Artificial Enzyme-Computational Design of Catalytic Functions-

Literature Seminar
2014.2.3 (Mon.)
Yusuke Shimizu (B4)1

§1 Introduction

Biocatalysis
Biocatalysis = Application of enzymes and microbes to chemical transformations

Humans have utilized biocatalysis in the fermentation processes for millennia.

2§1 Introduction

Biocatalysis

Rosenthaler, L. Biochem. Z. 1908, 14, 238

Actual “biocatalyst” is an enzyme related to cyanogenesis

3§1 Introduction

What is Enzyme?
••••First isolation in 1833 by Payen. He discovered and isolated amylase.
Kühne coined term enzyme means “in yeast” in 1876
Sumner crystallized urease, and showed enzymes are proteins in 1926.
The first crystal structure was obtained in 1965 via X-ray crystallography
Enzyme is a protein catalyst , which is...

••••Highly selective (chemo-, regio-, diastereo-, and enatnio-selective)
Activated in mild condition (typically pH 5–8 and 20–40ºC)
Environment friendly (completely degraded in the environment)
Generally very efficient4§1 Introduction

Enzymes in Industry

Bruggink, A. Chimia, 1996, 50, 431

Semi-synthetic antibiotics

Jensen, V.J., Rough, S. Methods Enzymol. 1987, 136, 356

Convert glucose to the sweeter-tasting fructose

Laundry-detergent contain enzymes

5§1 Introduction

Artificial Enzyme
Protein engineering technologies has developed to design and synthesize
molecules with the attributes of enzymes(selective, proficient, green, nontoxic, and biodegradable) for non-natural reactions

Today, I introduce development of approach to enzyme design and finally,de novo Computational Enzyme Design.

6

Contents
1. Introduction
2. Catalytic Antibodies
3. Directed Evolution
4. Computational Enzyme Design
5. Summary & Future Outlook7Contents
1. Introduction
2. Catalytic Antibodies
3. Directed Evolution
4. Computational Enzyme Design
5. Summary & Future Outlook8§2 Catalytic Antibodies

Pauling’s hypothesis
I think that enzymes are molecules that arecomplementary in structure to the activated complexesof the reaction that they catalyze, that is, to the molecularconfiguration that is intermediate between reacting substances
and the products of reaction for these catalyzed process
Pauling, L. Nature, 1948, 161, 707

Linus Pauling

The transition state analog of a particular enzymatic reaction would bebound tightly to enzymes. (Concept of enzyme inhibitors)

9§2 Catalytic Antibodies

Catalytic Antibodies
According to Pauling’s hypothesis, conversely, a receptor designed to optimallybind a suitable analog of a transition state would achieve the catalytic function of anenzyme.

The immunological reservoir provides thevariety of receptor sites with the required
specificities for such a study. The combining
sites of antibodies have been considered as
useful templates for simulating the environment ofan enzyme active site

Antibodies specifically bind to corresponding antigens.

10§2 Catalytic Antibodies

Kinetics
Limited only by diffusion rate

Wolfenden, R., Snider M. J. Acc. Chem. Res. 2001, 34, 938
11§2 Catalytic Antibodies

Pioneering work by Schulz
Pioneering work of catalytic antibody using IgA MOPC167

KM = 208 μM
Kcat = 6.0*10-3 sec-1
kcat/KM = 28.8 M-1s-1
kcat / kuncat = 770

Schulz, P.G., et al. Science, 1986, 234, 1570

12

§2 Catalytic Antibodies

Achievement
A number of catalytic antibodies catalyze natural and non-natual reaction were generated.

kcat/KM = 5.5*103 M-1s-1

Diels-Alder reaction
Hilvert, D., et al. J. Am. Chem. Soc. 1989, 111, 9262

Kemp elimination
Hilvert, D., et al. Nature, 1995, 373, 22813§2 Catalytic Antibodies

