1. 9. Protein interface Alanine Scanning and Design

2. Interface manipulation and design
Computational Alanine scanning
Computational binding prediction
Interface design
Specificity switch
Negative design
Multistate design
De novo design

3. Interface design is difficult
very diverse shape and chemical character at interfaces
electrostatics are difficult to model at the interface
balance between electrostatic and hydrogen-bonding and unfavorable de-solvation of polar groups at protein–protein interfaces
water molecules at protein interfaces
dynamical behavior of a protein upon binding
Considerable entropy–enthalpy compensation
Changes can be dramatic
… But it is possible! Let’s start from small: predict effect of mutation to alanine….

4. 1. Computational Alanine Scanning
A small number of residues at interface contribute most of the binding energy: Interface hotspots
Given: structure of a protein complex
Wanted: detect hotspots
Approach: experimental or computational alanine scanning

5. A hotspot of binding energy in a hormone-receptor interface Clackson & Wells, Science 1995
Experimental alanine scanning of HGH – HGHr interface
30 residues
only 2-10 residues contribute
complementary in structure

6. Computational alanine scanning
Given: Structure of protein complex
Wanted: list of interface hotspot residues
Approach:
compute loss in binding energy upon mutation to alanine (for each interface residue)
Select positions with large effect (e.g. DDGbind>1kcal/mol)
Binding energy: DGbind
DGbind = GAB GA – GB
DGbindWT = GABWT - GAWT - GBWT DGbindALAX = GABALAX - GAALAX – GBALAX
DDGbindALAX = DGbindWT - DGbindALAX

7. Rosetta computational alanine scanning - flowchart
Possibilities
just remove side chain
repack neighboring residues
preminimize structure
…..
Just removing side chain works usually best
Kortemme et al; Sci. STKE, Vol. 2004

8. Rosetta alanine scanning: energy function
ELJattr , ELJrep Lennard-Jones potential
EHB(sc−bb) , EHB(sc−sc) Hydrogen bond potential
Esol Implicit solvation model
Stability of monomers:
Eφ/ψ(aa) aa-dependent phi-psi propensity
Eaa ref aa-dependent reference energy
W Relative weights of the different energy terms
optimized on monomer mutations
(minimizing DGexp-DGpred with conjugate gradient minimization)

9. Rosetta computational alanine scanning Kortemme & Baker, PNAS 2002
ProteinG – IgG Fc
Barnase - Barstar
Calculate change in interface energy DDGcalc
Compare to experimental value DDGobs
 Significant correlation
 Water not modeled
HEL – Ab2
HEL – Ab1
Red: water-mediated interactions

10. FOLDX alanine scanning Guerois, 2002
Calibration & validation on experimental values of effect of mutation in monomers (n=339 & 1000)
Application to prediction of effect on binding of two proteins
Energy function similar to Rosetta score
DGvdw van der Waals.
DGsolvH / DGsolvP solvation energy apolar/ polar groups
DGhbond hydrogen-bond energy
DGwb water bridges at the interface
DGel electrostatic energy
DSmc entropy cost for fixing the backbone in the folded state. (tendency of a particular amino acid to adopt certain dihedral angles)
DSsc is the entropic cost of fixing a sidechain in a particular conformation.
Energy scaled according to exposure.

11. FOLDX performance Test on DDGbind values for X-ALA mutations
ALA-X mutations
in T4 Lysozyme.
Good correlation
Reliable prediction of effect of point mutation
Figure 3. Calculated DDGs com- pared to the experimental DDGs for the 84 mutants of the T4 lysozyme database. The continuous lines represent the linear regressions obtained considering the 42 muta- tions (X ! A, G) shown as open circle (eq: y ¼ 20:072 þ 0:87x; R ¼ 0:83), the 42 reverse mutations (A, G ! X) shown as filled triangles (eq: y ¼ 0:12 þ 0:92x; R ¼ 0:81), or the sum of them (eq: y ¼ 20:013 þ 0:86x; R ¼ 0:81), or the sum of them (eq: y ¼20:013 þ0:86x; R ¼ 0:89).

