Download PMC-AT Enzyme Engineering Research Overview.

Enzyme Engineering Research & Technology Development
Enzymatic Catalysis Group, PMC Advanced Technology
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)
Schematic of computational enzyme design technology
Core design
Ab initio loop
prediction
Software
Patents
Classical sequence
optimization
Quantum chemical
sequence optimization
Experimental
sampling
Design
Protocol
Patents
Zymzyne Enzyme Design and Optimization Platform
Design Computationally
Input information
System Output
Target chemical
Desired raw
material
Refine Experimentally
Zymzyne™
Computational
Design Process
~1000 potential
candidates
expected catalytic
activity
Zymzyne™
Experimental
Optimization
Optimized
Biocatalyst
Existing synthetic
pathways
1030 candidates screened
500 candidates screened
Existing biocatalysts
Software
Patents
Design
Protocol
Patents
A model fitness measure for enzyme sequence optimization
substrate binding 
catalysis  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
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.
Active site sequence optimization requires accurate energy functions,
solvation models, and search algorithms
S-GB continuum solvation
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.
Active site sequence optimization requires accurate energy functions,
solvation models, and search algorithms
S-GB continuum solvation
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.
OPLS-AA molecular mechanics force field + Glidescore semiempirical binding affinity scoring function
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.
φ,ψ = the backbone torsion angles
Backbone = the sequence of (COOH)-[N-(CH-Ri)-(C=O)]N-NH2 , where Ri 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 χi.
Some side chains have no χ angles.
Some have quite a few, such as the lysine above with χ1-χ4.
Computational sequence optimization correctly predicts most residues in
ligand-binding sites…
Streptavidin
kcal/mol
Native –10.04
Chakrabarti, R., Klibanov, A.M. and Friesner, R.A. Computational prediction of native protein ligand-binding and enzyme
active site sequences. PNAS, 2005.
Computational sequence optimization correctly predicts most residues in
ligand-binding sites…
Streptavidin
kcal/mol
Native –10.04
Chakrabarti, R., Klibanov, A.M. and Friesner, R.A. Computational prediction of native protein ligand-binding and enzyme
active site sequences. PNAS, 2005.
Computational sequence optimization correctly predicts most residues in
ligand-binding sites…
Streptavidin
kcal/mol
Native –10.04
CO2- is covalent attachment site
for biomolecules
9 / 10 residues predicted correctly in top 0.5 kcal/mol of sequences
Easy to exptly screen
libraries of this size
Chakrabarti, R., Klibanov, A.M. and Friesner, R.A. Computational prediction of native protein ligand-binding and enzyme
active site sequences. PNAS, 2005.
…and enzyme active sites
R61 DD-peptidase
kcal/mol
Native –10.02
…and enzyme active sites
R61 DD-peptidase
kcal/mol
Native –10.02
0.18
0.16
0.14
High MSA
variability
0.12
0.1
0.08
0.06
0.04
0.02
0
D A F
R S Q E Y
H I
L K N G T WV M
T123 highly degenerate in
multiple sequence alignment
Computational enzyme sequence optimization: sugar catalysis
b-galactosidase
kcal/mol
Native –9.13
1
0.8
Computed
0.6
0.4
0.2
0
D A F R S Q E Y H I L K N G T WV M
• 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 amino acid distributions contain
detailed evolutionary information
Glucose-binding protein
kcal/mol
Native –8.81
Computed amino acid distributions contain
detailed evolutionary information
0.6
Glucose-binding protein
Native –8.81
0.5
Observed
(sequence alignment)
Frequency
kcal/mol
0.4
0.3
0.2
0.1
0
DA F R S Q E Y H I L K N G T WV M
Computed amino acid distributions contain
detailed evolutionary information
0.6
Observed
(sequence alignment)
0.5
Native –8.81
Glucose-binding protein
Frequency
kcal/mol
0.4
0.3
0.2
0.1
0
D A F R S Q E Y H I L K N G T WV M
Epimeric
promiscuity
Anomeric promiscuity
0.6
0.5
Computed
Frequency
0.4
OH
0.3
0.2
0.1
OH
0
D A F R S Q E Y H I
L K N G T W V M
• Computed residue frequencies often mirror
natural frequencies
Summary of recent results: classical sequence optimization
(Side chain prediction/ Binding affinity calculation / Sequence opt)
Acid/base
Y159
Electrostatic stabilizer
Lys65
Nucleophile
Ser62
T123 highly degenerate in
multiple sequence alignment
0.18
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
R61 DD-peptidase
0
D A F R S Q E Y H I L K N G T WV M
Rmsd to native (A)
1.2
1
0.8
0.6
0.4
0.2
0
Phe120 Asn161 Trp233 Arg285 Thr299 Ser326
Ser62
Lys65
Tyr159
Computed amino acid distributions contain
detailed evolutionary information
0.6
Native –8.81
Glucose-binding protein
0.5
Observed
(sequence alignment)
Frequency
kcal/mol
0.4
0.3
0.2
0.1
0
Epimeric
promiscuity
Anomeric promiscuity
0.6
DA F R S Q E Y H I L K N G T WV M
0.5
Computed
Frequency
0.4
OH
0.3
0.2
0.1
OH
0
D A F R S Q E Y H I
L K N G T W V M
• Computed residue frequencies often mirror
natural frequencies
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
Multisubstrate enzyme active site sequences
represent superpositions of computational predictions
dTMP
Ganciclovir
(dG analog)
Multisubstrate enzyme active site sequences
represent superpositions of computational predictions
dTMP
Ganciclovir
(dG analog)
Thymidine
Apply multiobjective
sequence search
algorithms to
accommodate
several substrates
Multisubstrate enzyme active site sequences
represent superpositions of computational predictions
dTMP
Ganciclovir
(dG analog)
Native sequence =
superposition of optimal
sequences for multiple
substrates
Thymidine
Catalytic hydrogen-bonding networks can be incorporated
into sequence optimization
GLU 272
LYS 315
Cephalothin
W402
d
b-Lactamase : cephalothin
b
GLU 272
SER 62
c
e
g
a
f
LYS 67
h
TYR 150
ARG 148
ASN 152
GLN 120
Catalytic hydrogen-bonding networks can be incorporated into sequence
optimization
2.5
+2 kcal/mol
+1 kcal/mol
Constrained
Constrained + Filtered
Site entropy
2
1.5
1
0.5
0
119
120
152
221
293
316
318
346
LYS 315
Cephalothin
W402
d
b
GLU 272
SER 62
c
e
g
a
f
LYS 67
h
TYR 150
ARG 148
ASN 152
GLN 120
Chakrabarti, R., Klibanov, A.M. and Friesner, R.A. Sequence optimization and designability of enzyme active sites.
PNAS, 2005.
Refining the scoring function: quantum chemical transition state calculations
Enzyme
WT
kcat (s-1)
KM (μM)
kcat/KM (% Wild-type)
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/mol
Measured 14.3 kcal/mol
Active Site Designability: The Number of Sequences that Solve a Given Design
Problem
0.45
Fraction of total
sequences
0.4
+2 kcal/mol
0.35
0.3
+ 1 kcal/mol
0.25
0.2
0.15
Constrained
0.1
0.05
0
0
1
2
3
4
5
6
0 1
7
2
3 4
5
6 7
8
9 10
Number of residues correctly predicted
General acid/base
Y159
Electrostatic stabilizer
Lys65
Catalytic nucleophile
Glu-299
Catalytic
Nucleophile Ser62
DD-peptidase
General acid/base
Glu-200
b-gal
Patents: computational sequence optimization / experimental mutagenesis
Example of screening focused
library of sequence variants
3 permissible mutations identified by
modeling at a target position
3 positions subject to mutagenesis
43 mutation combinations
= 64 sequence variations
Synthetic gene assembly and variant
library construction via DNA synthesis
0.4
0.7
0.35
0.6
0.3
0.5
0.25
0.3
0.35
0.25
0.4
0.2
0.15
0.3
0.15
0.1
0.2
0.1
0.05
0.1
0.05
0.2
0
D A
F
R
S
N
E Y
H
I
L
K
N G
T W V
C
0
Biological selection of variant library
0
D A F R S N E Y H I L K N G T W V C
D A F R S N E Y H I L K N G T W V
New enzymes Improved catalytic turnover
Altered substrate selectivity
Patents: algorithms in development
Protein structure
Loop
New algorithms
for side chain
optimization
Sidechain
Substrate binding
Glidescore
Pose sampling
QM sequence refinement
Classical
Sequence
Optimization
(fixed ligand)
Active site reshaping
• scores desired loop
against other low-energy
excitations
Reactive chemistry
• for QM/MM refinement
Calculating
mutant enzyme of enzyme design
• speeding up mutant
reaction rates
TS searches
Classical
Sequence
Optimization
(free ligand)
• 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
Exptl project
Methods applied
Notes
1) Accommodate NAD+
Sirtuin redesign for
enhanced
activity
1) Active site backbone
reshaping, multiobjective
genetic and monte carlo
sequence search
2) Reduce binding affinity
to NAM (reaction product) to
reduce product inhibition
2) Selection via in vivo
complementation
3) In vitro kinetics of
engineered enzymes
Mutant activation barrier
predictions
in PBP b-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 highlevel 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 Design
slack variable
N 1 N

