Enzymatic Catalysis Group, PMC Advanced Technology

Enzymatic Catalysis Group, PMC Advanced Technology

Enzyme Engineering Research & Technology Development

Overview of objectives

Quantitative understanding of enzyme evolution (academic publications)

• explaining origin of natural active site sequence distributions (benchmarking on MSA and pdb data)

Redesign enzyme active sites (designer enzyme products) • modify substrate selectivity, product inhibition, etc• for industrial biocatalysis, biotechnology and biotherapeutics (with experiment)

To advance the state-of-the-art in enzyme design technology (design software) • through the application of high-resolution physics-based methods for active site modeling using:

1) High-res protein structure prediction (OPLS + SGB): loop prediction for reshaping active sites, side chain optimization

2) Semiempirical enzyme-substrate binding affinity scoring (Km), substrate pose sampling

3) Refinement based on details of electronic structure: scoring activation energies (kcat)

Quantum chemical

sequence optimization

Ab initio loop

prediction

Experimental

sampling

Classical sequence

optimization

Core design

Schematic of computational enzyme design technology

Software Patents

Design ProtocolPatents

Input information

Target chemical

Desired raw material

Existing synthetic pathways

Existing biocatalysts

Zymzyne™ Computational Design Process

System Output

~1000 potential candidatesexpected catalytic activity

Zymzyne™ Experimental Optimization

Optimized Biocatalyst

Design Computationally Refine Experimentally

1030 candidates screened 500 candidates screened

Zymzyne Enzyme Design and Optimization Platform

Software Patents

Design ProtocolPatents

A model fitness measure for enzyme sequence optimizationA model fitness measure for enzyme sequence optimization

• Maximize free energy of substrate binding over sequence space

Represent catalysis through constraints on interatomic distances of catalytic side chains

• Minimize total energy of complex for any sequence

• To start, omit selection pressure for product release

• Maximize free energy of substrate binding over sequence space

Represent catalysis through constraints on interatomic distances of catalytic side chains

• Minimize total energy of complex for any sequence

• To start, omit selection pressure for product release

substrate binding catalysis product release

Active site sequence optimization requires accurate energy functions, solvation models, and search algorithms

10o resolution rotamer library (297 proteins)

Xiang, Z. and Honig, B. (2001) J. Mol. Biol. 311: 421-430.



Ghosh, A., Rapp, C.S. & Friesner, R.A. (1998) J. Phys Chem. B 102, 10983-10990.


S-GB continuum solvation



Ghosh, A., Rapp, C.S. & Friesner, R.A. (1998) J. Phys Chem. B 102, 10983-10990.


Friesner, R.A, Banks, J.L., Murphy, R.B., Halgren, T.A. et al. (2004) J. Med. Chem. 47, 1739-1749.Jacobson, M.P., Kaminski, G.A. Rapp, C.S. & Friesner, R.A. (2002) J. Phys. Chem. B 106, 11673-11680.

S-GB continuum solvation

OPLS-AA molecular mechanics force field + Glidescore semiempirical binding affinity scoring function

φ,ψ = the backbone torsion angles

Backbone = the sequence of (COOH)-[N-(CH-Ri)-(C=O)]

N-NH

2 , where R

i is the i'th side

chain.

2N torsion angles specify the backbone configuration.

Side-chains have their own rotamers too!These angles are represented by χ

i.

Some side chains have no χ angles.Some have quite a few, such as the lysine above with χ

1-χ

4.

Streptavidin Native –10.04 kcal/mol

Computational sequence optimization correctly predicts most residues in ligand-binding sites…

Chakrabarti, R., Klibanov, A.M. and Friesner, R.A. Computational prediction of native protein ligand-binding and enzyme active site sequences. PNAS, 2005.

Streptavidin Native –10.04 kcal/mol

Computational sequence optimization correctly predicts most residues in ligand-binding sites…

9 / 10 residues predicted correctly in top 0.5 kcal/mol of sequences

Chakrabarti, R., Klibanov, A.M. and Friesner, R.A. Computational prediction of native protein ligand-binding and enzyme active site sequences. PNAS, 2005.