12. DDG prediction: effect of mutation on binding
Different protocols are assessed for their ability to reproduce the experimental
effect of mutation
Kellogg et al. (2011). Proteins, 79: 830

13. 2. Prediction of binding energy
Step1: single mutations
FOLDX & Rosetta Alanine scanning indicate that the effect of single amino acid changes on binding can be predicted
Extension: Binding specificity
predict the relative binding energy of different protein-protein interactions
Step2: who binds??
predict binding partners that involve binding between given pair of domains
Domain A
Domain B

14. Computer-based definition of interacting pairs – in short:
Multiple mutations more difficult to model
No general scheme available – approaches are system-specific
Prediction of binding for given sequence pairs is difficult: subtle differences from the homolog template are not predicted
Alternative: Find possible sequence pairs: Computational Interface Design

15. 3. Computational interface design
Flavors of interface design
Figure 1 Concepts for protein interface design. (a) Designed substitutions to increase interface affinity can either be made in both partners in a complex (left) or just to one partner designed to match a fixed target (right). Such strategies are useful to develop protein interaction inhibitors. (b) One of the simplest formulations of the selectivity design problem is to change an existing interface A-B so that the two new partners A' and B' specifically recognize each other in such a way that cross-talk interactions with their original wild-type partners (A'-B and A-B') are avoided (left). Such ‘orthogonal pairs’ are useful for re-wiring of signaling pathways mediated by protein interactions. The right schematic illustrates the design of a homodimer into a heterodimer. (c) Functional design of ‘hub’ proteins accounts for multi- specificity across shared interfaces (left). Many key signaling proteins, such as GTPases (right), exhibit multi-specificity through overlapping interfaces that bind several partners (GTPase Ran in grey, multiple partners colored). (d) Protein design can be useful to dissect individual interactions (left), an approach that is complementary to common knockout approaches of genes or proteins (right).
Mandell & Kortemme Nat Chem Biol 2009

16. Computational interface design
Enhance binding
Perturb binding & Recover binding
(2nd site suppressor strategy)
Specificity switch
(no binding to original partners) E
AWT BWT
ADES BDES
AWT BDES
ADES BDES
ADES BWT

17. Increasing binding affinity
Use
Inhibitors
Therapeutics
Strategies
Increase hydrophobic buried surface area in interface
Add polar interaction at periphery
Increase on-rate by electrostatic steering
Hydrogen bonding network: multi-residue problem – achieved, but not with increasing affinity

18. Computational redesign of protein specificity
Given: Interacting protein pair (known structure)
Wanted: new protein pair that does not bind to original partners

19. Computational redesign of protein specificity Kortemme, …, Baker, NSB 2004
Approach: “Second site suppressor design”
Identify perturbations in one monomer that destabilize the interaction (negative design)
Compensate by redesigning the other monomer (positive design)
Screen each position for detrimental mutation that can be compensated by mutations in partner
Optimize region by repacking adjacent residues

20. Computational redesign of protein specificity Kortemme, …, Baker, NSB 2004
System: E7-Im7: Colicin (DNase)-Immunity protein
Crystal structures solved
Remarkably specific, bind with high affinity
Easily assayed – protects cells from death
Active site distinct from binding
Bonus: altered specificity might be used to make antibiotics E7
Im7

21. Selected Mutations
Three mutations were selected by the protocol in E7, 6 mutations were proposed on the Im7 to accommodate those.
Site I
Site II
Col3 – from first screen
Col4 – after calculation of binding energy
Col7,8 only mutations to one of the sites (1 or 2)
A1 vs A2

22. Site 1 – polar switch Site 2 – steric switch Cognate wt
Cognate mutant – 1 site
Cognate mutant – 2 sites

23. In Vitro DNase Activity Assay
control
control
Site 1
Site 2
A1 vs A2
A1 vs A2
Cognate wt
Cognate mutant – 1 site
Cognate mutant – 2 sites
Non-cognate mutants

24. Measuring binding affinity with Surface Plasmon Resonance (SPR)
Kastritis, P. L., & Bonvin, A. M. J. J. (2013). Journal of the Royal Society, Interface 10:835.

25. In Vitro Binding Assay (SPR)
dissociation
association
Cognate mutant – 1 site
►Differences in dissociation
WT :
Very slow dissociation rates (cannot be measured with SPR/ Trp fluorescence)
Similar association rates
Non-cognate mutants

26. E7C/Im7C crystal structure
Other did not crystallize
Yellow is crystal
Left is wt
RMSD of 0.6Å over all IF atoms
RMSD of 0.5Å over backbone

27. Design of E-Im specificity switch: Conclusions
Structural:
 well-modeled side chains with hydrogen bonds – accurate energy function
 solvation effects are poorly described,
 water at interface important, but not modeled.
General:
 Full switch not achieved

28. Computational design failed to create full switch – why?
Nature developed full E-Im switches:
E7-Im7 binds
E9-Im9 binds
E7-Im9 does not bind
So why does computational design fail? Too restricted to starting structure
Followup study addresses this drawback (Kortemme, Joachimiak, Stoddard & Baker JMB 2007)
Alternative approach: experimental – in vitro evolution approaches (e.g. work by group of Dan Tawfik at WIS*)
*Bernath, Magdassi & Tawfik, JMB Evolution of protein inhibitors of DNA-nucleases by in vitro compartmentalization (IVC) and nano-droplet delivery.
*Levin, et al. NSMB Following evolutionary paths to protein-protein interactions with high affinity and selectivity.

