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Protein and Metabolic Engineering
Huimin Zhao
8/31/2005
Suggested readings: handouts
1
Building Blocks of Biotechnology
Enabling Components
• Recombinant DNA Technology (1973)
• DNA Sequencing (1977, 1987)
• Monoclonal Antibodies (1977)
• Site-directed Mutagenesis (1982)
Applications • Polymerase Chain Reaction (1983)
• Gene Therapy (1981,1990-)
$$$$$ • Pharmaceuticals
• Directed Molecular Evolution (1994)
• Agriculture • siRNA technology (1995)
• Stem cells (1998)
• Food
$ • Chemicals
Systems and Processes
• Expression Systems
• Metabolic Control
• Fermentor Design
• Downstream Processing
2
Approaches for Biomolecular Engineering
Commercially viable
gene products
Product Development
Functional Gap
1 2 3
Natural occurring
gene products
Time & Cost
1. Directed Molecular Evolution
2. Rational Design
3. Bioprospecting 3
Two Unsolved Fundamental Problems in
Protein Science
20 natural amino acids, assembled in the cell
according to DNA instructions
D A T F S C F
+ E A
H3N K A
N D P
G O
M M T S
A S Y V
...
Q V P C O
S I
R E
H
L T G
A S H D Y A
?
Functions
? 4
An Example of Classical Breeding
5
Directed Molecular Evolution
Target • Activity
Protein/Pathway • Stability
• Selectivity
• Activity in Solvents
• Substrate Specificity
• pH Profile
Generation of Diversity
Relative Performance
Iterative Cycles
• Cofactor Requirement
1. Random Mutagenesis
2. Gene Recombination
Screen or Selection
1 2 3 4 ……
Goal Achieved
Generations
Molecular Breeding vs. Classical Breeding
Classical Breeding Molecular Breeding
• Cycle time = years • Cycle time = days
• Often two at a time • Unlimited at a time
• Not applicable to microbes • Applicable to microbes
• Evolve whole genome • Evolve partial genome
• Not focus on specific genes • Focus on specific genes
Slow evolution of whole Rapid evolution of genes,
eukaryotic genomes Pathways, operons, viruses
Directed Evolution: Diversity Generation Methods
Random Mutagenesis: mutations are randomly introduced into the
progeny genes
parent gene
8
Directed Evolution: Diversity Generation Methods
e.g. Error-prone PCR
x
x
x
x
x x
x x
• Taq polymerase (low fidelity, 10-5 errors per bp)
• unbalanced dNTP concentrations
• add MnCl2 (vs. MgCl2)
Limitations:
• single nucleotide substitutions 5.7 a.a. per residue position
9
Directed Evolution: Diversity Generation Methods
Random Mutagenesis
• Error prone PCR
• Mutator Strains
• Chemical mutagens
• UV irradiation
• Random insertion/deletion mutagenesis
The distribution of mutations follows Poisson distribution
lk
pk (x=k) = e-l k! (k = 0,1,2...)
10
Directed Evolution: Diversity Generation Methods
Gene Recombination e.g.
• DNA shuffling
• StEP
• RACHITT
pool of genes • CLERGY
• SHIPREC
• ITCHY
• THIO-ITCHY
• Exon Shuffling
• Degenerate homoduplex recombination
• Synthetic shuffling
Key advantage: accumulate beneficial mutations while removing
deleterious mutations at the same time
11
In vitro Recombination by DNA Shuffling
Random Fragmentation (DNase I
or sonication)
Fragments Reassembly
Cycle 1
Denature
Anneal
Prime
Extend
Further Cyclec
Chimera 12
Stemmer, W.P.C. PNAS, 91, 1994.
In vitro Recombination by Staggered
Extension Process (StEP)
1. Short fragments generated by primer
extension along template strands
2. After denaturation, fragments re-anneal
randomly to templates and re-extend
3. Repeat denaturation and extension to
make full-length genes
13
Zhao, H. et al. Nature Biotechnology, 16, 1998.