Limitations• Lower binding constants• Lack of covalent binding and catalysis• Smaller buried surface area
• Inadequacies of the immunoglobulin fold
• Product inhibition• The comparatively low stability of the immunoglobulin fold
• High cost of producing antibody catalysts

14

Contents
1. Introduction
2. Catalytic Antibodies
3. Directed Evolution
4. Computational Enzyme Design
5. Summary & Future Outlook15

§3 Directed Evolution

Directed Evolution

Directed evolution is a molecular biology methods to modify biocatalysts
via in vitro version of “Darwinian evolution”
Directed evolution provide improved enzymatic activity, thermostability,tolerance to organic solvent, substrate specificity, enantioselectivity and so on.

Reetz, M. T. Angew. Chem. Int. Ed. 2011, 50, 138
16

§3 Directed Evolution

Mutagenesis

Sequence recombination

Random mutagenesis

Site saturation mutagenesis

Random mutagenesis: No structural or machanistic information is required

Site saturation mutagenesis : Require prior structural or biochemical knowledge
Dramatic functional alteration can be sought
Sequence recombination: Diversity can be further expanded17§3 Directed Evolution

Challenges• Identification of variants that have desired improvements out of large set of sequences.
(e.g. two mutations anywhere in 200 amino-acid protein have 7,183,900 possibilities)• Set up of high-throughput assays which can detect slight improvements18

§3 Directed Evolution

Application – Sitagliptin Manufacture
Route A••••

Need for high pressure H2
Use and removal of precious and toxic Rh
Ligand screening and synthesisInsufficient stereoselectivity

Route B
• Limited substrate range• Low turn over numbers• Stability to chemical processes conditions

Directed Evolution

Savile, C. K., et al. Science, 2010, 329, 305
19§3 Directed Evolution

Substrate range of transamirase
Transaminases have a limited substrate range;
Most of them accept only substrates with a substituent
no longer than methyl group at the position adjacent to the ketone

Screening a variety of transaminases provided no detectable activity.

20

§3 Directed Evolution

Docking Study
Using (R)-selective transaminase ATA-117, homology model was generated.

Undesired interactions

Steric interference

Prositagliptin ketone has problems in binding both with small pocket and large pocket.

substrate of ATA-117
Savile, C. K., et al. Science, 2010, 329, 305
21

§3 Directed Evolution

Round 1Firstly, the large binding pocket was engineered using truncated substrate
Consistent with the model,
this substrate was poorly active.
(4% conversion of 2g/L substrate)

Undesired interactions

Site saturation mutagenesis around the large pocket.
The best variant containing S223→P223 showed 11-fold activity
A small library for potential activity on prositagliptin ketoneAnalysis of the model suggested four residues (V69, F122, T283, and A284)
potentially interact with trifluorophenyl group. Savile, C. K., et al. Science, 2010, 329, 305
22§3 Directed Evolution

Round 1
ATA-117: S223→P223(S223P) variant
Saturation mutagenesis on V69, F122, T283, and A284 individually&Combinatorial library based on structural considerations.
(V69GA, F122AVLIG, T283GAS, and A284GF; 216 variants)
Single site saturation library provided no active variant.
Y26H, V65A, V69G, F122I, A284G provided initial active variant.
(0.7% conversion of 2g/L ketone)

Round 2Relieved steric interference
Effective interaction Savile, C. K., et al. Science, 2010, 329, 305
23§3 Directed Evolution

Round 2
Initial active variant
Library screening
Variant containing 12 mutations showed 75-fold activity
Despite these enhancement, this catalyst was not yet practical...
・Organic co-solvent (low water-solubility of ketone)
・Higher temperature (rate and solubility enhancement)
・Large excess of iPrNH2(transamination is equilibrium controlled)
Transaminase have to withstand these harsh conditions.

