1. Directed Evolution Charles Feng, Andrew Goodrich Team Presentation
BIOE 506 Cellular & Molecular Bioengineering

2. The Issue At Hand
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

3. 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?

4. Protein Design
Original ideas: forcing design on existing proteins, “top-down” approach
More recently: directed evolution
Buchholtz et al: improve function of site- specific FLP recombinase
Kumamaru et al: polychlorinated biphenyl- degrading enzymes with novel substrates
What’s so great about the above?

5. 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

6. Major Challenge
We’re not sure what affects performance and specificity!
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
Improving stability might adversely affect catalytic activity, etc.

7. The Solution
Directed evolution lets proteins reinvent themselves, thereby eliminating the need for mindless tinkering
Requirements:
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

8. 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

9. The Evolutionary Process
More difficult problem - how do we force something to change in the way we want?
Random mutagenesis - Arnold et al
Can create enzyme variants on scale of months/weeks/days by rounds of mutagenesis and screening
Family shuffling - Stemmer et al
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

10. 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

11. 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

12. 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

13. 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 higher antiviral activity

14. Homologous Recombination
Recombination works well for similar sequences
Another study: 26 subtilisin sequences with 56.4% sequence identity
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

15. RACHITT 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

16. Nonhomologous Recombination
Creation of fused enzyme libraries
ITCHY: library of chimeric E. coli and human GAR (glycinamide ribonucleotide) as model system
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”
Two genes truncated at restriction sites, then linearized and fragments cloned
Correct reading frame established by adding chloramphenicol resistance gene in frame

17. Applications to Enzymes
Enzyme stability and activity
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

18. Applications to Enzymes
Substrate specificity:
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

19. Applications to Enzymes
Enantioselectivity
Cofactor/activator requirements
Resistance to oxidizing conditions
Resistance to chemical modifications

20. Application to Binding Proteins
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

21. Creation of New Metabolic Pathways
Modification/combination of existing pathways by evolving metabolic genes
Can help with discovery of new, useful compounds
TIM barrel fold protein: important protein found in many enzyme families catalyzing different reactions
Transplant new catalytic activity on scaffold with existing binding site
Transplant new binding site on scaffold with existing catalytic activity

22. Creation of New Metabolic Pathways
New pathways for production of novel carotenoids
Combine carotenoid biosynthetic genes from different microorganisms

23. Conclusions
Directed evolution has potential for solving many bioenzymatic design problems:
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

24. Questions?