29. Design of new Endonuclease Ashworth 2006
Apply second site suppressor strategy to protein-dna interactions
DNAWT
-6C:G, +6A:T
DNADES
-6G:C, +6C:G
MsoIWT
61 nM
(0.0)
>25 mM
(+3.2)
MsoIDES
K28L T83R
6.1 mM
(+1.6)
192 nM
(-4.2)
KD (DDGcalc)

30. 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

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

32. bZip transcription factor family:
Design of protein-interaction specificity gives selective bZIP-binding peptides (Grigoryan et al, Nature 2009)
bZip transcription factor family:
Leucine zipper: Coiled-coil
Homodimerize, heterodimerize
Human: ~53 bZip, 20 different classes
Challenge: design of inhibitor specific leucine zippers (prevent side-effects due to binding of inhibitor to other bZips in genome)

33. Bzip proteins
Basic region
Zipper region

34. Leucine zipper is responsible for dimerization specificity
GCN4- GCN4
Jun- Jun
Fos- Jun
Fos- Fos
Jun- Jun
Bzip region alone acts as inhibitor

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

36. Challenge: design specific inhibitors to 46 human bzips Scheme:
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
Tradeoff: maximize affinity & optimize specificity

37. 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

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

39. 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 2 3 4

40. With processing specificity sweep:
Design of protein-interaction specificity gives selective bZIP-binding peptides (Grigoryan et al, Nature 2009)
Results:
Homodimer most stable
With processing specificity sweep:
Gap to other sequences increases
Stability decreases

41. Specific design: highest affinity to target (or target sibling)
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

42. Analysis of sequence diversity and specificity
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

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

44. Multistate design: binding to many partners
Protocol:
select set of proteins that bind to multiple partners (solved structures; n=20)
2. redesign interface sequence using
All structures together (use genetic algorithm to propagate sequence changes to all structural templates)
each structure separately
3. Compare outcome

45. Multi-faceted binding in Hub protein RAN
(C) Optimal interface sequences taken from the endpoint of the trajectories in (A) and (B). The first row in the table contains the interface residue PDB numbering, the second row lists the native sequence (red), and the following rows list sequences predicted to be optimal in each simulation: multi- constraint (second sequence), single-constraint (third through seventh sequences). Plus signs in the table denote that the wild-type amino acid residue type was recovered as optimal. The number and percent of interface residues recovered as identical to native is shown for each simulation in the rightmost column. Grey shading denotes interface positions not within 4 A ̊ of the shaded interaction partner
74:The design simulations predict that three of Ran’s binding prefer side-chains larger that the wild-type glycine that have additional side-chain hydrogen bonding capability. However, tight steric constraints for binding the remaining two partners necessitate glycine to be the ‘‘optimal’’ compromise for this interface position.
76: Ran interface residue that our simulations predict to be highly shared among all partners. Here the wild-type residue, arginine, is correctly recovered by every single-constraint simulation where it mediates an inter-chainhydrogen bonding network.
Humphris & Kortemme (2007) PLoS CB
(grey –not at interface in that structure)

46. Two strategies Group I: distinct patches at interface
No improvement in sequence recovery by using multiple constraints
Group II: same interface for different partners
Multiple constraints improve sequence recovery
(A) The number of residues recovered as identical to native are plotted for each promiscuous protein (see Figure 2). For reference, the size of the shared interface is shown for each protein in red. For roughly half the dataset, (group II, pink shading), sequence recovery from the multi-constraint simulations (black) significantly out-performed the average single-constraint recovery (grey). The remaining proteins (group I, blue shading) showed similar native recovery regardless of whether sequences were optimized with respect to one or all characterized partners. Error bars represent the best and worst native sequence recovery in a single-constraint optimization.
Humphris & Kortemme (2007) PLoS CB

