Directed EvolutionCharles Feng, Andrew Goodrich Team Presentation

BIOE 506 Cellular & Molecular Bioengineering

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

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•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?

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• 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.

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

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

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

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

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

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

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

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

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

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

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

Applications to Enzymes

•Enantioselectivity

•Cofactor/activator requirements

•Resistance to oxidizing conditions

•Resistance to chemical modifications

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

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

Creation of New Metabolic Pathways• New pathways for production of novel carotenoids

• Combine carotenoid biosynthetic genes from different microorganisms

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

Questions?