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Download Directed Evolution Charles Feng, Andrew Goodrich Team
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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 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 • • 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 • • 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 Pathways •Metabolic 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?