Further evolution24

§3 Directed Evolution

Round 3-11
Transaminase with enhanced activity
Library generated by a variety of methods (total 36480 variants)
Rendering condition more stringent with the rising tolerance
Final screening conditions
Substrate : 2 → 100g/L
iPrNH2: 0.5 → 1M
Co-solvent : 5 → 50% DMSO
pH: 7.5 to 8.5
Temp.
: 22ººC to 45ººC

Process tolerant transaminase containing 27 mutations25

§3 Directed Evolution

Summary of evolution

Savile, C. K., et al. Science, 2010, 329, 305
26

§3 Directed Evolution

Improvements

In comparison with Rh-catalyzed process,・10-13% increase in overall yield・No need for high-pressure H2 equipment
・53% increase in productivity (kg/L per day) ・Reduction in total manufacturing cost
・19% reduction in total waste
・Elimination of all heavy metals
Savile, C. K., et al. Science, 2010, 329, 305
27

Contents
1. Introduction
2. Catalytic Antibodies
3. Directed Evolution
4. Computational Enzyme Design
5. Summary & Future Outlook28

§4 Computational Enzyme Design

Computational enzyme design
1984

B.A. at Harvard University

1989

Ph.D. at University of California, Berkeley

1990-1993 Postdoctoral work at University of California, San Francisco
199X

Professor of Biochemistry, University of Washington

David Baker
Investigator, Howard Hughes Medical Institute

The Baker laboratory developed the Rosetta algorithm forProfessor of Biochemistry, University of Washington

de novo protein structure prediction.

Adjunct Professor of Genome Sciences

He aims to produce structural models for protein complexes

Adjunct Professor of Physics

as well as individual polypeptide chains.

Adjunct Professor of Computer Science
Adjunct Professor of Chemical Engineering
Adjunct Professor of Bioengineering

His group is recognized as the first group to have designed a protein
Here, I’ll focus on his works.
29§4 Computational Enzyme Design

De novo computational Enzyme Design
1. QM caliculation to generate “Theozyme”,functional groups stabilizing the TS.
A number of theozyme motif are usually generated.
2. Run RosettaMatch to search active sites of existing proteinsfor backbone position that can accommodate
three-dimensional side chain rearrangement of theozyme.
3. RossetaDesign attempts to generate the best possible
stabilization for a geometry .
Normally, even the highest ranked design difffer quiteconsiderably from the original theozyme geometry.
4. Final designs are assessed towards their capability to stabilize
the key catalytic residues on the basis of criteria such as
Rosetta energy, hydrogen bond, active site geometry etc...
5. Enzyme assay
6. Directed evolution
Houk, K. N., et al. Angew. Chem. Int. Ed. 2013, 52, 5700
30

§4 Computational Enzyme Design

Achievement – Retro-Aldolases
The first example of de novo computational enzyme design is retro-aldolase

Jiang, L. Science, 2008, 319, 1387
31

§4 Computational Enzyme Design

Achievement – Retro-Aldolases
Theozyme design

30 design showed detectable activity
kcat/KM = 0.02 – 0.74M-1s-1
Enhancement of rate is inferior tothat of catalytic antibodies.
Low activity can be attributed to dynamic distortion
such as solvation, conformational flexibility.

Jiang, L. Science, 2008, 319, 1387
32

§4 Computational Enzyme DesignAchievement – Diels-Alderase

Designing enzyme that catalyze bimolecular bond-forming reaction is challenging.
(Both substrate must be bound in the proper relative orientation.)In this design, hydrogen bond donor & acceptor modulate molecular orbital energies
and stabilize TS to accelerate the intermolecular Diels-Alder reaction.
Siegel, J. B., et al. Science, 2010, 329, 309
33§4 Computational Enzyme Design

QM calculations

Model substrates

Quantum mechanical simulation predict that these hydrogen bonds canstabilize the TS by 4.7kcal/mol.
Siegel, J. B., et al. Science, 2010, 329, 309
34§4 Computational Enzyme Design