Easy to exptly screen libraries of this size

CO2- is covalent attachment site

for biomolecules

R61 DD-peptidase Native –10.02 kcal/mol

…and enzyme active sites

R61 DD-peptidase Native –10.02 kcal/mol

High MSA variability

…and enzyme active sites

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T123 highly degenerate in multiple sequence alignment

-galactosidase Native –9.13 kcal/mol

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• Native amino acid is generally one of top 3 most frequently predicted

• Could be used to focus combinatorial libraries (3N vs 20N, N = # of residues)

Computed

Computational enzyme sequence optimization: sugar catalysis

Glucose-binding protein Native –8.81 kcal/mol

Computed amino acid distributions contain detailed evolutionary information


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• Computed residue frequencies often mirror natural frequencies

OH

OH

Anomeric promiscuityEpimeric promiscuity

R61 DD-peptidase 0

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Summary of recent results: classical sequence optimization (Side chain prediction/ Binding affinity calculation / Sequence opt)

T123 highly degenerate in multiple sequence alignment

Nucleophile Ser62

Acid/baseY159

Electrostatic stabilizerLys65

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Phe120 Asn161 Trp233 Arg285 Thr299 Ser326 Ser62 Lys65 Tyr159

Rm

sd to

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(A)


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• Computed residue frequencies often mirror natural frequencies

OH

OH

Anomeric promiscuityEpimeric promiscuity

High-resolution sequence optimization is robust across diverse functional families

Peptide

Nucleotide

Sugar

Active Site Design of Enzymes with Nucleotide Substrates: Cytidine Kinase

Multisubstrate enzyme active site sequences represent superpositions of computational predictions

dTMP

HSV-1 thymidine kinase


dTMP

Ganciclovir (dG analog)


dTMP


Thymidine

Apply multiobjectivesequence search algorithms to accommodateseveral substrates

Native sequence =superposition of optimal sequences for multiple

substrates


dTMP


Thymidine

Catalytic hydrogen-bonding networks can be incorporated into sequence optimization

GLU 272

TYR 150

ASN 152GLN 120

ARG 148

GLU 272

LYS 315

Cephalothin

LYS 67

W402

a

b c

dSER 62

e

f

g

h

-Lactamase : cephalothin

TYR 150

ASN 152GLN 120

ARG 148

GLU 272

Cephalothin

LYS 67

W402

a

b c

dSER 62

e

f

g

h

Chakrabarti, R., Klibanov, A.M. and Friesner, R.A. Sequence optimization and designability of enzyme active sites. PNAS, 2005.

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119 120 152 221 293 316 318 346S

ite

entr

op

y

Constrained

Constrained + Filtered

+1 kcal/mol

+2 kcal/mol

LYS 315

Catalytic hydrogen-bonding networks can be incorporated into sequence optimization

Refining the scoring function: quantum chemical transition state calculations

Enzyme kcat (s-1) KM (μM) kcat/KM (% Wild-type)

WT 150 14 100

N152S 3 7 4.3

N152D 0.12 24 0.05

N152S/Q120F 3 4.6 6.7

N152S/Q120H 20 11.4 16.3

Predicted 14.3 kcal/molMeasured 14.3 kcal/mol

Number of residues correctly predicted

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Active Site Designability: The Number of Sequences that Solve a Given Design Problem

Catalytic Nucleophile Ser62

General acid/baseY159 Electrostatic stabilizer

Lys65

Catalytic nucleophileGlu-299

General acid/baseGlu-200

DD-peptidase -gal

+ 1 kcal/mol

+2 kcal/mol

Constrained

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Patents: computational sequence optimization / experimental mutagenesis

New enzymes - Improved catalytic turnover Altered substrate selectivity

New enzymes - Improved catalytic turnover Altered substrate selectivity

3 permissible mutations identified by modeling at a target position

3 permissible mutations identified by modeling at a target position

43 mutation combinations = 64 sequence variations

43 mutation combinations = 64 sequence variations

Example of screening focused library of sequence variants

Example of screening focused library of sequence variants

3 positions subject to mutagenesis3 positions subject to mutagenesis

Synthetic gene assembly and variant library construction via DNA synthesis Synthetic gene assembly and variant

library construction via DNA synthesis

Biological selection of variant library Biological selection of variant library

Patents: algorithms in development

Protein structure Substrate binding Reactive chemistry

Active site reshaping

Loop Sidechain Glidescore Pose sampling

ClassicalSequence Optimization(fixed ligand)

ClassicalSequence Optimization(free ligand)

Calculatingmutant enzyme reaction rates

• for QM/MM refinement of enzyme design• speeding up mutant TS searches

New algorithms for side chain optimization

• scores desired loop against other low-energy excitations

QM sequence refinement

• Hierarchical pose screening• Locates global seq/struct optima for a given active site/ligand comb • Estimates “designability” of active site (fixed backbone)

Testing: Current experimental projects

•Dehalogenase-dehydrogenase redesign: arbitrary backbone reshaping to

accommodate NAD – being tested now, could also benefit from faster side chain opt

Art testing induced fit results on this system without sequence opt

• PBP/ -lactamase redesign (needed – covalent docking in Glide [XP grow] or Macromodel-prime):

helix breaking (now being done)/backbone reshaping + redocking + QM/MM sequence refinement

• Single mutation activation barrier predictions in -lactamase – currently being tested

