Transcript Directed Evolution Charles Feng, Andrew Goodrich Team

Directed Evolution
Charles Feng, Andrew Goodrich Team
Presentation
BIOE 506 Cellular & Molecular Bioengineering
The Issue At Hand
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Biotechnology requires specifically designed
catalytic processes
One option is biocatalytic processes using
enzymes, but there’s only so many available
Biocatalyst optimization has been a major topic,
but we have limited predictive power for the
relationship between structure and function for
proteins
So far, engineering of biocatalysts has been
difficult and time-consuming
The Magic of
Evolution
• All of nature’s complexity/beauty can be
attributed to the “blind watchmaker”
• Mutation and its impact on life as a
basis for natural selection
• Proteins as most basic element,
function affects compatibility with
environment
• Why can’t we do things the same way?
Protein Design
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Original ideas: forcing design on existing
proteins, “top-down” approach
More recently: directed evolution
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Buchholtz et al: improve function of sitespecific FLP recombinase
Kumamaru et al: polychlorinated biphenyldegrading enzymes with novel substrates
What’s so great about the above?
Differences between
Lab/Natural Evolution
• Lab evolution is a “guided” process
towards a final goal that may or may not
make biological sense
• Natural evolution is a gradual
accumulation of changes based on
environmental factors
Major Challenge
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We’re not sure what affects performance and specificity!
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Thermostability?
Activity?
Solubility?
Binding properties?
Structure?
Proteins too complex to manually change, as we don’t know
effects of one change on other functions/behaviors
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Improving stability might adversely affect catalytic
activity, etc.
The Solution
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Directed evolution lets proteins reinvent
themselves, thereby eliminating the need for
mindless tinkering
Requirements:
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Function must be physically feasible
Function must be biologically feasible
Must be able to make libraries of mutants via a
complex enough microorganism
Must have a rapid screen or selection to
evaluate the desired function
Screening for
Function
• Need to combine two things:
• In vitro transcription/translation
apparatus
• SIngle genes
• Tawfik and Griffiths: Combine in reverse
micelles, select by evaluating
modification of gene by its protein
product
• Many other ideas out there
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The Evolutionary
Process
More difficult problem - how do we force something to
change in the way we want?
Random mutagenesis - Arnold et al
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Can create enzyme variants on scale of
months/weeks/days by rounds of mutagenesis and
screening
Family shuffling - Stemmer et al
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Homologous recombination of evolutionarily related
genes
Library of “chimeric genes” created that should fold in
the same way as their precursors, but now there’s
variation present
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Mathematical
Standpoint
All possible changes/variations in amino
acid sequence creates a multidimensional
“performance landscape”
We’re trying to go from one (biologically,
naturally evolved) maximum to another that
may be a distance away
In order to get from one to the other, we
need to use evolutionary strategies that take
us along a stepwise variational path
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Random
Mutagenesis
Error-prone PCR: method of choice if
starting from single protein sequence
Mutation rate is 1/2 mutations per
protein so all variants can be
exhaustively evaluated - more
mutations would create combinatorial
challenges
Many created enzymes will be
non/dysfunctional, evaluated through
large screening libraries
Promising/improved variants
subsequently subjected to additional
rounds of mutagenesis
Results of
Mutagenesis
• Can successfully improve stability or
activity of an enzyme - many specific
solutions exist and mutations in iterative
rounds are very additive
• Drawback - genetic code is
conservative, many similar codons code
for same amino acid or another amino
acid w/ same properties
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Homologous
Recombination
Alternatively we can use
recombination to create
chimeras of many homologous
genes
Advantages: will result in mostly
functional variants b/c genes
have already been naturally
selected
Can possibly create new
functions
Most common method: “family
shuffling” - example is chimeric
protein made from 6 parent
sequences, now having 87-fold
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Homologous
Recombination
Recombination works well for similar sequences
Another study: 26 subtilisin sequences with 56.4% sequence
identity
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Wide range of enzymatic properties including those not found
in the parent
Much better performance than parental gene
Interesting point: sequence-wise, many times the best parent
is dissimilar to best chimera suggesting that sequence isn’t
everything
Limitation of method: demands high sequence identity (normally
70%), difficulty of some crossover events based on parent gene
sequence
RACHITT
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Developed by Coco et al to improve recombination
efficiency
Hybridize random DNA fragments to a single-stranded
DNA scaffold, then trim overlaps, fill gaps, ligate nicks
Subsequent digestion of resulting ds DNA strand can
create chimeric DNA fragments
Average 14 crossovers/gene variant versus 1-4 in
previous shuffling techniques
Allows for crossovers in dissimilar areas, i.e. those with
less than five consecutive matching bases
Technically more demanding
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Nonhomologous
Recombination
Creation of fused enzyme libraries
ITCHY: library of chimeric E. coli and human GAR (glycinamide
ribonucleotide) as model system
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Ligation of truncated fragments from each organism
Low frequency of functional chimeras
Fusion occurred near central region of proteins
SHIPREC: “sequence homology-independent protein
recombination”
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Two genes truncated at restriction sites, then linearized and
fragments cloned
Correct reading frame established by adding chloramphenicol
resistance gene in frame
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Applications to
Enzymes
Enzyme stability and activity
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Good targets for directed evolution
Additive mutations can lead to much improved
variants
Important for biocatalytic application
Must be stable under both evolution process
and application conditions
Wintrode et al: low-temperature activity and
high-temperature stability can be evolved
independently
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Applications to
Enzymes
Substrate specificity:
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Improving catalytic activity for new substrates
Example: in vitro evolution of an aspartate
aminotransferase with 1 million-fold increased
efficiency for catalysis of non-native substrate
valine
Best chimeras have modified active sites (i.e.
having contributions from both parents)
P450 monooxygenases: promising for
biotransformation applications - eight positions
identified defining length of substrate it can act on
Applications to
Enzymes
• Enantioselectivity
• Cofactor/activator requirements
• Resistance to oxidizing conditions
• Resistance to chemical modifications
Application to Binding
Proteins
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Improving binding affinity to specific
substrates, or binding capabilities to
additional substrates
Knappik et al: 40-fold higher antibody
affinity for bovine insulin
Stability of poorly folding anti-fluorescein
binding antibody improved by grafting
binding loops into better human antibody further improved with mutagenesis
Creation of New
Pathways
•Metabolic
Modification/combination
of existing pathways
by evolving metabolic genes
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Can help with discovery of new, useful
compounds
TIM barrel fold protein: important protein found
in many enzyme families catalyzing different
reactions
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Transplant new catalytic activity on scaffold
with existing binding site
Transplant new binding site on scaffold with
existing catalytic activity
Creation of New
Metabolic Pathways
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New pathways for production of novel carotenoids
Combine carotenoid biosynthetic genes from different
microorganisms
Conclusions
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Directed evolution has potential for solving
many bioenzymatic design problems:
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Improve enzyme substrate specificity,
stability, activity, etc
Improve protein binding affinity
Create novel metabolic pathways
In the future: applications to pathways,
viruses, even complete genomes
Questions?