Directed Evolution: Library Selection
or Screening Methods
“Find a needle in a hay stack”
A. Selection: link the protein of interest to the growth or survival
of the host organism
Target gene
cell Cells w/o target gene
cell
cell
cell Cells w/ target gene
cell
cell
14
Directed Evolution: Library Selection
or Screening Methods
e.g. cloning vector
amp b-lactamase (hydrolyze ampicillin)
Ampicillin: inhibits several enzymes in the cell
MCS wall synthesis
Ori
Pros:
• efficient, >106 library size (limited by DNA transformation efficiency)
Cons:
• difficult to devise a selection method (the desired protein function is often
non-natural and can’t be coupled to the cell growth or survival)
• due to redundancy and complexity of genetic regulatory network, host
organisms can often create solutions that are not related to the target
protein function 15
Directed Evolution: Library Selection
or Screening Methods
“Find a needle in a hay stack”
B. Screening: each library member is assayed individually by
using biochemical or biophysical analysis
rely on:
• Chromagenic (fluorescent) substrate and product
• Petri dish
• Microtiter plates (96-well or 384-well plates)
Plate reader
(absorbance change)
16
Directed Evolution: Library Selection
or Screening Methods
“Find a needle in a hay stack”
B. Screening: each library member is assayed individually by
using biochemical or biophysical analysis
Pros:
• versatile and flexible (experimental conditions can be easily tailored to
meet a specific industrial setting such as non-natural environment or
substrates)
Cons:
• low throughput, 104~106
17
Directed Evolution Field is Rapidly Expanding
300
Number of papers published
250
200
150
100
50
0
Pre-1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Thermogen
Isogenica
Codexis
Nautilus
Diversa
Proteus
Enchira
Phylos
Avidia
Mixis
Timeline
Pfizer, Eli Lilly, BMS, Merck, Novartis, etc.
Evolva
Alligator
Maxygen
Kairos
AME
Verdia
DuPont, Dow, BASF, Bayer, DSM, Degussa, etc.
Aventis, AstraZeneca, Finnfeeds, etc. 18
Selected Successful Examples
Single genes
• b-Lactamase activity 32,000x Nature, 1994
• Human antibody binding >440x Nature Biot., 1996
• Glyphosate N-acetyl transferase activity >10,000x Science, 2004
Multiple genes
• Class C cephalosporinases (4) activity 270-540x Nature, 1998
• Subtilisins (26) multiple functions Nature Biot., 1999
• Human interferon-as (20) antiviral 285,000x Nature Biot., 1999
• Murine leukemia virus (6) stability 30-100x Nature Biot., 2000
Pathways
• Arsenate pathway (3) detoxification 12x Nature Biot., 1997
• Carotenoid biosyn. Pathway (2) new product Nature Biot., 2000
Genomes
• Streptomyces fradiae tylosin production 6x Nature, 2002
• Lactobacillus improved acid tolerance Nature Biot., 2002
Case I: Improving the Antiviral and Antiproliferation
Activity of Human a-Interferons
• Alpha interferons (IFN-as) are members of the diverse helical-bundle superfamily
of cytokine genes. Although these proteins possess therapeutic value in the
treatment of a number of diseases, they have not been optimized for use as
pharmaceuticals. For example, dose-limiting toxicity, receptor cross-reactivity, and
short serum half-lives significantly reduce the clinical utility of many of these
cytokines.
