9. Protein interface Alanine Scanning and Design 1.

9. Protein interface Alanine Scanning and Design

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Interface manipulation and design

1. Computational Alanine scanning2. Computational binding prediction3. Interface design

– Specificity switch– Negative design– Multistate design– De novo design

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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….

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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

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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

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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. Gbind>1kcal/mol)

• Binding energy: Gbind

Gbind = GAB - GA – GB

GbindWT = GAB

WT - GAWT - GB

WT GbindALAX =

GABALAX - GA

ALAX – GBALAX

GbindALAX = Gbind

WT - GbindALAX

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Kortemme et al; Sci. STKE, Vol. 2004

Rosetta computational

alanine scanning - flowchart

Possibilities1.just remove side chain2.repack neighboring residues3.preminimize structure4.…..

Just removing side chain works usually best

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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 Gexp-Gpred with conjugate gradient minimization) 8

Rosetta computational alanine scanning Kortemme & Baker,

PNAS 2002

ProteinG – IgG Fc Barnase - Barstar

HEL – Ab1 HEL – Ab2

Red: water-mediated interactions

• Calculate change in interface energy Gcalc

• Compare to experimental value Gobs

Significant correlation Water not modeled

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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

Gvdw van der Waals.GsolvH / GsolvP solvation energy apolar/ polar groupsGhbond hydrogen-bond energy Gwb water bridges at the interfaceGel electrostatic energySmc entropy cost for fixing the backbone in the folded state. (tendency of a particular amino acid to adopt certain dihedral angles)Ssc is the entropic cost of fixing a sidechain in a particular conformation.

Energy scaled according to exposure.10

FOLDX performance

Test on Gbind values for

▲ X-ALA mutationso ALA-X mutationsin T4 Lysozyme.

Good correlation Reliable prediction of

effect of point mutation

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G prediction: effect of mutation on binding

Different protocols are assessed for their ability to reproduce the experimentaleffect of mutation

Kellogg et al. (2011). Proteins, 79: 830

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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

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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

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3. Computational interface design

• Flavors of interface design

Mandell & Kortemme Nat Chem Biol 200915

Computational interface design

• Enhance binding

• Perturb binding & Recover binding

(2nd site suppressor strategy)

E

AWT BWT ADES BDES AWT BDES ADES BDES ADES BWT

• Specificity switch

(no binding to original partners)

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Increasing binding affinityUse• 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

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• Given: Interacting protein pair (known structure)

• Wanted: new protein pair that does not bind to original partners

Computational redesign of protein specificity

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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

Computational redesign of protein specificity Kortemme, …, Baker, NSB 2004

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E7

Im7

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

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Selected Mutations• Three mutations were selected by the

protocol in E7, 6 mutations were proposed on the Im7 to accommodate those.

A1 vs A2

Site I

Site II

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Site 1 – polar switch

Site 2 – steric switch

Cognate wt Cognate mutant – 1 site Cognate mutant – 2 sites 22

In Vitro DNase Activity Assay

Cognate wt Cognate mutant – 1 site Cognate mutant – 2 sites

Non-cognate mutants

c

A1 vs A2A1 vs A2

cont

rol

Site 1 Site 2

cont

rol

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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.

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In Vitro Binding Assay (SPR)

►Differences in dissociationWT :• Very slow dissociation rates (cannot be measured

with SPR/ Trp fluorescence) • Similar association rates

Cognate mutant – 1 site

Non-cognate mutants

association

dissociation

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E7C/Im7C crystal structure

• RMSD of 0.6Å over all IF atoms• RMSD of 0.5Å over backbone 26

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

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Computational design failed to create full switch – why?

• Nature developed full E-Im switches:E7-Im7 bindsE9-Im9 bindsE7-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 2005. Evolution of protein inhibitors of DNA-nucleases by in vitro compartmentalization (IVC) and nano-droplet delivery.*Levin, et al. NSMB 2009. Following evolutionary paths to protein-protein interactions with high affinity and selectivity .

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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 M

(+3.2)

MsoIDES

K28L T83R

6.1 M

(+1.6)

192 nM

(-4.2)

KD (Gcalc)

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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 / interactions30

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)

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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)

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Bzip proteins

Basic regionBasic region Zipper regionZipper region

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Leucine zipper is responsible for dimerization specificity

GCN4- GCN4 Jun- Jun Fos- Jun Fos- Fos

Jun- Jun

Bzip region alone acts as inhibitor

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

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• Challenge: design specific inhibitors to 46 human bzips

• Scheme:+ Binding to target- No binding to self- No binding to 19 other classes of humanbzip proteins

Tradeoff: maximize affinity & optimize specificity 36


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

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CLASSY setup for Bzip

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Sparse interaction scheme – simple system


Approach: “Specificity Sweep” - minimize sacrifice in stability when increasing energy

gaps from competing complexes

11 22 33 44

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Results:• Specific design:

highest affinity to target (or target sibling)

• Good inhibitors: target binds better to design than to its original partner

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Analysis of sequence diversity and specificity• designed sequencesare less diverse, but• contribute many moreInteractions

• Conclusion: interaction space was not fully sampled by

evolution: 1900 new possible interactions• Excellent for synthetic biology!!

natural

designs

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Multistate design: binding to many partners

Humphris & Kortemme (2007) PLoS CB

• What are the restrictions of evolution on protein binding? • How is promiscuity achieved?

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Multistate design: binding to many partners

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

Protocol:

1.select set of proteins that bind to multiple partners (solved structures; n=20)

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Multi-faceted binding in Hub protein RAN

Humphris & Kortemme (2007) PLoS CB (grey –not at interface in that structure)45

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

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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

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Difference in binding contribution


High compromise: Ran

Medium compromise: CheY Low compromise:

Ovomucoid inhibitor

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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) 49

Overview of approach (Fleishman 2011, Science)

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1. Hotspot library design

•Dock single amino acids onto defined surface patches of the target:

–HS1–HS2–HS3

•Create libraries (inverse rotamer approach)

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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

–C-distance;

–C-C and N-C 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

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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 library1. attach scaffold to hotspot2. optimize structure

•Applied to:– HS1 -> HS2 (2 residue strategy)– HS3 ->HS1 &HS2 (three residue strategy

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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

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5. Results88 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

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2/88 bound with medium affinity

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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

biotinbiotin

strepavidin

phycoerythrin

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Affinity maturation

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

7. Proof: crystal structure

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8. What can we improve?

Steric interactions Salt bridges Solvation

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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 interactions61

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!!

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9. Protein interface Alanine Scanning and Design 1.

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