Design and screening
84 designs were synthesized through QM calculation, RosettaMatch, and RosettaDesign.
Two of them were found to have Diels-Alderase activity. (DA_20_10 and DA_42_00)

Then, these Diels-Alderase were evolved
to further improved the activity Hydrogen bondSince initial activity is promising,directed evolution can be effectively
combined with computational enzyme design.
Structure of DA_20_10

DA_20_10(100-fold active), DA_42_04(20-fold active)

Siegel, J. B., et al. Science, 2010, 329, 309
35

§4 Computational Enzyme Design

Specificity and selectivity

Specificity switched.

In addition to high substrate specificity and stereoselectivity,
once an initial active enzyme is engineered, it can be easily modified to catalyze
similar reactions with alternate substrate.
36

§4 Computational Enzyme Design

Challenges in enzyme design
There is much room for improvement in computational enzyme design.
• Only two of 50 designs exhibited detectable activity (low success rate)• Low activity relative to natural enzymes• The resulting microenvironments are still far from “finely tuned”

37§4 Computational Enzyme Design

Design through Crowd Sourcing

38§4 Computational Enzyme Design

Structure prediction with Foldit

http://fold.it/portal/39§4 Computational Enzyme Design

About Foldit
Foldit is a multiplayer online game developed on the hypothesis that problem ofprotein structure prediction can be solved with human directed computing.
The goal of Foldit is producing accurate protein structure models through gameplay.

stochastic
Foldithuman decision making

deterministic
Rosetta algorithms

40

§4 Computational Enzyme Design

User interface
Tools and visualizations make the game approachable for non-scientists.

1. Interatomic repulsion
2. Hydrogen bond
3. Exposed hydrophobic side chain
4. Hydrophilic side chain
5. High residue energy
6. User tool “rubber bands”
7. User tool “freezing”
8. Score
9. Leader board
10. Tools and options
11. Chat window
12. “Cookbook”

Cooper, S., et al. Nature, 2010, 466, 756
41

§4 Computational Enzyme Design

Background of players

Cooper, S., et al. Nature, 2010, 466, 756

Only a minority have advanced knowledge of biochemistry42§4 Computational Enzyme Design

Social evolution of “recipes”

Khatib, F., et al. PNAS, 2011, 108, 18949

Foldit recipes are used with very different frequencies.
Popular recipes are copied and modified more often to evolve.
Thus, popular recipes spawn large numbers of descendants,
and there are multiple independent lineages each spanning many generations.
43§4 Computational Enzyme Design

Strand-swap puzzle

Rosetta protocol
Foldit player

Foldit player solution
NMR modelStarting puzzle

Strand-swap

Rosetta couldn’t reach swapped conformations

Humans significantly outperformed computers!
Cooper, S., et al. Nature, 2010, 756
44§4 Computational Enzyme Design

Key difference between humans and computers

Cooper, S., et al. Nature, 2010, 466, 756

The player went through high energy conformations to reach the native state.

Deterministic minimize function has disadvantage of being trapped at local minima.
45

§4 Computational Enzyme Design

Crystal structure solved by Foldit
Crystal structure of M-PMV retroviral protease had been elusive for over a decade(Despite the availability of crystals, wide range of attempts were unsuccessful.)
Foldit players solved this long-standing protein crystal structure problem

crystal structure (later determined)
inaccurate NMR model
spvincent’s model
grabhorn’s model
mimi’s model

Khatib, F., et al. Nat. Struct. Mol. Biol. 2011, 18, 1175
46

§4 Computational Enzyme Design

Enzyme remodeling through Foldit
Computationally designed Diels-Alderase, DA2010 has open active site,leaving substrate exposed to solvent.
To improve activity, Foldit players were enlisted to remodel loops.
Players were allowed to insertions and deletions.