• Over 20 human alpha interferons were recombined to create a library of variants
using DNA shuffling
• A few thousands of clones were screened in 96-well plates
20
Case I: Improving the Antiviral and Antiproliferation
Activity of Human a-Interferons
Sequences and genealogies of shuffled interferons. (A) The amino acid sequences of seven evolved IFN- s
and the eight native Hu-IFN- s from which they are derived. The most parsimonious genealogies of the shuffled IFN- s
are shown schematically. Recombination junctions are shown at the midpoint between two amino acids derived from
different parental genes. The gene segments are colored according to which parental gene they are derived from (red,
Hu-IFN- 1; green, Hu-IFN- 5; yellow, Hu-IFN- 8; purple, Hu-IFN- 16; orange, Hu-IFN- 17; blue, Hu-IFN- F; gray, Hu-
IFN- H). Amino acids that arose by point mutation during DNA shuffling are circled. (B) The sequence of one of the
cycle 2 chimeras, IFN-CH2.2, is aligned with the most potent human and mouse IFN- s, Hu-IFN- 1 and Mu-IFN- 4. The
IFN- residues that putatively contact the IFN- receptor27, 28 are boxed. Residues in Hu-IFN- 1 that have been shown
by site-directed mutagenesis to contribute to activity on mouse cells7, 26-28 are shaded. 21
Case I: Improving the Antiviral and Antiproliferation
Activity of Human a-Interferons
• Summary of antiviral activities of native IFN-as and evolved IFN-as
on murine L929 cells.
22
One Limitation of Directed Evolution
n n: # of mutations
Library size = 20n Cm m: length of target protein
For a typical protein w/ 300 aa:
mutations library size organism library size
single 6x103 mammalian 105
double 1.79x107 yeast 107
triple 2.56x1010 bacteria 108
quadruple 5.29x1013
phage 109
quintuple 6.26x1016
23
Rational Design: Structural Analysis and
Bioinformatics
Structural Analysis Bioinformatics
AGGHHSWVNLDDLLLTTYAEVRARKNVVLTIGGG-IGTPAKAAHYLTGQW
AGGHHSWVNLDDLLLTTYAELRSRKNVVVMIGGG-IGTPAKAAYYLTGEW
AGGHHSWVDLDEMLLATYACAREHDNLAITVGGG-IHSPDRASEYLTGTW
AGGHHSWEALDDLLAATYAEVRACDNLVLVAGGG-IGTPERAADYISGQW
AGGHHSWEDLDDLLLATYSELRSRANITVCVGGG-IGTPRRAAEYLSGRW
AGGHHSWEDLDDLLLATYSELRSHANITVCVGGG-IGTPEKAAEYLSGRW
AG--TIPGRISHLLLATYSADRAPRQHHVCVGGGHLGTPKKGCGYLSG-P
** : : * * : : ..* : *:.*
Site-directed mutagenesis
Site-directed Mutagenesis
Mutations are site-specifically introduced into the progeny genes.
e.g.
Wild type • Oligonucleotide-directed mutagenesis
w/ M13DNA
• Kunkel method
• Overlap extension PCR (SOEing method)
• Megaprimer