47. Difference in binding contribution
Group I: distinct patches at interface
Group II: same interface for different partners
“tradeoff value”: improvement in energy of single design compared to multi design.
Highly shared
residues:
residues with
low tradeoff values
Tradeoff at each interface position in our dataset was estimated by the per-residue difference in scores of amino acids chosen when each partner was optimized alone as compared with when all binding partners were considered in the optimization procedure (see Figure 1A2). The percentage of interface sites displaying the lowest level (0–0.5) of ‘‘tradeoff value’’ (see Methods and text) is shown for all 20 proteins in our dataset (A). Such positions are predicted to be highly shared, in that no partner considered had to ‘‘give up’’ potential gain so that other partners could fulfill their optimal interactions. Blue and pink shading denotes whether each protein was assigned to group I or II.
Humphris & Kortemme (2007) PLoS CB

48. Difference in binding contribution
Medium compromise: CheY
Low compromise: Ovomucoid inhibitor
High compromise: Ran
Tradeoff at each interface position in our dataset was estimated by the per-residue difference in scores of amino acids chosen when each partner was optimized alone as compared with when all binding partners were considered in the optimization procedure (see Figure 1A2). The percentage of interface sites displaying the lowest level (0–0.5) of ‘‘tradeoff value’’ (see Methods and text) is shown for all 20 proteins in our dataset (A). Such positions are predicted to be highly shared, in that no partner considered had to ‘‘give up’’ potential gain so that other partners could fulfill their optimal interactions. Blue and pink shading denotes whether each protein was assigned to group I or II.
Humphris & Kortemme (2007) PLoS CB

49. What next? De novo design of interaction (Fleishman 2011, Science)
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)

50. Overview of approach (Fleishman 2011, Science)

51. 1. Hotspot library design
Dock single amino acids onto defined surface patches of the target:
HS1
HS2
HS3
Create libraries (inverse rotamer approach)

52. 2. Find shape complementary scaffolds
Search set of 865 proteins
Easy to express
Use Patchdock to find loose matches to 3 hotspots
Refine with RosettaDock with constraints
Cb-distance;
Ca-Cb and N-Ca angles
Filter
>1000A2 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

53. 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
attach scaffold to hotspot
2. optimize structure
Applied to:
HS1 -> HS2 (2 residue strategy)
HS3 ->HS1 &HS2 (three residue strategy

54. 4. Design scaffold residues around hotspots
Several rounds of design/structure optimization
Minimize mutations: Residues with improvement of <0.5REU are reverted back to wt
Manual intervention – improve electrostatics

55. 5. Results 88 designs, Experimental assessment: yeast display
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

56. 2/88 bound with medium affinity

57. 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
strepavidin
biotin
phycoerythrin

58. Affinity maturation few mutations increase affinity dramatically,
….. and identify weaknesses of computational approach

59. 7. Proof: crystal structure

60. 8. What can we improve? Steric interactions Salt bridges Solvation
ig. 3. Affinity maturation. Substitutions that increase the affinity of the original designs reflect deficiencies in modeling the (A and B) repulsive interactions HB36 A60V (A), HB80 M26T (B); (C and D) electrostatics HB36 N64K (C), HB80 N36K (D); and (E and F) solvation HB36 D47S (E), HB80Kd (nM)NB (NB) 200 (>2000) (29) 4 (22) NB > (38)D12G (F). Binding titrations of HB36.4 (G) and HB80.3 (H) to SC1918/H1 HA as measured by yeast surface display. Red circles represent the affinity-matured design; blue squares, the scaffold protein from which the design is derived; and black crosses, the design in the presence of 750 nM inhibitory CR6261 Fab.

61. Challenges ahead: challenging interfaces in nature
Networks of hydrogen bonds and waters
Strand pairing
Computational challenges ahead. The diversity of protein interface characteristics observed in nature suggests future challenges for computational design. (A) Fleishman et al. designed a hydrophobic helix (purple) to bind a hydrophobic groove (gray) with unprecedented accuracy in binding location and orientation. (B) The high-affinity interaction between the bacterial proteins barnase and barstar features a sophisticated hydrogen bond network that also includes water molecules. (C) Strand-strand pairings at an interface feature regular repeats of polar atoms. (D) Imitating an antibody interface that features long loops will require precise backbone conformational sampling and scoring methods. Loops provide a rich diversity of backbone conformations, such that binding can occur using only tyrosine and serine side chains (5). (E) The quaternary structure of an antibody is stabilized by a sheet-sheet interface.
Antibodies: Considerable loop flexibility allows creation of binding partners using Y/S alone
Sheet interactions

62. 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!!