Exptl project Methods applied Notes

Sirtuin redesign for enhanced

activity

1) Active site backbone

reshaping, multiobjective

genetic and monte carlo

sequence search

2) Selection via in vivo

complementation

3) In vitro kinetics of engineered enzymes

1) Accommodate NAD+

2) Reduce binding affinity

to NAM (reaction product) to

reduce product inhibition

Mutant activation barrier predictions

in PBP -lactamase

1) Side chain structure prediction + QM/MM activation barrier calculation

2) In vitro kinetics of

mutants: compare Kd and kcat to computed values

1) To establish foundation for computational refinement of activity

2) Basis for future work on rapid algorithms for QM refinement of enzyme design

Discussion Points

• NEB combinatorial screening protocols

• NEB DNA enzyme engineering challenge problems

• Scope for interaction:

– Technology Platform to be used by both parties?

– Engineered DNA Enzyme Products? Cosolvent-resistant polymerases?

– IP: Software, Designability-Based Screening Protocols (compare Maxygen, Diversa), and Engineered Enzymes

Sirtuin – mutant production, selection, protein expression and enzyme assay

Main objective - Develop genetic and biochemical assay systems to screen sirtuin mutant library and quantify enzymatic activities.

Main steps -

Model mutations in the active site residues of bacterial sir2Tm. Generate a set of mutations using wild-type sirtuin as template based on computation-guided structural modeling.Transform the mutants into host strains with sirtuin deletion. Assay growth of mutant transformants under carbon source limitation. Select mutant constructs which can complement the growth defects resulted from sirtuin deficiency, which are manifested under carbon limitation. Purify the wild-type and active mutant enzymes and quantify their kinetic properties.

Sirtuin – mutant generation

Model mutations in the active sites of sirtuin genes by computational analysis.

Construct mutations in the wild-type sir2Tm plasmid (2 potential methods) By synthetic gene method – Generate sequence map for proposed nucleotide changes in the wild-type template (sir2Tm) . Work with gene synthesis groups to make synthetic constructs for the mutant collection, e.g. how to get efficient oligo assembly to cover all the mutations. Obtain suitable plasmid vectors and clone the mutant constructs into the vectors. The vectors would depend on the host cells in which the mutant constructs would be expressed and selected, e.g. yeast, salmonella have different vectors to allow high-level expression.

By multi-site directed mutagenesis method – Use reagents including cells, enzymes and mutagenic primers to generate mutation in the wild-type sirTm template. Verify mutations by DNA sequencing.

Both procedures for mutagenesis depend on the actual mutations to be made and how many constructs are needed to allow for effective functional screening.

Sirtuin – mutant library screening assay

When the mutant collection is generated, transform the constructs into host cell with sirtuin deletion. Make competent cells for the host strain so they can take up DNA. Transform the wild-type plasmid into host as positive control. Transform the mutant plasmids into host cells.Assay whether the transformants could grow on carbon-limited media, such as with acetate or propionate as sources. If there is complementation, characterize the growth features of these cells. Verify the specific mutations by DNA sequencing. Transform the mutant construct into protein-expression host, such as Ecoli BL21. Grow cultures and purify sufficient quantities of proteins. Set up enzymatic assays to quantify kinetic properties of wild-type and selected mutants.

Beta-lactamase – mutant selection, protein expression and activity assay

Model mutations in the active site residues of P99 beta-lactamase.

Construct mutations in the wild-type P99 beta-lactamase gene.

Obtain bacterial host strains suitable for screening beta-lactam antibiotic resistance.

Transform bacteria host cells with wild-type and mutant constructs.

Select transformed cells in the presence of beta-lactam antibiotics.

Identify the mutant clones which can grow in beta-lactam and thus retain beta-lactamase activities.

Express and purify the wild-type and mutant beta-lactamases and quantify their kinetic properties.

Beta-lactamase – mutant generation

Model mutations in the active site of P99 beta-lactamase based on computation.

Construct mutations in the wild-type P99 beta-lactamase plasmid. The actual processes would depend on what the mutations are and how many mutants are to be made.

By synthetic gene method – Work with gene synthesis group to construct synthetic constructs, esp. in how to set up efficient oligonucleotides coverage for all the mutations. Clone all mutant constructs into suitable bacterial expression vector.

By multi-site directed mutagenesis method - Need to obtain mutagenic reagents such as cells, enzymes and primers to generate a set of mutations. Verify mutant production by DNA sequencing of individual clones.

Beta-lactamase – mutant selection

With the bacterial host strains used for selection, make competent cells so that they can take up plasmid DNA.

Transform wild-type P99 beta-lactamase plasmid into host cells as positive control. Transform the mutant plasmids into host cells to select for active constructs.