• Quikchange method (Stratagene)
Mutant • Excite method (Stratagene)
• AlteredSite method (Promega)
Site-directed Mutagenesis
Overlap extension PCR based method
x
5’ 3’
3’ 5’
x
PCR #1
x PCR #2
x
x
x
x
x
Overlap extension PCR
x
x
Enzymes Used in Biocatalysis
Isomerases(±)
Ligases(±) Oxidoreductases(+++)
Lyases(++)
25%
Transferases(+)
Hydrolases(+++)
65% +++ (very useful) ± (limited use)
Source: Faber, K. Biotransformations in Organic Chemistry, 2000
Oxidoreductases:
Requires expensive redox cofactors NADH = $38 / g
80%: requires NAD(H) NAD = $22 / g
10%: requires NADP(H) NADPH = $668 / g
NADP = $165 / g 27
From Sigma Catalog 2005
Enzymatic Cofactor-Recycling Systems
Target
Enzyme A Target
Substrate Product
NAD(P)H NAD(P)+
Auxiliary Auxiliary
Product Substrate
Enzyme B
CO2 Formate dehydrogenase Formic acid (Degussa)
Gluconolactone Glucose dehydrogenase Glucose
Acetalaldehyde Alcohol dehydrogenase Ethanol
28
van der Donk & Zhao, Curr. Opin. in Biot. 14, 421 (2003)
Zhao & van der Donk, Curr. Opin. in Biot. 14, 583 (2003)
PTDH-based NAD(P)-Recycling System
O Phosphite Dehydrogenase O
(PTDH)
P P
OH - O-
HO O
H O-
Phosphite Phosphate
NAD(P)+ NAD(P)H
• Net redox potential ~ -300 mV
- essentially irreversible (Keq = 1011)
Project Goals:
- provide driving force as a cofactor regenerator
• Improve the activity of PTDH toward NADP
• Inexpensive substrate of PTDH toward NAD
• Improve the activity
• Easily removable by-product
• Improve the stability of PTDH
• Favorable kinetics: kcat (PTDH)=7.5s-1 vs. kcat(FDH)=2.5s-1
29
PTDH -------MLPKLVITHRVHDEILQLLAPHCELMTNQTDSTLTREEILRRCRDAQAMMAFM 53
1GDH --------KKKILITWPLPEAAMARARESYDVIAHGDDPKITIDEMIETAKSVDALLITL 52
1PSD
2DlD Multiple Sequence Alignment
AKVSLEKDKIKFLLVEGVHQKALESLRAAGYTNIEFHKGALDDEQLKESIRDAHFIGLRS
------MTKVFAYAIRKDEEPFLNEWKEAHKDIDVDYTDKLLTPETAKLAKGADGVVVYQ
60
54
: : : : . :... :
PTDH PDRVDADFLQACPE--LRVVGCALKGFDNFDVDACTARGVWLTFVPDLLTVPTAELAIGL 111
1GDH NEKCRKEVIDRIPEN-IKCISTYSIGFDHIDLDACKARGIKVGNAPHGVTVATAEIAMLL 111
1PSD RTHLTEDVIN-AAEK-LVAIGCFCIGTNQVDLDAAAKRGIPVFNAPFSNTRSVAELVIGE 118
2DlD QLDYTADTLQALADAGVTKMSLRNVGVDNIDMDKAKELGFQITNVPVYSPNAIAEHAAIQ 114
: :: .: : :. * ::.*:* . *. : .* . . ** .
PTDH AVGLGRHLRAADAFVRSGEFQGWQP-QFYGTGLDNATVGILGMGAIGLAMADRLQGWGAT 170
1GDH LLGSARRAGEGEKMIRTRSWPGWEPLELVGEKLDNKTLGIYGFGSIGQALAKRAQGFDMD 171 Rossman Fold
1PSD LLLLLRGVPEANAKAHRGVWNKLAAGSFEARGKK---LGIIGYGHIGTQLGILAESLGMY 175
2DlD AARVLRQDKRMDEKMAKRDLR-WAP--TIGREVRDQVVGVVGTGHIGQVFMRIMEGFGAK 171
* : . . :*: * * ** : :. .
