do not change upon binding – Stabilized in monomer  Might prevent non-native interactions by restricting side chain conformation  Negative design in Nature Aim: reproduce this in our designs Stabilize hotspots within monomer – Good internal as well as binding energy – Design in clusters Critical feature for successful designs!

36. Challenges ahead: challenging interfaces in nature Networks of hydrogen bonds and waters Strand pairing Antibodies: Considerable loop flexibility allows creation of binding partners using Y/S alone Sheet interactions 36

37. Interface design - summary Binding Prediction Effect of point mutations effectively predicted Prediction of binding specificity of different protein pairs is difficult Polar effects are modeled less well than hydrophobic interactions Design of binding Creation of specificity switches is difficult, but possible Combine computational design with experimental refinement (e.g. in vitro evolution) Negative design can be important to achieve binding specificity De novo design of interaction achieved!! 37

38. Multiprotein complex design: cages and layers Natural large protein assemblies Biotechnology  Goal : Efficient design of protein assemblies  Application: vaccine design,molecular delivery agent

39.  Symmetric assemblies: fewer distinct interfaces  Small subunit forms the whole final assembly Geometry of protein assemblies  Cages or shells  Layers Symmetry in natural protein assemblies

40. Fusion of natural oligomers Interface design Design strategies

41. Goal: Design cage like protein nanomaterials e.g. Octahedral symmetry; Trimer building blocks King, Sheffer, et al. Science 2012

42. 1.Symmetric docking of protein building blocks in a target symmetric architecture 1.Design low-energy protein-protein interfaces between the building blocks Computational method: Rosetta matdes_dock

43. Single subunit + Symmetry definition file Symmetrical arrangement of building block at the origin Full space of contacting symmetry configurations sampled Step 1. Symmetrical docking Select conformations with highest C b contacts for interface design King, Sheffer, … Baker. Science 2012

44. Form grid of configuration centered at docked configuration Select amino acids to be changed with specific criteria Design these residues only (RosettaDesign) Minimize side chains at the designed positions Model selection according to: interface size, shape complementarity and binding energy Test mutations using Foldit Step 2. Interface design King, Sheffer, et al. Science 2012

45. Experimental methods: PAGE  Determine size of each protein (PAGE; non- denaturing conditions)  Shift in apparent size relative to the corresponding wt Trimer -> self-assembly to designed size King, Sheffer, et al. Science 2012

46. Particles of expected size (~13 nm) Experimental Methods: Negative - Stain Electron Microscopy Resemblance to projections along symmetry axes

47. EM and crystal structure are similar

48. Extension to multi - component systems King, Bale, Sheffer, … & Baker. Nature 2014

49. Layer design Shane, … & Baker. Science 2015

50. Design of cages and layers: Conclusions  Symmetric docking & interface design: simple & generally applicable strategy to broad range of symmetric materials  Building blocks: here naturally occurring oligomeric proteins, but de novo design possible too  Designed, self-assembling protein materials: basis of advanced functional materials and custom designed molecular machines

51. Using Rosetta - the day after Robetta (http://robetta.bakerlab.org/)http://robetta.bakerlab.org/ “ROSIE” (http://rosie.rosettacommons.org/)http://rosie.rosettacommons.org/ Backrub (https://kortemmelab.ucsf.edu/backrub/cgi- bin/rosettaweb.py?query=index)https://kortemmelab.ucsf.edu/backrub/cgi- bin/rosettaweb.py?query=index Design (http://rosettadesign.med.unc.edu/)http://rosettadesign.med.unc.edu/ “FlexPepDock” (http://flexpepdock.furmanlab.cs.huji.ac.il/)http://flexpepdock.furmanlab.cs.huji.ac.il/ “FunHunt” (http://funhunt.furmanlab.cs.huji.ac.il/)http://funhunt.furmanlab.cs.huji.ac.il/