Make agar plates containing different types of beta-lactam compounds and at different concentration.

Grow bacteria transformed with beta-lactamase plasmids on these plates and monitor colony formation.

Identify the clones with good growth characteristics so they would be the candidates to provide hydrolytic activities on a variety of beta-lactam substrates.

Verify specific mutations by DNA sequencing.

Proceed to protein expression, purification and activity quantitation.

A General Framework for Computationally Directed Biocatalyst DesignA General Framework for Computationally Directed Biocatalyst Design

Catalytic constraint: interatomic distances rij < hbond dist

Catalytic constraint: interatomic distances rij < hbond dist Enzyme-substrate

binding affinity

Enzyme-substratebinding affinity

• Minimize J over sequence space

• Represent dynamical constraint with requirement that total energy of complex minimized for any sequence

• Omits selection pressure for product release

• Minimize J over sequence space

• Represent dynamical constraint with requirement that total energy of complex minimized for any sequence

• Omits selection pressure for product release

slack variableslack variable

1

1 1

2hbond,

N

i

N

jijijijijbind seqrrseqGseqJ

Assessment of active site designability

Need to assess number of sequences that are structurally similar to native

Requires sampling over ligand conformations

1

, hbond10

1 1ln (seq) (seq) (seq)

N N

bind bind opt ijj i i ij

S Z G G r rT T

Two approaches:

Marginal distributions (as shown) using top m (m constant) as shown or setting m_i according to exp(shannon entropy). Choose T based on exptl tractability. Assumes independence, but easier for exptlst to implement out-of-box. Note S in this case cannot be interpreted as number of microstates since LLN does not hold

b) Joint distribution: sample m sequences from joint distribution for specified T’s. S computed based on moments of objectives. Compare D=exp(S) for several T’s, look for transition to region where denser sampling possible (heat capacity analogy). LLN holds, allowing interpretation of designability as relative number of microstates

Computationally directed active site sequence library generation

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Shannon sequence entropy: Si = - (a=1...20) [f(ia) ln f(ia)]

Computed sequence entropies suggest equilibrium in sequence space

Penicillium sp. -galactosidase

Computed Observed

Catalytic constraint

Comparable Shannon site entropies suggest equilibrium for same fitness measure and provide concise comparison of distributions at all positions (rather than showing pdf at each position)

Marginal active site sequence distributions

Shannon site entropies: Computed based on marginal distributions; unlike joint cannot be expressed in closed form in terms of exp fn. Two approaches to estimating distribution – a) in terms of marginal moments of functions of f_i’s; b) in terms of explicit f_i’s (used here). Both based on drawing m samples from joint

Extensions/modifications to PNAS paper figures:

Better to display K-L relative entropies rather than site entropies for marginal distributions at each position

Instead compare K-L relative entropies (joint distribution) wrt MSAs for models w different objectives, on same plot;

alternatively use approach based on marginal distributions on Shannon entropy slide

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For such figures, compare K-L rel entropies (here marginal)

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Plan for development of designability theory and experimental application (to be described in conclusion of our early papers)

Apply designability theory to all major enzyme families from PNAS papers; extend to designability of modified sirtuins experimentally

Could id the catalytic constraints and focus on objective for reducing inhibition (NAM binding affinity); estimate latter temperature.

Compare designability of NAD site to that of other enzyme classes studied, for same T’s. Check designability at lower T for NAM inhibition

Designability approach will help determine viability of drug development efforts more effectively than comb chem

http://tinyurl.com/63gt3lm

Components of energy function

Covalent bond potential

Non-bonding terms (Van Der Waals)

Torsional potential

H-bonding (sometimes)

Electrostatic potential

Surface-area term

H2OH2OH2O

H2OH2O

H2O

H2OH2OH2O

H2OH2O

H2O

H2OH2OH2O

H2OH2O

H2O

H2OH2OH2O

H2OH2O

H2O

H2OH2OH2OH2OH2O

H2OH2O

H2OH2OH2OH2OH2OH2O

H2OH2O

H2O

The effect of water(a rude fellow!)

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Phe120 Asn161 Trp233 Arg285 Thr299 Ser326 Ser62 Lys65 Tyr159

Rm

sd t

o n

ativ

e (A

)

Computational active site optimization is structurally accurate to near-crystallographic resolution

Future plans

• Understanding differences between PLOP/Glide/Qsite energies

for summing energy calcs to calculate Km, kcat

• Modeling the denatured state of proteins to estimate folding free energy for core sequence optimization

Integration with other current developments

• Induced fit + Backbone reshaping to start with globally-relaxed backbone shapes for unnatural ligands

• MD treatment of loops + Backbone reshaping + Classical affinity opt for antibody engineering

Enzymatic Catalysis Group, PMC Advanced Technology

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