PTDH LQYHEAKALDTQTEQR-LGLRQVACSELFASSDFILLALPLNADTQHLVNAELLALVRPG 229
1GDH IDYFDTHRASSSDEASYQATFHDSLDSLLSVSQFFSLNAPSTPETRYFFNKATIKSLPQG 231 Cofactor
1PSD VYFYDIENKLPLGNAT----QVQHLSDLLNMSDVVSLHVPENPSTKNMMGAKEISLMKPG 231 Specificity
2DlD VIAYDIFKNPELEKKG---YYVDSLDDLYKQADVISLHVPDVPANVHMINDKSIAEMKDG 228
: .: : ..* ::.. * * . . :.. : : *
PTDH ALLVNPCRGSVVDEAAVLAALERGQLGGYAADVFEMEDWARAD------RPRLIDPALLA 283
1GDH AIVVNTARGDLVDNELVVAALEAGRLAYAGFDVFAGEP--------------NINEGYYD 277
1PSD SLLINASRGTVVDIPALCDALASKHLAGAAIDVFPTEP---------ATNSDPFTSPLCE 282
2DlD VVIVNCSRGRLVDTDAVIRGLDSGKIFGFVMDTYEDEVGVFNKDWEGKEFPDKRLADLID 288 Catalytic
:::* .** :** : .* :: *.: * Residues
PTDH HPNTLFTPHIGSAVRAVRLEIERCAAQNIIQVLAGARPINAANRLPKAEPAAC------- 336
1GDH LPNTFLFPHIGSAATQAREDMAHQANDLIDALFGGADMSYALA----------------- 320
1PSD FDNVLLTPHIGGSTQEAQENIGLEVAGKLIKYSDNGSTLSAVNFPEVSLPLHGGRRLMHI 342
2DlD RPNVLVTPHTAFYTTHAVRNMVVKAFNNNLKLINGEKPDSPVALNKNKF----------- 337
*.:. ** . . . :: . . . 30
PTDH ------------------------------------------------------------
Homology Model
D-Lactate Dehydrogenase Phosphite Dehydrogenase
• Accelrys Insight II MODELER was used to create structure with
2DLD, 1PSD, and 1GDH as templates.
• Molecular Operating Environment (MOE) was used to energy
minimize structure and dock NADH 31
In silico Mutant Design
A176
WT + NAD A176 WT + NADP
2.84
3.50
E175 E175
E175A + NAD E175A + NADP A176
A176
2.90
1.34
A175 A175
32
PTDH Double Mutant
R176
3.40
E175A, A176R + NADP 3.14
2.96
1.72
A175
The double mutant has favorable interactions with NADP+ without excluding NAD+
33
Enzyme Kinetics
NAD+
Enzyme kcat / KM
KM (mM, NAD+) kcat (s-1) KM (mM, Pt-H)
(mM-1min-1)
WT 53 ± 9.0 2.93 ± 0.14 3.3 47 ± 6.0
E175A 16 ± 0.8 3.50 ± 0.05 13.1 23 ± 2.9
A176R 60 ± 7.0 4.28 ± 0.08 4.3 156 ± 60
E175A+A176R 20 ± 1.3 3.94 ± 0.08 11.8 61 ± 13
NADP+
kcat / KM
KM (mM, NAD+) kcat (s-1) KM (mM, Pt-H)
(mM-1min-1)
WT 2510 ± 410 1.41 ± 0.08 3.37E-02 1880 ± 325
E175A 144 ± 14 2.18 ± 0.07 0.91 138 ± 25
A176R 77 ± 8.4 2.18 ± 0.07 1.7 140 ± 20
E175A+A176R 3.5 ± 0.5 1.98 ± 0.08 32.5 21 ± 2.7
34
Metabolic Engineering
Targeted improvement of cellular activities by manipulation
of enzymatic, transport, and regulatory functions of the cell
with the use of rDNA technology. (Jay Bailey (1991) Science).
The directed improvement of product formation or cellular
properties through the modification of specific biochemical
reaction(s) or the introduction of new one(s) w/ the use of
recombination DNA technology. (Stephanopoulos et al Metabolic
Engineering, page 2)
35
Metabolic Engineering
Key steps in Metabolic Engineering
• Identify the reaction target
• modify (amplify, inhibit or delete, transfer, or deregulate)
the corresponding genes or enzymes
Chemical plants whose units are individual enzymes,
w/ similar issues of design, control and optimization.
36
Principles of Metabolic Engineering
DNA
Material flux Control
mRNA
Regulatory
Metabolic
Network
Network Proteins
Metabolites
37
Principles of Metabolic Engineering
RNA Proteins
Metabolites
DNA Membrane
38
Principles of Metabolic Engineering
Transcription
Translation
• Reaction
• Transport
• Regulation
• Communication
Replication
39
Principles of Metabolic Engineering
Metabolic pathway: any sequence of feasible and observable
biochemical reaction steps connecting a specified set of input and
output metabolites.
