DEVELOPMENT OF AN ENZYMATIC DEHALOGENATION PROCESS FOR THE

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DEVELOPMENT OF AN ENZYMATIC DEHALOGENATION PROCESS FOR THE Powered By Docstoc
					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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