J seq   Gbind seq    ij rij,hbond  rij seq    ij2

i 1 j 1
Enzyme-substrate
binding affinity
Catalytic constraint: interatomic
distances rij < hbond dist
• 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
Assessment of active site designability
 Need to assess number of sequences that are structurally similar to native
 Requires sampling over ligand conformations
N N 1
1
1
S  ln Z (seq) 
Gbind (seq)  Gbind ,opt  
rij (seq)  rhbond
T0
j i i 1 Tij
Computationally directed active site sequence library generation
Two approaches:
0.4
0.35
0.3
0.25
0.2
0.15
 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
0.1
0.05
0
D A
F
R
S
N
E Y
H
I
L
K
N G
T W V
C
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
D A F R S N E Y H I L K N G T W V C
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
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
D A F R S N E Y H I L K N G T W V
Computed sequence entropies suggest equilibrium in sequence space
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)
Shannon sequence entropy: Si = - S(a=1...20) [f(ia) ln f(ia)]
Computed
Catalytic constraint
Observed
Penicillium sp. b-galactosidase
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
0 1
2
3 4
5
6 7
8
9 10
0.6
Frequency
0.5
0.4
0.3
For such figures, compare K-L rel entropies (here marginal)
0.2
0.1
0
DA F R S Q E Y H I L K N G T WV M
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
Components of energy function
Surface-area term
Covalent bond
potential
Torsional potential
H-bonding
(sometimes)
H2OH2O
H2H
OO
HO22HO
O2H
HO22OH2O
H
O
H
H22O 2H HOHOOH2OH2O
2 H
H22O2 H
OHOHOO
H2O 2H22O2 2
H
H2O
OH2O
2
Non-bonding terms
(Van Der Waals)
The effect of water
(a rude fellow!)
Electrostatic potential
http://tinyurl.com/63gt3lm
Computational active site optimization is structurally accurate
to near-crystallographic resolution
Rmsd to native (A)
1.2
1
0.8
0.6
0.4
0.2
0
Phe120 Asn161 Trp233 Arg285 Thr299
Ser326
Ser62
Lys65
Tyr159
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