Flux: J (most critical parameter of a metabolic pathway) the rate at
which input metabolites are processed to form output metabolites.
e.g.
v1 v2 v3 vn
Linear: A B
E1 E2 E3 En
J = v1 = v2 = … = vn at steady state
B
J2
J1
Branched: A I J1 = J2 +J3
J3 C 40
Principles of Metabolic Engineering
Transcription
Translation
• Reaction
• Transport
• Regulation
• Communication
Replication
Product
41
Principles of Metabolic Engineering
Concept
Design
Metabolic Redirection
Idea
Attenuation/Amplification
Modification of Control
Synthesis Analysis
Genetic Engineering Genome, Transcriptome,
Knock-out, Amplification Proteome, Fluxome,
Product
Reaction Engineering Metabolome/control
42
Applications of Metabolic Engineering
• Industrial applications: use microorganisms to manufacture
amino acids, antibiotics, solvents, vitamins, chemicals and
materials.
• Medical applications:
43
Applications of Metabolic Engineering
• Industrial applications: use microorganisms to manufacture
amino acids, antibiotics, solvents, vitamins, chemicals and
materials.
Driving forces:
(1) Continuing increase in the production volume of carbohydrate
raw materials
(2) Continuing decline in the manufacturing cost of
biotechnologically produced products
(3) Technical advances in modern molecular biology
Practical goal of ME:
The design and creation of optimal biocatalysts (maximizing
the yield and productivity of desired products)
44
Applications of Metabolic Engineering
• Medical applications:
Identify specific targets for drug development
Design of gene therapy
Produce drugs or drug-leads
45
Examples of Metabolic Engineering
• Five types of applications
Enhancement of the yield and productivity of
products made by an organism
Expansion of the range of substances that can be
metabolized by an organism
Formation of new and novel products
General improvement of cellular properties
Xenobiotic degradation
46
Examples of Metabolic Engineering
Enhancement of the yield and productivity of
products made by an organism
Yield: impact the cost of raw materials (redirect
metabolic fluxes)
Productivity: key determinant of the capital cost of
bioprocessing equipment depends first and
foremost on the specific rate of substrate uptake
(amplify metabolic fluxes)
47
Examples of Metabolic Engineering
e.g. tryptophan production using E. coli
a.a.: food additives, feed supplements,
therapeutic agents, precursors for the
synthesis of peptides and agrochemicals
48
TyrR
Erythrose-4-P
AroF (Tyr)
+ AroG (Try) DHAP
PEP AroH (Phe)
Chorismate
Anthranilate
synthase Chorismate mutase
TrpR Prephanate
Anthranilate
Trp
P-hydroxyphenylpyruvate
Phenylpyruvate
Indole tna
Pyruvate Tryptophan Tyrosine
NH3 Phenylalanine
49
TyrR
Erythrose-4-P
AurF (Tyr)
+ AurG (Try) DAHP
PEP AurH (Phe)
TrpR
Chorismate
Anthranilate
Trp synthase Chorismate mutase
Prephanate
Anthranilate
P-hydroxyphenylpyruvate
Phenylpyruvate
Indole tna
Pyruvate Tryptophan Tyrosine
NH3 Phenylalanine
50
• Metabolic Engineering Strategies
(1) Delete aroG and aroH
(2) Mutate gene aroF (insentitive to feedback inhibition) – aroF394
(3) Inactivate the repress gene (TyrR)
(4) Remove branches leading to Tyr and Phe
(5) Inactivate tryptophanase (tna) to prevent possible Trp degradation
(6) Mutate anthranilate synthase (insensitive to Trp feedback inhibition)
(7) Mutate gene trpS (tryptophanyl tRNA synthase) (destruction of the
cell’s attenuation control)
(8) Inactivate the tryptophan repressor (TrpR)
Industrial E. coli strain:
6.2 g۰L-1 (in 5% glucose for 24 h)
51
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