Directed Evolution Charles 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?