“Cover the Ligand”
Remodel any of four active site loops

open active site

After a week
4 of 69773 designs made particularly favorable interactions
Library screening
Most of variants exhibited no activity.
CE0(based on design A) showed activity 10-fold decrease relative to DA2010
Eiben, C., et al. Nat. Biotechnol. 2012, 30, 19047

§4 Computational Enzyme Design

Enzyme remodeling through Foldit
CE0 (suboptimal interaction with the substrate or transition state)
Exploring design further
CE4 was 9-fold more active than DA2010“Back Me Up”
Transform a loop into additional helix
to stabilize initially designed helix
After a week
Based the top design of 109,421, library was created to provide CE6CE6 has helix-turn-helix motif, and 18-fold more active than DA2010

Helix-turn-helix motif

Eiben, C., et al. Nat. Biotechnol. 2012, 30, 19048

§4 Computational Enzyme DesignEnzyme remodeling through Foldit
Enhanced Diels-Alderase CE6 with community-designed helix-turn-helix motif“Quick Loop”
Predict the structure of insertions
After a week
The lowest energy of 335,697 solutions was selected as predicted structure

Structure solved

Predicted structure
Crystal structure

Eiben, C., et al. Nat. Biotechnol. 2012, 30, 190
49

§4 Computational Enzyme Design

Enzyme remodeling through Foldit
Workflow of design process

Foldit puzzle
Designed enzyme

Eiben, C., et al. Nat. Biotechnol. 2012, 30, 190
50

§4 Computational Enzyme Design

Enzyme remodeling through Foldit
Substrate specificity

Specificity for hydrophilic dienopiles increased
However, specificity for similar-size hydrophobic dienophiles was lost
Further improvement remain possible51§4 Computational Enzyme Design

Human insight and creativity can be extended to molecular-scale design problems!

52

§4 Computational Enzyme Design

Enzyme therapy
Computational design of α-gliadin peptidase as a therapeutic for celiac disease

Rosetta Molecular Modeling Suite

KumaWT••••

KumaMax

Active in stomach after a meal (pH 2-4)
Resistant to common digestive protease
Facile recombinant production
Specificity for PQ motif
Gordon, S. R. J. Am. Chem. Soc. 2012, 134, 20513
53

Contents
1. Introduction
2. Catalytic Antibodies
3. Directed Evolution
4. Computational Enzyme Design
5. Summary & Future Outlook54§6 Summary & Future Outlook

Summary

Computational design and directed evolution enanced KM value comparable to natural enzymes.
However, in terms of kcat/kuncat value, even the most active designed enzyme have rates comparable
only with the least proficient of natural enzyme. Further enhancement is still required.
55

§6 Summary & Future Outlook

Future Outlook
Computatinal enzyme design holds promise forthe production of renewable fuels,drugs, therapeutics.
Computatinalenzyme
designbiofuelmedicine

Enzyme therapy

Protein engineering is one of the most dynamically developing scientific fields.
Efforts to engineer truly enzyme-like proteins are just getting started!
56

Appendix

Catalytic Antibodies
Aldol reaction

Claisen rearrangement
haptenkcat = 0.045sec-1
KM = 260μM
kcat/KM = 170
kcat/kuncat = 104

Wagner, J. Science, 1995, 270, 1797

Schultz, P. Science, 1988, 240, 426

Appendix

Directed evolution

Today, numerous experimental methods
are available in directed evolution process.

Hilvert, D. Annu. Rev. Biophys. 2008, 37, 153

Appendix

Directed evolution
Protein sequence activity relationships (ProSAR)

Variants are categorized into four classes.
Neutral and Deleterous are discarded.
Beneficial variants will be parental enzyme
in the next round.
Potentially beneficial variants are retested.

Fox, R. J. Nat. Biotechnol. 2007, 25, 338

Appendix

Directed evolution
Gene shuffling
degenerate oligonucleotide gene shuffling(DOGS)

Bergquist, P.L. Biomol. Eng. 2005, 22, 63